3.1 AC completion

We first introduce a framework for reasoning by affirming the consequent (AC) in ASP. In GEDPs, a conditional sentence “ ${\varphi}\Rightarrow \psi$ " ( if ${\varphi}$ then $\psi$ ) is represented by the rule “ $\psi~\leftarrow~{\varphi}$ " where $\psi$ is a disjunction “ $L_1\,;\,\cdots\,;\, L_k\, ;\, not\,L_{k+1}\,;\cdots ;\,not\,L_l$ " and ${\varphi}$ is a conjunction “ $L_{l+1},\,\ldots,\,L_m,\,not\,L_{m+1},\,\ldots,\,not\,L_n$ ". To realize reasoning backward from $\psi$ to ${\varphi}$ , we extend a program $\Pi$ by introducing new rules.

Definition 3.1 (AC completion) Let $\Pi$ be a program and $r\in\Pi$ a rule of the form:

\begin{eqnarray*}&& L_1\,;\,\cdots\,;\, L_k\, ;\, not\,L_{k+1}\,;\cdots ;\,not\,L_l\\&& \quad\qquad \leftarrow\; L_{l+1},\,\ldots,\,L_m,\,not\,L_{m+1},\,\ldots,\,not\,L_n.\;\;\end{eqnarray*}

1. 1. For each disjunct in $head^+(r)$ and $head^-(r)$ , converse the implication:

   (5) \begin{eqnarray}&& L_{l+1},\,\ldots,\,L_m,\, not\,L_{m+1},\,\ldots,\,not\,L_n \;\leftarrow\; L_j \;\;\; (1\leq j\leq k), \end{eqnarray}

(6) \begin{eqnarray}&& L_{l+1},\,\ldots,\,L_m,\, not\,L_{m+1},\,\ldots,\,not\,L_n \;\leftarrow\; not\,L_j\;\;\; (k+1\leq j\leq l). \end{eqnarray}

In (5) and (6), the conjunction “ $L_{l+1},\,\ldots,\,L_m,\, not\,L_{m+1},\,\ldots,\,not\,L_n$ " appears on the left of “ $\leftarrow$ ". The produced (5) (resp. (6)) is considered an abbreviation of the collection of $(n-l)$ rules: $(L_{l+1}\leftarrow L_j),\ldots, (not\,L_n\leftarrow L_j)$ (resp. $(L_{l+1}\leftarrow not\,L_j),\ldots, (not\,L_n\leftarrow not\,L_j)$ )^{Footnote 3}, hence we abuse the term “rule” and call (5) or (6) a rule. In particular, (5) is not produced if $head^+(r)={\varnothing}$ or $body^{+}(r)=body^{-}(r)={\varnothing}$ , and (6) is not produced if $head^-(r)={\varnothing}$ or $body^{+}(r)=body^{-}(r)={\varnothing}$ . The set of all rules (5) and (6) is denoted as conv(r).

1. 2. Define

   \begin{eqnarray*}ac(\Pi)&\!=\!& \{\; \Sigma_1\,;\,\cdots\, ;\,\Sigma_p \;\leftarrow \; \ell_j \;\mid\\&& \quad \Sigma_i\leftarrow \ell_j \;\, (1\le i\le p)\;\;\mbox{is in}\;\; \bigcup_{r\in\Pi}\, conv(r)\, \}\end{eqnarray*}
   where each $\Sigma_i$ $(1\le i\le p)$ is a conjunction of literals and NAF-literals, and $\ell_j$ is either a literal $L_j$ $(1\le j\le k)$ or an NAF-literal $not\,L_j$ $(k+1\le j\le l)$ .

2. 3. The AC completion of $\Pi$ is defined as:

   $$ AC(\Pi) = \Pi \;\cup\; ac(\Pi). $$
   (5) and (6) in conv(r) represent converse implications from the disjunction in the head of r to the conjunction in the body of r. $ac(\Pi)$ collects rules “ $\Sigma_i\leftarrow \ell_j$ " $(1\le i\le p)$ having the same (NAF-)literal $\ell_j$ on the right of “ $\leftarrow$ ", and constructs “ $\Sigma_1\,;\,\cdots\, ;\,\Sigma_p \;\leftarrow \; \ell_j$ ," which we call an extended rule. Introducing $ac(\Pi)$ to $\Pi$ realizes reasoning by AC in $\Pi$ .

The set $ac(\Pi)$ contains an extended rule having a disjunction of conjunctions in its head, while it is transformed to rules of a GEDP. That is, the extended rule:

$$ (\ell^1_1,\ldots,\ell^1_{m_1})\, ;\, \cdots\, ; \, (\ell^p_1,\ldots,\ell^p_{m_p})\leftarrow \ell_j $$

is identified with the set of $(m_1\times\cdots\times m_p)$ rules of the form:

$$ \ell^1_{i_1}\, ;\, \cdots\, ; \, \ell^p_{i_p}\leftarrow \ell_j \;\;\;\;\; (1\le i_k\le m_k;\, 1\le k\le p).$$

By this fact, $AC(\Pi)$ is viewed as a GEDP and we do not distinguish extended rules and rules of a GEDP hereafter. The semantics of $AC(\Pi)$ is defined by its answer sets.

Example 3.1 Let $\Pi$ be the program:

\begin{eqnarray*}&& p\,;\, not\,q\leftarrow r,\, not\,s, \\&& p\leftarrow q.\end{eqnarray*}

Then, $ac(\Pi)$ becomes

\begin{eqnarray*}&& (r,\, not\,s)\,;\, q\leftarrow p,\\&& r,\, not\,s\leftarrow not\,q\end{eqnarray*}

where the first rule “ $(r,\, not\,s)\,;\, q\leftarrow p$ " is identified with

\begin{eqnarray*}&& r\,;\, q\leftarrow p,\\&& not\,s\,;\, q\leftarrow p,\end{eqnarray*}

and the second rule “ $r,\, not\,s\leftarrow not\,q$ " is identified with

\begin{eqnarray*}&& r\leftarrow not\,q,\\&& not\,s\leftarrow not\,q.\end{eqnarray*}

Then, $AC(\Pi)\cup \{p\leftarrow\}$ has two answer sets $\{p,q\}$ and $\{p,r\}$ .

By definition, if there is more than one rule in $\Pi$ having the same (NAF-)literal in the heads, they are collected to produce a single converse rule in $ac(\Pi)$ . For instance, $\Pi=\{\, p\leftarrow~q,\;\; p\leftarrow~r\,\}$ produces $ac(\Pi)=\{\, q\,;\,r\leftarrow p\,\}$ but not $\Lambda=\{\, q\leftarrow p,\;\; r\leftarrow p\,\}$ . Then, $AC(\Pi)\cup\{ p\leftarrow\}$ has two answer sets $\{p,q\}$ and $\{p,r\}$ . Suppose that the new fact “ $\neg\, q\leftarrow$ " is added to $\Pi$ . Put $\Pi'=\Pi\cup\{\,\neg\, q\leftarrow\,\}$ . Then, $AC(\Pi')\cup\{ p\leftarrow\}$ has the answer set $\{p,r\}$ , which represents the result of AC reasoning in $\Pi'$ . If $\Lambda$ is used instead of $ac(\Pi)$ , however, $\Pi'\cup\Lambda\cup\{ p\leftarrow\}$ has the answer set Lit. The result is too strong because r is consistently inferred from $\Pi'\cup\{ p\leftarrow\}$ by AC reasoning. As a concrete example, put $p=wet\mbox{-}grass$ , $q=rain$ , and $r=sprinkler\mbox{-}on$ . Then, $AC(\Pi')\cup\{wet\mbox{-}grass\leftarrow\,\}$ has the answer set $\{\,wet\mbox{-}grass, \neg\, rain, sprinkler\mbox{-}on\,\}$ , while $\Pi'\cup\Lambda\cup\{wet\mbox{-}grass\leftarrow\,\}$ has the answer set Lit. AC completion derives an antecedent from a consequent, but it does not derive negation of antecedent by its nature. For instance, given $\Pi=\{\, p\,;\,q\leftarrow r,\;\;\; p\leftarrow\,\}$ , $AC(\Pi)\models_s r$ but $AC(\Pi)\not\models_c \neg\,q$ .

Note that in Definition 3.1, the converse of constraints and facts are not produced. When $head^+(r)=head^-(r)={\varnothing}$ , r is considered a rule with $\mathit{false}$ in the head, then (5) and (6) become

$$ L_{l+1},\,\ldots,\,L_m,\, not\,L_{m+1},\,\ldots,\,not\,L_n \;\leftarrow \mathit{false}$$

which has no effect as a rule. On the other hand, when $body^{+}(r)=body^{-}(r)={\varnothing}$ , r is considered a rule with $\mathit{true}$ in the body, then (5) and (6) respectively become

$$ true\leftarrow L_j\; (1\leq j\leq k)\quad\mbox{and}\quad true\leftarrow not\,L_j\; (k+1\leq j\leq l).$$

We do not include this type of rules for constructing $ac(\Pi)$ because it would disable AC reasoning. For instance, transform $\Pi=\{\, p\leftarrow q,\;\;\; p\leftarrow\,\}$ to $\Pi'=\Pi\cup\{ q\,; true\leftarrow p\}$ . Then, $\{p\}$ is the minimal set satisfying $\Pi'$ , and q is not included in the answer set of $\Pi'$ . With this reason, constraints and facts are not completed at the first step of Definition 3.1.

The result of AC completion is syntax-dependent in general. That is, two (strongly) equivalent programs may produce different AC completions.

In the above example, “ $not\,p\leftarrow q$ ” is a conditional sentence which is subject to AC inference, while “ $\leftarrow p,q$ ” is a constraint which is not subject to AC inference by definition. For instance, given the conditional sentence “if it is sunny, the grass is not wet" and the fact “the grass is not wet.," people would infer “it is sunny" by AC inference. On the other hand, given the constraint “it does not happen that wet-grass and sunny-weather at the same time" and the fact “the grass is not wet," the number of people who infer “it is sunny" by AC would be smaller because the cause-effect relation between “sunny" and “not wet" is not explicitly expressed in the constraint.

Reasoning by AC is nonmonotonic in the sense that $\Pi\models_c L$ (or $\Pi\models_s L$ ) does not imply $AC(\Pi)\models_c L$ (or $AC(\Pi)\models_s L$ ) in general.

3.2 DC completion

We next introduce a framework for reasoning by denying the consequent (DC) in ASP. There are two ways for negating a literal – one is using explicit negation and the other is using default negation. Accordingly, there are two ways of completing a program for the purpose of reasoning by DC.

Definition 3.2 (DC completion) Let $\Pi$ be a program. For each rule $r\in\Pi$ of the form:

\begin{eqnarray*}&& L_1\,;\,\cdots\,;\, L_k\, ;\, not\,L_{k+1}\,;\cdots ;\,not\,L_l\\&& \qquad \leftarrow\; L_{l+1},\,\ldots,\,L_m,\,not\,L_{m+1},\,\ldots,\,not\,L_n\;\;\end{eqnarray*}

define wdc(r) as the rule:

(7) \begin{equation}not\,L_{l+1};\cdots ; not\,L_m \,;\, L_{m+1}; \cdots; L_n\leftarrow not\, L_1,\ldots, not\,L_k,\, L_{k+1},\ldots, L_l\end{equation}

and define sdc(r) as the rule:

(8) \begin{equation}\neg\,L_{l+1};\cdots ; \neg\,L_m \,;\, L_{m+1}; \cdots; L_n\leftarrow \neg\, L_1,\ldots, \neg\,L_k,\, L_{k+1},\ldots, L_l.\end{equation}

In particular, (7) or (8) becomes a fact if $head^+(r)=head^-(r)={\varnothing}$ , and it becomes a constraint if $body^{+}(r)=body^{-}(r)={\varnothing}$ . The weak DC completion and the strong DC completion of $\Pi$ are respectively defined as:

\begin{eqnarray*}WDC(\Pi) &=& \Pi \;\cup\; \{wdc(r) \,\mid\, r\in \Pi \,\}, \\SDC(\Pi) &=& \Pi \;\cup\; \{sdc(r) \,\mid\, r\in \Pi \,\}.\end{eqnarray*}

By definition, $WDC(\Pi)$ and $SDC(\Pi)$ introduce contrapositive rules in two different ways. In (7), literals $L_i$ $(1\leq i\leq k;\,l+1\leq i\leq m)$ are negated using default negation not and NAF-literals $not\,L_j$ $(k+1\leq i\leq l;\,m+1\leq i\leq n)$ are converted to $L_j$ . In (8), on the other hand, literals $L_i$ $(1\leq i\leq k;\,l+1\leq i\leq m)$ are negated using explicit negation $\neg$ and NAF-literals $not\,L_j$ $(k+1\leq i\leq l;\,m+1\leq i\leq n)$ are converted to $L_j$ . $WDC(\Pi)$ and $SDC(\Pi)$ are GEDPs, and their semantics are defined by their answer sets. In particular, $SDC(\Pi)$ becomes an EDP if $\Pi$ is an EDP.

Note that contraposition of facts or constraints is produced in WDC/SDC. For instance, the fact “ $p\leftarrow$ " produces the constraint “ $\leftarrow not\,p$ " by WDC and “ $\leftarrow \neg p$ " by SDC. The fact “ $not\,p\leftarrow$ " produces the constraint “ $\leftarrow p$ " by WDC and SDC. On the other side, the constraint “ $\leftarrow p$ " produces the fact “ $not\,p\leftarrow$ " by WDC and “ $\neg p\leftarrow$ " by SDC. The constraint “ $\leftarrow not\,p$ " produces the fact “ $p\leftarrow$ " by WDC and SDC.

The WDC and SDC produce different results in general.

Example 3.5 Given $\Pi=\{\,p\leftarrow not\, q\,\}$ , it becomes

\begin{eqnarray*} WDC(\Pi)&=&\{\, p\leftarrow not\,q, \quad q\leftarrow not\,p\,\},\\ SDC(\Pi)&=&\{\, p \leftarrow not\,q, \quad q\leftarrow \neg\,p\,\}.\end{eqnarray*}

Then, $WDC(\Pi)$ has two answer sets $\{p\}$ and $\{q\}$ , while $SDC(\Pi)$ has the single answer set $\{ p\}$ .

Example 3.5 shows that WDC is nonmonotonic as $\Pi\models_s p$ but $WDC(\Pi)\not\models_s p$ . SDC is also nonmonotonic (see Example 3.8). The result of DC completion is syntax-dependent in general.

Example 3.8 The program $\Pi = \{\, p\leftarrow q,\;\;\; p\leftarrow \neg q,\;\;\; \neg\,p\leftarrow\, \}$ has the answer set $\{\neg\,p\}$ , but

$$ SDC(\Pi)=\Pi \cup \{\,\neg\,q\leftarrow \neg\,p,\;\;\; q\leftarrow \neg\,p,\;\;\; \leftarrow p\,\} $$

is incoherent.

Note that in GEDPs, contraposition of a rule does not hold in general. Thus, the program $\Pi=\{\, p\leftarrow q,\;\; \neg\,p\leftarrow \,\}$ does not deduce $\neg\,q$ . SDC completes the program as $SDC(\Pi)=\Pi\cup \{\, \neg\,q\leftarrow \neg\,p,\;\; \leftarrow p\,\}$ and makes $\neg\,q$ deducible. In this sense, SDC has the effect of making explicit negation closer to classical negation in GEDP.

3.3 DA completion

As a third extension, we introduce a framework for reasoning by denying the antecedent (DA) in ASP. As in the case of DC completion, two different ways of completion are considered depending on the choice of negation.

Definition 3.3 (weak DA completion) Let $\Pi$ be a program and $r\in\Pi$ a rule of the form:

\begin{eqnarray*}&& L_1\,;\,\cdots\,;\, L_k\, ;\, not\,L_{k+1}\,;\cdots ;\,not\,L_l\\&& \quad\qquad\leftarrow\; L_{l+1},\,\ldots,\,L_m,\,not\,L_{m+1},\,\ldots,\,not\,L_n.\end{eqnarray*}

1. 1. For each disjunct in $head^+(r)$ and $head^-(r)$ , inverse the implication:

   (9) \begin{eqnarray}not\,L_i &\leftarrow& not\,L_{l+1}\,;\,\cdots\, ;\, not\,L_m\,;\, L_{m+1}\,;\cdots\, ;\, L_n\;\; (1\leq i\leq k), \end{eqnarray}

(10) \begin{eqnarray}L_i &\leftarrow& not\,L_{l+1}\,;\,\cdots\, ;\, not\,L_m\,;\, L_{m+1}\,;\,\cdots\, ;\, L_n\;\; (k+1\leq i\leq l). \;\;\;\;\;\end{eqnarray}

In (9) and (10), the disjunction “ $not\,L_{l+1}\,;\,\cdots\, ;\, not\,L_m\,;\, L_{m+1}\,;\,\cdots\, ;\, L_n$ " appears on the right of “ $\leftarrow$ ". The produced (9) (resp. (10)) is considered an abbreviation of the collection of $(n-l)$ rules: $(not\,L_i \leftarrow not\,L_{l+1}),\ldots, (not\,L_i \leftarrow L_n)$ (resp. $(L_i \leftarrow not\,L_{l+1}),\ldots, (L_i \leftarrow L_n)$ ), hence we abuse the term “rule” and call (9) or (10) a rule. In particular, (9) is not produced if $head^+(r)={\varnothing}$ or $body^{+}(r)=body^{-}(r)={\varnothing}$ , and (10) is not produced if $head^-(r)={\varnothing}$ or $body^{+}(r)=body^{-}(r)={\varnothing}$ . The set of rules (9)–(10) is denoted as winv(r).

1. 2. Define

   \begin{eqnarray*}wda(\Pi) &\!=\!& \{\, \ell_i \leftarrow \Gamma_1,\ldots, \Gamma_p \,\mid \\&& \quad \ell_i\leftarrow \Gamma_j\; (1\leq j\leq p)\;\;\mbox{is in}\;\; \bigcup_{r\in\Pi}\; winv(r)\,\}\end{eqnarray*}
   where $\ell_i$ is either a literal $L_i$ $(k+1\le i\le l)$ or an NAF-literal $not\,L_i$ $(1\le i\le k)$ , and each $\Gamma_j$ $(1\le j\le p)$ is a disjunction of literals and NAF-literals.

2. 3. The weak DA completion of $\Pi$ is defined as:

   $$ WDA(\Pi)=\Pi \;\cup\; wda(\Pi).$$

(9) and (10) in winv(r) represent inverse implication from the (default) negation of the conjunction in the body of r to the (default) negation of the disjunction in the head of r. $wda(\Pi)$ collects rules “ $\ell_i\leftarrow \Gamma_j$ " $(1\leq j\leq p)$ having the same (NAF-)literal $\ell_i$ on the left of “ $\leftarrow,$ " and constructs “ $\ell_i \leftarrow \Gamma_1,\ldots, \Gamma_p$ ," which we call an extended rule. Introducing $wda(\Pi)$ to $\Pi$ realizes reasoning by weak DA. An extended rule has a conjunction of disjunctions in its body, while it is transformed to rules of a GEDP as the case of AC completion. That is, the extended rule:

$$ \ell_i \leftarrow (\ell^1_1\,;\,\cdots\,;\,\ell^1_{m_1})\, , \ldots,\, (\ell^p_1\,;\,\cdots\,;\,\ell^p_{m_p})$$

is identified with the set of $(m_1\times\cdots\times m_p)$ rules of the form:

$$ \ell_i \leftarrow \ell^1_{j_1} ,\, \ldots,\, \ell^p_{j_p} \;\;\;\;\; (1\le j_k\le m_k;\, 1\le k\le p).$$

By this fact, $WDA(\Pi)$ is viewed as a GEDP and we do not distinguish extended rules and rules of a GEDP hereafter. The semantics of $WDA(\Pi)$ is defined by its answer sets.

Example 3.9 Let $\Pi$ be the program:

\begin{eqnarray*}&& p\,;\,q\leftarrow r,\, not\,s,\\&& q\,;\,not\,r\leftarrow t,\\&& s\leftarrow.\end{eqnarray*}

Then, $wda(\Pi)$ becomes

\begin{eqnarray*}&& not\,p\leftarrow not\,r\, ;\, s,\\&& not\,q\leftarrow (not\,r\,;\, s),\, not\,t,\\&& r\leftarrow not\,t\end{eqnarray*}

where the first rule “ $not\,p\leftarrow not\,r\, ;\, s$ " is identified with

\begin{eqnarray*}&& not\,p\leftarrow not\,r,\\&& not\,p\leftarrow s,\end{eqnarray*}

and the second rule “ $not\,q\leftarrow (not\,r\,;\, s),\, not\,t$ " is identified with

\begin{eqnarray*}&& not\,q\leftarrow not\,r,\,not\,t,\\&& not\,q\leftarrow s,\, not\,t.\end{eqnarray*}

Then, $WDA(\Pi)$ has the answer set $\{ s, r\}$ .

As in the case of AC completion, if there is more than one rule having the same (NAF-)literal in the heads, they are collected to produce a single inverse rule. For instance, $\Pi=\{\,p\leftarrow q,\;\; p\leftarrow~r\,\}$ produces $wda(\Pi)=\{\, not\,p\leftarrow not\,q,\,not\,r\,\}$ but not $\Lambda=\{\, not\,p\leftarrow not\,q,\;\; not\,p\leftarrow not\,r\,\}$ . Suppose that the new fact “ $r\leftarrow$ ” is added to $\Pi$ . Put $\Pi'=\Pi\cup\{ r\leftarrow\}$ . Then, $WDA(\Pi')$ has the answer set $\{p,r\}$ . If $\Lambda$ is used instead of $wda(\Pi)$ , however, $\Pi'\cup\Lambda$ is incoherent because the first rule of $\Lambda$ is not satisfied. The result is too strong because p is deduced by “ $p\leftarrow r$ " and “ $r\leftarrow,$ " and it has no direct connection to DA inference in the first rule of $\Lambda$ . Hence, we conclude $not\,p$ if both q and r are negated in $wda(\Pi)$ .

The strong DA completion is defined in a similar manner.

Definition 3.4 (strong DA completion) Let $\Pi$ be a program and $r\in\Pi$ a rule of the form:

\begin{eqnarray*}&& L_1\,;\,\cdots\,;\, L_k\, ;\, not\,L_{k+1}\,;\cdots ;\,not\,L_l\\&& \qquad\leftarrow\; L_{l+1},\,\ldots,\,L_m,\,not\,L_{m+1},\,\ldots,\,not\,L_n.\end{eqnarray*}

1. 1. For each disjunct in $head^+(r)$ and $head^-(r)$ , inverse the implication:

   (11) \begin{eqnarray}\neg\,L_i &\leftarrow& \neg\,L_{l+1}\,;\,\cdots\, ;\, \neg\,L_m\,;\, L_{m+1}\,;\cdots\, ;\, L_n\;\; (1\leq i\leq k), \end{eqnarray}

(12) \begin{eqnarray}L_i &\leftarrow& \neg\,L_{l+1}\,;\,\cdots\, ;\, \neg\,L_m\,;\, L_{m+1}\,;\,\cdots\, ;\, L_n\;\; (k+1\leq i\leq l). \;\;\;\;\;\end{eqnarray}

As in the case of WDA, the produced (11) (resp. (12)) is considered an abbreviation of the collection of $(n-l)$ rules: $(\neg\,L_i \leftarrow \neg\,L_{l+1}),\ldots, (\neg\,L_i \leftarrow L_n)$ (resp. $(L_i \leftarrow \neg\,L_{l+1}),\ldots, (L_i \leftarrow L_n)$ ), hence we call (11) or (12) a rule. In particular, (11) is not produced if $head^+(r)={\varnothing}$ or $body^{+}(r)=body^{-}(r)={\varnothing}$ , and (12) is not produced if $head^-(r)={\varnothing}$ or $body^{+}(r)=body^{-}(r)={\varnothing}$ . The set of rules (11)–(12) is denoted as sinv(r).

1. 2. Define

   \begin{eqnarray*}sda(\Pi) &\!=\!& \{\, \ell_i \leftarrow \Gamma_1,\ldots, \Gamma_p \,\mid \\&& \quad \ell_i\leftarrow \Gamma_j\; (1\leq j\leq p)\;\;\mbox{is in}\;\; \bigcup_{r\in\Pi}\; sinv(r)\,\}\end{eqnarray*}
   where $\ell_i$ is either a literal $L_i$ $(k+1\le i\le l)$ or $\neg\,L_i$ $(1\le i\le k)$ , and each $\Gamma_j$ $(1\le j\le p)$ is a disjunction of positive/negative literals.

2. 3. The strong DA completion of $\Pi$ is defined as:

   $$ SDA(\Pi)=\Pi \;\cup\; sda(\Pi).$$

As in the case of WDA, extended rules in $sda(\Pi)$ are transformed to rules of a GEDP. Then, $SDA(\Pi)$ is viewed as a GEDP and its semantics is defined by its answer sets. In particular, $SDA(\Pi)$ becomes an EDP if $\Pi$ is an EDP.

The result of DA completion is syntax-dependent in general.

4.1 Default AC completion

The AC completion is modified for default AC reasoning.

Definition 4.1 (default AC completion) Let $\Pi$ be a program. For each rule $r\in\Pi$ of the form:

\begin{eqnarray*}&& L_1\,;\,\cdots\,;\, L_k\, ;\, not\,L_{k+1}\,;\cdots ;\,not\,L_l\\&& \qquad \leftarrow\; L_{l+1},\,\ldots,\,L_m,\,not\,L_{m+1},\,\ldots,\,not\,L_n,\end{eqnarray*}

define dac(r) as the set of rules:

(13) \begin{eqnarray}&& L_{l+1},\,\ldots,\,L_m,\, not\,L_{m+1},\,\ldots,\,not\,L_n \leftarrow L_i,\,\Delta\;\;\;\;\;\quad (1\leq i\leq k), \end{eqnarray}

(14) \begin{eqnarray}&& L_{l+1},\,\ldots,\,L_m,\, not\,L_{m+1},\,\ldots,\,not\,L_n \leftarrow not\,L_i,\,\Delta\;\;\;\; (k+1\leq i\leq l)\end{eqnarray}

where $\Delta=not\,\neg\,L_{l+1},\,\ldots,\,not\,\neg\, L_m,\, not\,L_{m+1},\,\ldots,\,not\,L_n$ . As before, (13) is not produced if $head^+(r)={\varnothing}$ or $body^+(r)=body^-(r)={\varnothing}$ ; and (14) is not produced if $head^-(r)={\varnothing}$ or $body^+(r)=body^-(r)={\varnothing}$ . The default AC completion of $\Pi$ is defined as:

$$ DAC(\Pi) = \Pi \;\cup\; dac(\Pi)$$

in which

\begin{eqnarray*}dac(\Pi)&\!=\!& \{\; \Sigma_1\,;\,\cdots\, ;\,\Sigma_p \;\leftarrow \; \ell_j,\,\Delta_i \;\mid\\&& \quad \Sigma_i\leftarrow \ell_j,\,\Delta_i \;\, (1\le i\le p)\;\;\mbox{is in}\;\; \bigcup_{r\in\Pi}\, dac(r)\, \}\end{eqnarray*}

where each $\Sigma_i$ $(1\le i\le p)$ is a conjunction of literals and NAF-literals, and $\ell_j$ is either a literal $L_j$ $(1\le j\le k)$ or an NAF-literal $not\,L_j$ $(k+1\le j\le l)$ .

Like $AC(\Pi)$ , rules in $dac(\Pi)$ are converted into the form of a GEDP, then $DAC(\Pi)$ is viewed as a GEDP. Compared with the AC completion, the DAC completion introduces the conjunction $\Delta$ of NAF-literals to the body of each rule. Then, the rules “ $\Sigma_1\,;\,\cdots\, ;\,\Sigma_p \,\leftarrow \, \ell_j,\,\Delta_i$ " having the same head with different bodies are constructed for $i=1,\ldots,p$ .

Example 4.1 Let $\Pi=\{\,p\leftarrow q,\;\; p\leftarrow r,\;\; p\leftarrow,\;\; \neg r\leftarrow\,\}$ . Then, $DAC(\Pi)=\Pi\cup dac(\Pi)$ where

$$ dac(\Pi)=\{\, q\,; r\leftarrow p,not\,\neg q,\;\;\; q\,; r\leftarrow p,not\,\neg r\,\}. $$

As a result, $DAC(\Pi)$ has the answer set $\{p,q,\neg r\}$ .

We say that a set S of ground literals satisfies the conjunction “ $L_1,\ldots,L_k$ " of ground literals if $\{L_1,\ldots,L_k\}\subseteq S$ ; and S satisfies the conjunction “ $not\,L_1,\ldots,not\,L_k$ " of ground NAF-literals if $\{L_1,\ldots,L_k\}\cap S={\varnothing}$ .

When $AC(\Pi)$ has a consistent answer set, $DAC(\Pi)$ does not change it.

4.2 Default DA completion

The DA completion is modified for default DA reasoning.

Definition 4.2 (default DA completion) Let $\Pi$ be a program. Define

\begin{eqnarray*}wdda(\Pi) &\!=\!& \{\, \ell_i \leftarrow \Gamma_1,\ldots, \Gamma_p,\,\delta^w_i \,\mid \\&& \quad \ell_i\leftarrow \Gamma_j\; (1\leq j\leq p)\;\;\mbox{is in}\;\; \bigcup_{r\in\Pi}\; winv(r)\,\},\\sdda(\Pi) &\!=\!& \{\, \ell_i \leftarrow \Gamma_1,\ldots, \Gamma_p,\,\delta^s_i \,\mid \\&& \quad \ell_i\leftarrow \Gamma_j\; (1\leq j\leq p)\;\;\mbox{is in}\;\; \bigcup_{r\in\Pi}\; sinv(r)\,\}\end{eqnarray*}

where $\ell_i$ , $\Gamma_j$ , winv(r), and sinv(r) are the same as those in Definitions 3.3 and 3.4. In addition, $\delta^w_i=not\,\neg\,L_i$ if $\ell_i=L_i$ , and $\delta^w_i=not\,L_i$ if $\ell_i=not\,L_i$ ; $\delta^s_i=not\,\neg\,L_i$ if $\ell_i=L_i$ , and $\delta^s_i=not\,L_i$ if $\ell_i=\neg\,L_i$ . The weak default DA completion and the strong default DA completion of $\Pi$ are respectively defined as:

\begin{eqnarray*}WDDA(\Pi) &=& \Pi \;\cup\; wdda(\Pi),\\SDDA(\Pi) &=& \Pi \;\cup\; sdda(\Pi).\end{eqnarray*}

Rules in $wdda(\Pi)$ and $sdda(\Pi)$ are converted into the form of a GEDP, so $WDDA(\Pi)$ and $SDDA(\Pi)$ are viewed as GEDPs. Like the DAC completion, both WDDA and SDDA introduce an additional NAF-literal to each rule.

When $WDA(\Pi)$ (resp. $SDA(\Pi)$ ) has a consistent answer set, $WDDA(\Pi)$ (resp. $SDDA(\Pi)$ ) does not change it.

5.1 Suppression task

Byrne (Reference Byrne1989) provides an empirical study which shows that human conditional reasoning can be nonmonotonic in the so-called suppression tasks. She verifies the effects in different types of conditional reasoning by experimental testing on college students. Students are divided into three groups: the first group receives simple conditional arguments; the second group receives conditional arguments accompanied by another conditional sentence with an alternative antecedent; and the third group receives conditional arguments accompanied by another conditional sentence with an additional antecedent. More precisely, suppose the following three conditional sentences:

$S_1$ : If she has an essay to write then she will study late in the library.

$S_2$ : If she has some textbooks to read then she will study late in the library.

$S_3$ : If the library stays open then she will study late in the library.

Given the conditional sentence $S_1$ , $S_2$ represents a sentence with an alternative antecedent, while $S_3$ represents a sentence with an additional antecedent. The antecedent of $S_2$ is considered an alternative sufficient condition for studying late in the library. By contrast, the antecedent of $S_3$ is considered an additional sufficient condition for studying late in the library. Table 1 presents the percentages of inferences made by subjects from the three kinds of arguments.

Table 1. The percentages of inferences in experiments (Byrne Reference Byrne1989)

By the table, given the sentence $S_1$ and the fact: “she will study late in the library," the 71% of the first group concludes: “she has an essay to write" by AC. When $S_1$ is accompanied by a conditional $S_2$ containing an alternative antecedent, on the other hand, the percentage of subjects who perform AC inference reduces to 13%. The reason is that people know that the alternative: “She has some textbooks to read,” could be the case instead. Similar reduction is observed for DA. Byrne argues that those fallacious inferences are suppressed when a conditional is accompanied by an alternative antecedent. She also observes that the inference patterns change when $S_1$ is accompanied by a conditional $S_3$ containing an additional antecedent. In Table 1, the number of subjects who conclude: “She will study late in the library" by AA reduces to 38%, and the number of subjects who conclude: “She does not have an essay to write" by DC reduces to 33%. By contrast, the suppression of AC and DA are relaxed, 54% of subjects make AC and 63% of subjects make DA. Byrne then argues that “valid inferences are suppressed in the same way as fallacious inferences."

The suppression task is characterized in our framework as follows. First, the sentence $S_1$ is represented as the rule: “ $library \leftarrow essay$ ." Then, four conditional inferences (AA, DC, AC, DA) in simple arguments are respectively represented by the following programs:

\begin{eqnarray*}\mbox{(AA)} && \Pi_0=\{\, library \leftarrow essay,\;\;\; essay\leftarrow\,\}, \\\mbox{(DC)} && \Pi_1=\{\, library \leftarrow essay,\;\;\; \neg\, library\leftarrow\,\}, \\\mbox{(AC)} && \Pi_2=\{\, library \leftarrow essay,\;\;\; library\leftarrow\,\}, \\\mbox{(DA)} && \Pi_3=\{\, library \leftarrow essay,\;\;\; \neg\,essay\leftarrow\,\}.\end{eqnarray*}

Then, $\Pi_0$ has the answer set $\{\,library, essay\,\}$ in which AA inference is done. By contrast, DC, AC, and DA inferences are not performed in $\Pi_1$ , $\Pi_2$ , and $\Pi_3$ , respectively. To realize those inferences, consider completions such that

\begin{eqnarray*}SDC(\Pi_1)&=&\Pi_1\cup \{\, \neg\,essay \leftarrow \neg\,library\,\}, \\AC(\Pi_2)&=&\Pi_2\cup\{\, essay\leftarrow library \,\}, \\SDA(\Pi_3)&=&\Pi_3\cup\{\, \neg\,library \leftarrow \neg\,essay\,\}\end{eqnarray*}

where $SDC(\Pi_1)$ has the answer set $\{\,\neg\,library, \neg\,essay\,\}$ , $AC(\Pi_2)$ has the answer set $\{\,library, essay\,\}$ , and $SDA(\Pi_3)$ has the answer set $\{\,\neg\,library, \neg\,essay\,\}$ . As a result, DC, AC, and DA inferences are performed in $SDC(\Pi_1)$ , $AC(\Pi_2)$ , and $SDA(\Pi_3)$ , respectively.

Next, consider the alternative arguments $S_1$ and $S_2$ . They are represented by the programs:

$$\Pi_k^{\rm ALT}=\Pi_k \cup \{\, library\leftarrow text\,\}\;\;\; (k=0,1,2,3). $$

The program $\Pi_0^{\rm ALT}$ has the answer set $\{\,library, essay\,\}$ in which the result of AA inference does not change from $\Pi_0$ . Programs $\Pi_1^{\rm ALT}$ , $\Pi_2^{\rm ALT}$ , and $\Pi_3^{\rm ALT}$ are completed as follows:

\begin{eqnarray*}SDC(\Pi_1^{\rm ALT})&=&\Pi_1^{\rm ALT}\cup \{\, \neg\,essay \leftarrow \neg\,library,\;\;\; \neg\,text \leftarrow \neg\,library\,\}, \\AC(\Pi_2^{\rm ALT})&=&\Pi_2^{\rm ALT}\cup\{\, essay\,;\, text\leftarrow library \,\}, \\SDA(\Pi_3^{\rm ALT})&=&\Pi_3^{\rm ALT}\cup\{\, \neg\,library \leftarrow \neg\,essay,\,\neg\,text\,\}\end{eqnarray*}

where $SDC(\Pi_1^{\rm ALT})$ has the answer set $\{\,\neg\,library, \neg\,essay, \neg\,text\,\}$ , $AC(\Pi_2^{\rm ALT})$ has the two answer sets $\{\,library, essay\,\}$ and $\{\,library, text\,\}$ , and $SDA(\Pi_3^{\rm ALT})$ has the answer set $\{\,\neg\,essay\,\}$ . As a result, $SDC(\Pi_1^{\rm ALT})\models_s \neg\,essay$ , $AC(\Pi_2^{\rm ALT})\not\models_s essay$ , and $SDA(\Pi_3^{\rm ALT})\not\models_s \neg\,library$ , which indicate that AC and DA inferences are suppressed while DC is not suppressed. In this way, the completion successfully represents the effect of suppression of AC/DA inference in alternative arguments.

$\circ$ means that inference succeeds; $\times$ means that inference is suppressed.

In additional arguments, on the other hand, Byrne (Reference Byrne1989)observes that AA/DC inference is also suppressed. Our completion method does not characterize the suppression of AA inference because we enable fallacious inferences by AC/DA completion while still keep the valid AA inference. Byrne (Reference Byrne1989) says “ people may consider that certain other conditions are necessary for this conclusion to hold, for example, the library must remain open. Thus, conditionals are frequently elliptical in that information that can be taken for granted is omitted from them." In the above example, the availability of the library is necessary for studying in it but it is just omitted in the initial premises. Then, it is considered that the rule: “ $library\leftarrow essay$ " in mind is overwritten by the rule: “ $library\leftarrow essay,\, open$ " when the additional antecedent is given. Let

\begin{eqnarray*}&& \Pi_0^{\rm ADD}=\{\, library\leftarrow essay,\,open,\;\;\; essay\leftarrow\,\}, \\&& \Pi_1^{\rm ADD}=\{\, library\leftarrow essay,\,open,\;\;\; \neg\, library\leftarrow\,\}, \\&& \Pi_2^{\rm ADD}=\{\, library\leftarrow essay,\,open,\;\;\; library\leftarrow\,\}, \\&& \Pi_3^{\rm ADD}=\{\, library\leftarrow essay,\,open,\;\;\; \neg\,essay\leftarrow\,\}.\end{eqnarray*}

The program $\Pi_0^{\rm ADD}$ has the answer set $\{\,essay\,\}$ , thereby $\Pi_0^{\rm ADD}\not\models_s library$ . Then, the result of AA inference is suppressed. Programs $\Pi_1^{\rm ADD}$ , $\Pi_2^{\rm ADD}$ , $\Pi_3^{\rm ADD}$ are completed as follows:

\begin{eqnarray*}SDC(\Pi_1^{\rm ADD})&=&\Pi_1^{\rm ADD}\cup \{\, \neg\,essay\,;\, \neg\,open \leftarrow \neg\,library\,\}, \\AC(\Pi_2^{\rm ADD})&=&\Pi_2^{\rm ADD}\cup\{\, essay\leftarrow library,\;\;\; open\leftarrow library \,\}, \\SDA(\Pi_3^{\rm ADD})&=&\Pi_3^{\rm ADD}\cup\;\{\, \neg\,library \leftarrow \neg\,essay,\;\;\; \neg\,library \leftarrow \neg\,open\,\}\end{eqnarray*}

where $SDC(\Pi_1^{\rm ADD})$ has the two answer sets $\{\,\neg\,library, \neg\,essay\,\}$ and $\{\,\neg\,library, \neg\,open\,\}$ , $AC(\Pi_2^{\rm ADD})$ has the answer set $\{\,library, essay, open\,\}$ , and $SDA(\Pi_3^{\rm ADD})$ has the the answer set $\{\,\neg\,essay, \neg\,library\,\}$ . As a result, $SDC(\Pi_1^{\rm ADD})\not\models_s \neg\,essay$ , $AC(\Pi_2^{\rm ADD})\models_s essay$ , and $SDA(\Pi_3^{\rm ADD})\models_s \neg\,library$ . This indicates that DC is suppressed but AC and DA are not suppressed, which explains the results of Byrne (Reference Byrne1989).

The results of inferences using completion are summarized in Table 2. By the table, the suppression of AC and DA in face of an alternative antecedent is realized in our framework. The suppression of AA and DC in face of an additional antecedent is also realized if the additional condition is written in the antecedent of the original conditional sentence.

Table 2. Summary of inferences made by completion

5.2 Wason selection task

Wason (Reference Wason1968) introduces the selection task for examining human conditional reasoning. The task is described as follows. There are four cards on a table, each of which has a letter on one side and a number on the other. Suppose that those cards are showing respectively D, K, 3, 7. Given the sentence: “Every card which has the letter D on one side has the number 3 on the other side," then which cards are to be turned over in order to verify the truth of the sentence? Testing on college students, it turns out that a relatively small number of students select the logically correct answer “D and 7” (4%), while others select “D and 3” (46%) or D alone (33%) (Wason and Shapiro Reference Wason and Shapiro1971). The result shows that people are likely to perform AC inference but less likely to perform logically correct DC inference in this task. The situation is characterized in our framework as follows.

The sentence: “Every card which has the letter D on one side has the number 3 on the other side" is rephrased as “If a card has the letter D on one side, then it has the number 3 on the other side." Then, it is represented by the program:

(15) \begin{equation}\Pi_W =\{\, n_3 \leftarrow \ell_D \}\end{equation}

where $n_3$ means the number 3 and $\ell_D$ means the letter D. Four cards on the desk are represented by the facts:

(16) \begin{equation} \ell_D\leftarrow,\quad \ell_K\leftarrow,\quad n_3\leftarrow,\quad n_7\leftarrow.\end{equation}

Then, each card is checked one by one.

• • $\Pi_W\cup \{\ell_D\leftarrow\}$ has the answer set $\{\ell_D, n_3\}$ . If the other side of $\ell_D$ is not the number 3, however, $\{\ell_D, n_3\}\cup\{\neg n_3\}$ is contradictory. To verify the consistency, one has to turn over the card of D.

• • $\Pi_W\cup \{\ell_K\leftarrow\}$ has the answer set $\{\ell_K\}$ . Since both $\{\ell_K\}\cup\{n_3\}$ and $\{\ell_K\}\cup\{\neg n_3\}$ are consistent, there is no need to turn over the card of K.

• • $\Pi_W\cup \{n_3\leftarrow\}$ has the answer set $\{n_3\}$ . Since both $\{n_3\}\cup\{\ell_D\}$ and $\{n_3\}\cup\{\neg \ell_D\}$ are consistent, there is no need to turn over the card of 3.

• • $\Pi_W\cup \{n_7\leftarrow\}$ has the answer set $\{n_7\}$ . Since both $\{n_7\}\cup\{\ell_D\}$ and $\{n_7\}\cup\{\neg\ell_D\}$ are consistent, there is no need to turn over the card of D.

As the standard ASP does not realize DC inference, it characterizes reasoners who select only D as shown above.

By contrast, people who choose D and 3 are likely to perform AC inference using the conditional sentence. In this case, the situation is represented as

$$ AC(\Pi_W) = \{\, n_3 \leftarrow \ell_D,\;\;\; \ell_D\leftarrow n_3\,\}. $$

Now $AC(\Pi_W)\cup \{n_3\leftarrow\}$ has the answer set $\{n_3,\ell_D\}$ . If the other side of $n_3$ is not the letter D, however, $\{n_3,\ell_D\}\cup\{\neg\ell_D\}$ is contradictory. To verify the consistency, they opt to turn over the card of 3 as well as the card of D.

Finally, those who choose D and 7 perform weak DC inference as^{Footnote 4}

$$ \mathit{WDC}(\Pi_W)=\{\, n_3 \leftarrow \ell_D,\;\;\; not\,\ell_D\leftarrow not\,n_3\,\}.$$

The program $\mathit{WDC}(\Pi_W)\cup \{n_7\leftarrow\}$ has the answer set $\{n_7\}$ . However, if the other side of $n_7$ is the letter D, $\mathit{WDC}(\Pi_W)\cup \{n_7\leftarrow\}\cup\{ \ell_D\leftarrow\}\cup \{\leftarrow n_3,n_7\}$ is incoherent, where the constraint “ $\leftarrow n_3, n_7$ " represents that one card cannot have two numbers. To verify the consistency, they need to turn over the card of 7 as well as the card of D.^{Footnote 5}

It is known that the Wason selection task is context dependent and the results change when, for instance, it is presented with a deontic rule. Griggs and Cox (Reference Griggs and Cox1982) use the rule: “If a person is drinking beer, then the person must be over 19 year of age." There are four cards on a table as before, but this time a person’s age is on one side of a card and on the other side is what the person is drinking. Four cards show, “beer,” “coke,” “16,” and “22,” respectively. Then select the card(s) needed to turn over to determine whether or not no person is violating the rule. In this drinking-age problem, almost 75% of participants select logically correct answers “beer” and “16.” To characterize the situation, (W)DC completion is used for representing selection tasks in deontic contexts.

6.1 Abduction and prediction

Abduction reasons from an observation to explanations. An abductive logic program (Kakas et al. Reference Kakas, Kowalski and Toni1992) is defined as a pair $\langle\,\Pi,\Gamma\,\rangle$ where $\Pi$ is a program and $\Gamma\, (\subseteq Lit$ ) is a set of literals called abducibles.^{Footnote 6} It is assumed that abducibles appear in the head of no rule in $\Pi$ . Given an observation O as a ground literal, the abduction problem is to find an explanation $E\,(\subseteq \Gamma)$ satisfying (i) $\Pi\cup E\models_x O$ and (ii) $\Pi\cup E$ is consistent, where $\models_x$ is either $\models_c$ or $\models_s$ depending on the problem. Here we consider $\models_c$ that realizes credulous abduction. In GEDP, the abduction problem is characterized as follows. Let us define

$$abd(\Gamma)=\{\, \gamma\,;\,not\,\gamma\,\leftarrow\;\mid\; \gamma\in\Gamma\,\}.$$

Example 6.1 Consider $(\Pi_1,\Gamma_1)$ where

\begin{eqnarray*}\Pi_1:&& arrive\_on\_time\leftarrow not\;accident,\\&& \neg\,arrive\_on\_time\leftarrow accident. \\\Gamma_1:&& accident.\end{eqnarray*}

$\Pi_1$ represents that a train arrives on time unless there is an accident. Given the observation $O=\neg\,arrive\_on\_time$ , it has the explanation $E=\{accident\}$ . The problem is represented as the GEDP:

\begin{eqnarray*}&& arrive\_on\_time\leftarrow not\;accident,\\&& \neg\,arrive\_on\_time\leftarrow accident, \\&& accident\,;\, not\,accident\leftarrow,\\&& \leftarrow not\,\neg\,arrive\_on\_time\end{eqnarray*}

which has the answer set $S=\{ \neg\,arrive\_on\_time,\; accident \}$ where $S\cap\Gamma_1=E$ .

Since abduction reasons backward from an observation, it is characterized using AC inference as follows.

In Example 6.1, $AC(\Pi_1)\cup \{ O\}$ becomes

\begin{eqnarray*}&& arrive\_on\_time\leftarrow not\;accident,\\&& \neg\,arrive\_on\_time\leftarrow accident. \\\end{eqnarray*}

\begin{eqnarray*}&& not\;accident\leftarrow arrive\_on\_time,\\&& accident\leftarrow \neg\,arrive\_on\_time,\\&& \neg\,arrive\_on\_time \leftarrow,\end{eqnarray*}

which has the answer set $S=\{\, \neg\,arrive\_on\_time,\, accident\,\}$ . By $S\cap\Gamma_1=\{\,accident\,\}$ , $E=\{\,accident\,\}$ is the explanation.

Note that $AC(\Pi)$ introduces converse of every rule, while explanations are computed using the AC completion of a subset $\Pi'\subseteq \Pi$ in general (Proposition 6.2(ii)).

As illustrated in Example 6.2, abduction and AC completion produce different results in general.

Abductive logic programs of Kakas et al. (Reference Kakas, Kowalski and Toni1992) cannot compute explanations when contrary to the consequent is observed. For instance, consider $\langle\,\Pi_2,\Gamma_2\,\rangle$ such that

\begin{eqnarray*}\Pi_2:&& arrive\_on\_time\leftarrow not\;accident.\\\Gamma_2:&& accident.\end{eqnarray*}

Given the observation $O=\neg\,arrive\_on\_time$ , no explanation is obtained from $\langle\,\Pi_2,\Gamma_2\,\rangle$ . Generally, a program $\Pi$ does not necessarily contain a pair of rules r and r’ that define L and $\neg L$ , respectively. When there is a rule defining L but no rule defining $\neg L$ , abduction computes no explanation for the observation $O=\neg L$ . The problem is resolved by reasoning by DC. For the rule r in $\Pi_2$ , sdc(r) becomes

$$ accident\leftarrow \neg\, arrive\_on\_time.$$

Then, $SDC(\Pi_2)\cup\{O\}$ computes the explanation $\{\,accident\,\}$ .

In contrast to SDC, WDC is used for abduction from negative observations. A negative observation represents that some evidence G is not observed, and it is represented as $O=not\,G$ , which should be distinguished from the (positive) observation $O=\neg\,G$ meaning that $\neg\,G$ is observed. In the abductive program $\langle\,\Pi_2,\Gamma_2\,\rangle$ , the negative observation $O=not\,arrive\_on\_time$ is explained using wdc(r):

$$ accident\leftarrow not\; arrive\_on\_time.$$

Then, $WDC(\Pi_2)\cup\{O\}$ has the answer set $\{\,accident\,\}$ .

In this way, both AC and DC are used for computing explanations deductively, and DC is used for computing explanations that are not obtained using the framework of Kakas et al. (Reference Kakas, Kowalski and Toni1992). Moreover, AC and DC realize prediction by combining abduction and deduction.

Example 6.3 Consider $(\Pi_3,\Gamma_3)$ where

\begin{eqnarray*}\Pi_3:&& arrive\_on\_time\leftarrow not\;accident,\\&& \neg\,arrive\_on\_time\leftarrow accident, \\&& newspaper \leftarrow accident.\\\Gamma_3:&& accident.\end{eqnarray*}

The third rule in $\Pi_3$ says: if there is an accident, a newspaper will report it. Given the observation $O=\neg\,arrive\_on\_time$ , $AC(\Pi_3)\cup \{ O\}\models_s newspaper$ .

As such, AC or DC combines abduction and deduction to realize prediction.

6.2 Counterfactual reasoning

A counterfactual is a conditional statement representing what would be the case if its premise were true (although it is not true in fact). Lewis (Reference Lewis1973) introduces two different types of counterfactual sentences. Given two different events ${\varphi}$ and $\psi$ , two counterfactual sentences are considered: “if it were the case that ${\varphi}$ , then it would be the case that $\psi$ " (written ${\varphi}\,\Box\!\!\!\rightarrow\,\psi$ ) and “if it were the case that ${\varphi}$ , then it might be the case that $\psi$ " (written ${\varphi}\,\Diamond\!\!\!\rightarrow\,\psi$ ). Here $({\varphi}\,\Box\!\!\!\rightarrow\,\psi)$ implies $({\varphi}\,\Diamond\!\!\!\rightarrow\,\psi)$ . We consider counterfactual reasoning such that what would be the case if some facts were not true. We then realize Lewis’s two types of counterfactual reasoning using DA inference in ASP.

Definition 6.1 (counterfact) Let ${\varphi}$ be a fact of the form:

$$ L_1\,;\,\cdots\,;\, L_k\, ;\, not\,L_{k+1}\,;\cdots ;\,not\,L_l\leftarrow \quad (1\le k\le l).$$

Then, the counterfact $\overline{{\varphi}}$ is the set of facts such that

$$ \overline{{\varphi}}=\{\,\neg\,L_i\leftarrow \,\mid\, 1\le i\le k\,\}\;\cup\; \{\, L_j\leftarrow\,\mid\, k+1\le j\le l\,\}.$$

Given a set $\Sigma$ of facts, define

$$\overline{\Sigma}=\bigcup_{{\varphi}\in\Sigma} \overline{{\varphi}}.$$

We say that $\overline{\Sigma}$ satisfies the conjunction “ $L_{l+1},\,\ldots,\,L_m,\,not\,L_{m+1},\,\ldots,\,not\,L_n$ " if $\{\, L_i\leftarrow \,\mid\, l+1\le i\le m\}\subseteq\overline{\Sigma}$ and $\{\, L_j\leftarrow \,\mid\, m+1\le i\le n\}\cap\overline{\Sigma}={\varnothing}$ .

Definition 6.2 (counterfactual program) Let $\Pi$ be a program and $\Sigma$ a set of facts in $\Pi$ . Then, a counterfactual program $\Omega$ is defined as

$$\Omega=(\Pi\setminus\Sigma)\;\cup\; \overline{\Sigma} \;\cup\; sda(\Pi).$$

For $\lambda\in Lit$ , define

\begin{eqnarray*}&& \overline{\Sigma}\;\Box\!\!\!\rightarrow\,\lambda\;\;\mbox{if}\;\; \Omega\models_s\lambda,\\&& \overline{\Sigma}\;\Diamond\!\!\!\rightarrow\,\lambda\;\;\mbox{if}\;\;\Omega\models_c\lambda.\end{eqnarray*}

By definition, $\Omega$ is obtained from $\Pi$ by removing a set $\Sigma$ of facts, and instead introducing a set $\overline{\Sigma}$ of counterfacts as well as strong DA rules $sda(\Pi)$ . $\overline{\Sigma}\;\Box\!\!\!\rightarrow\,\lambda$ (resp. $\overline{\Sigma}\;\Diamond\!\!\!\rightarrow\,\lambda$ ) means that if the counterfacts $\overline{\Sigma}$ were the case, then $\lambda$ is included in every (resp. some) answer set of $\Omega$ .

Example 6.4 Consider the program $\Pi$ :

\begin{eqnarray*}&& London\,;\,Paris \leftarrow not\; virtual,\\&& virtual\leftarrow pandemic,\\&& pandemic \leftarrow.\end{eqnarray*}

An event is scheduled to take place in either London or Paris if it is not virtual. The pandemic turns the event into virtual, however. Suppose a counterfactual sentence: “ if there were no pandemic, the event would not be virtual." Putting $\Sigma=\{\,pandemic\,\}$ , $\Omega$ becomes:

\begin{eqnarray*}&& London\,;\,Paris \leftarrow not\; virtual,\\&& virtual\leftarrow pandemic,\\&& \neg\,London\leftarrow virtual,\\&& \neg\,Paris\leftarrow virtual,\\&& \neg\,virtual\leftarrow \neg\,pandemic,\\&& \neg\,pandemic \leftarrow.\end{eqnarray*}

Then, $\Omega$ has two answer sets:

\begin{eqnarray*}&& \{\, \neg\,pandemic,\; \neg\,virtual,\; London\,\},\\&& \{\, \neg\,pandemic,\; \neg\,virtual,\; Paris\,\}.\end{eqnarray*}

As a result, it holds that

$$\{\neg\,pandemic\}\,\Box\!\!\!\rightarrow\,\neg\,virtual\quad\mbox{and}\quad\{\neg\,pandemic\}\,\Diamond\!\!\!\rightarrow\,London. $$

Given a consistent program $\Pi$ , it might be the case that the program $\Omega$ is inconsistent. To eliminate contradictory $\Omega$ , default DA completion is used instead of DA completion. Define $\Omega_D$ that is obtained by replacing $sda(\Pi)$ by its default version $sdda(\Pi)$ in Definition 6.2. The next result holds by Proposition 4.4.

6.3 Neighborhood inference

Cooperative query answering analyzes the intent of a query and provides associated information relevant to the query (Chu et al. Reference Chu, Chen and Lee1990). Neighborhood inference (Gaasterland et al. Reference Gaasterland, Godfrey and Minker1992) is a technique used for such a purpose and reasons up/down in a taxonomy of atoms to reach neighboring solutions.

Example 6.5 A travel agency has flight information represented by the program $\Pi$ :

\begin{eqnarray*}&& travel(LHR,CDG)\leftarrow flight(AF1681),\\&& travel(LHR,CDG)\leftarrow flight(BA306),\\&& travel(NRT,CDG)\leftarrow flight(AF275),\\&& flight(BA306)\leftarrow,\;\;\; \neg\,flight(AF1681)\leftarrow,\;\;\;flight(AF275)\leftarrow\end{eqnarray*}

where “ $travel(X,Y)\leftarrow flight(Z)$ " means that a flight Z is used for traveling from X to Y. Suppose that a customer asks the availability of a flight AF1681 from LHR to CDG. Unfortunately, no ticket is available on the requested time of a day. The agent then proposes an alternative flight BA306 that is still available.

In this scenario, from the request flight(AF1681) the agent understands that the customer wants to travel from London (LHR) to Paris (CDG). The request flight(AF1681) is then relaxed to travel(LHR,CDG) and reaches the fact flight(BA306).

Neighborhood inference consists of two steps: generalization from the body to the head of one rule and specialization from the head to the body of another rule. The generalization is the inference of affirming the antecedent, while the specialization is the inference of affirming the consequent. Then, neighborhood inference is realized by combining AA and AC. In what follows, we consider a binary program $\Pi$ that consists of ground rules of the form “ $L_1\leftarrow L_2$ " where $L_1$ and $L_2$ are positive/negative literals ( $L_2$ is possibly empty). $\Pi$ is partitioned into the set of non-factual rules $\Pi_R$ and the set of facts $\Pi_F$ , that is, $\Pi=\Pi_R\cup\Pi_F$ .

Definition 6.3 (neighborhood solution) Let $\Pi$ be a binary program such that $\Pi=\Pi_R\cup \Pi_F$ and G a ground literal representing a request. Define

\begin{eqnarray*}U&=&\{\,r\,\mid\, r\in \Pi_R \;\mbox{and}\; body^+(r)=\{G\}\,\},\\V&=&\{\,r'\,\mid\, r'\in\Pi_R\setminus U\;\mbox{and}\; head^+(r')=head^+(r)\; \mbox{for some}\; r\in U\,\}.\end{eqnarray*}

If U and V are non-empty and $\Pi_R\cup ac(V)\cup \{G\}$ has a consistent answer set S, then $S\cap \Pi_F$ is called a neighborhood solution of G.

By definition, the AC completion is applied to a part of the program, which realizes neighborhood inference based on a request.

Example 6.6 (cont. Example 6.5) Given the request $G=flight(AF1681)$ , $U=\{\, travel(LHR,CDG)\leftarrow flight(AF1681) \,\}$ and $V=\{\, travel(LHR,CDG)\leftarrow flight(BA306) \,\}$ . Then, $ac(V)=\{\,flight(BA306)\leftarrow travel(LHR,CDG)\,\}$ , and $\Pi_R\cup ac(V)$ becomes

\begin{eqnarray*}&& travel(LHR,CDG)\leftarrow flight(AF1681),\\&& travel(LHR,CDG)\leftarrow flight(BA306),\\&& travel(NRT,CDG)\leftarrow flight(AF275),\\&& flight(BA306) \leftarrow travel(LHR,CDG).\end{eqnarray*}

$\Pi_R\cup ac(V)\cup \{G\}$ has the answer set

$$S=\{\, flight(AF1681),\, flight(BA306),\, travel(LHR,CDG)\,\}.$$

Hence, $S\cap \Pi_F=\{ flight(BA306) \}$ is a neighborhood solution of G.

7.1 Completion

The idea of interpreting if-then rules in logic programs as bi-conditional dates back to Clark (Reference Clark and Press1978). He introduces predicate completion in normal logic programs (NLPs), which introduces the only-if part of each rule to a program. Given a propositional NLP $\Pi$ , Clark completion $Comp(\Pi)$ is obtained by two steps: (i) all rules “ $p\leftarrow B_1$ ", $\ldots$ , “ $p\leftarrow B_k$ " in $\Pi$ having the same head p are replaced by “ $p\leftrightarrow B_1\vee\cdots\vee B_k,$ " where $B_i$ $(1\le i\le k)$ is a conjunction of literals; and (ii) for any atom p appearing in the head of no rule in $\Pi$ , add “ $p\leftrightarrow \mathit{false.}$ " The AC completion introduced in this paper extends the technique to the class of GEDP, while the result is generally different from Clark completion in NLPs. For instance, given the program:

$$ \Pi_1=\{\, p\leftarrow q,\;\;\; p\leftarrow \,\}, $$

Clark completion becomes

$$ Comp(\Pi_1)=\{\, p\leftrightarrow q\vee\top,\;\;\; q\leftrightarrow \bot \,\} $$

where $\top$ and $\bot$ represent true and false, respectively. $Comp(\Pi_1)$ has the single completion model called a supported model (Apt et al. Reference Apt, Blair, Walker and Kaufmann1988) $\{p\}$ . In contrast,

$$AC(\Pi_1)=\Pi_1\cup \{\,q\leftarrow p \,\}$$

has the answer set $\{p,q\}$ . The difference comes from the fact that in $Comp(\Pi_1)$ , q is identified with false but this is not the case in $AC(\Pi_1)$ . In Clark completion, undefined atoms (i.e. atoms appearing in the head of no rule) are interpreted false. We do not use this type of completion because it disturbs the basic type of AC reasoning that infers q from p and “ $p\leftarrow q$ ." Clark completion is extended to normal disjunctive programs by several researchers (Lobo et al. Reference Lobo, Minker, Rajasekar, Press and Cambridge1988; Alviano and Dodaro Reference Alviano and Dodaro2016; Nieves and Osorio Reference Nieves and Osorio2018).Those extensions reduce to Clark completion in NLPs, so that they are different from the AC completion. We also introduce the DC completion and the DA completion. When $\Pi_2=\{\, p\leftarrow not\,q\,\}$ , $Comp(\Pi_2)=\{\, p\leftrightarrow \neg q,\;\; q\leftrightarrow\bot \,\}$ has the supported model $\{p\}$ . On the other hand,

$$WDC(\Pi_2)=\Pi_2\cup \{\, q\leftarrow not\,p\,\}$$

has two answer sets $\{p\}$ and $\{q\}$ . When $\Pi_3=\{\, p\leftarrow not\,q,\;\; p\leftarrow q,\;\; q\leftarrow p\,\}$ , $Comp(\Pi_3)=\{\, p\leftrightarrow q\vee\neg q,\;\; q\leftrightarrow p\,\}$ has the supported model $\{p,q\}$ . In contrast,

$$ WDA(\Pi_3)=\Pi_3\cup\{\,not\,p\leftarrow q,not\,q,\;\; not\,q\leftarrow not\,p\,\}$$

has no answer set. As such, completion semantics introduced in this paper is generally different from Clark completion in NLPs.

The weak completion (Hölldobler and Kencana Ramli Reference Hölldobler and Kencana Ramli2009) leaves undefined atoms unknown under 3-valued logic. In the program $\Pi_1=\{\, p\leftarrow q,\;\; p\leftarrow \,\}$ , the weak completion becomes

$$ wcomp(\Pi_1)=\{\, p\leftrightarrow q\vee\top \,\} $$

which is semantically equivalent to $\{\, p\leftrightarrow \top \,\}$ . Then, p is true but q is unknown in $wcomp(\Pi_1)$ , which is again different from $AC(\Pi_1)$ that has the answer set $\{p,q\}$ . In the program $\Pi_2=\{\, p\leftarrow not\,q\,\}$ , the weak completion becomes

$$ wcomp(\Pi_2)=\{\, p\leftrightarrow \neg q \,\} $$

then both p and q are unknown. In contrast, $WDC(\Pi_2)$ has two answer sets $\{p\}$ and $\{q\}$ , and $WDA(\Pi_2)$ has the single answer set $\{p\}$ .

7.2 Human conditional reasoning

Stenning and Lambalgen (2008) formulate human conditional reasoning using Clark’s program completion under the three-valued logic of Fitting (Reference Fitting1985). They represent a conditional sentence “if p then q" as a logic programming rule:^{Footnote 8}

$$ q\leftarrow p\wedge \neg ab$$

where ab represents an abnormal atom. In this setting, DA is represented as

$$ \Pi_1=\{\,p\leftarrow\bot,\;\;\; q\leftarrow p\wedge\neg ab,\;\;\; ab\leftarrow\bot\,\}.$$

The rule “ $A\leftarrow\bot$ " means that A is a proposition to which the closed world assumption (Reiter Reference Reiter and Press1978) is applied. If a program does not contain “ $A\leftarrow\bot,$ " nor any other rule in which A occurs in its head, then A is interpreted unknown. Then, its completion

$$ Comp(\Pi_1)=\{\,p\leftrightarrow \bot,\;\;\; q\leftrightarrow p\wedge\neg ab,\;\;\; ab\leftrightarrow \bot\,\}$$

derives “ $q\leftrightarrow\bot$ ." On the other hand, completion does not realize AC or DC inference by itself. In their framework, AC is represented as

$$ \Pi_2=\{\,q\leftarrow\top,\;\; q\leftarrow p\wedge\neg ab,\;\; ab\leftarrow\bot\,\}$$

while its completion

$$ Comp(\Pi_2)=\{\,q\leftrightarrow \top\vee (p\wedge\neg ab),\;\; ab\leftrightarrow\bot\,\}$$

does not derive p. Likewise, DC is represented as

$$ \Pi_3=\{\,q\leftarrow\bot,\;\; q\leftarrow p\wedge\neg ab,\;\; ab\leftarrow\bot\,\}$$

while its completion

$$ Comp(\Pi_3)=\{\,q\leftrightarrow \bot\vee (p\wedge\neg ab),\;\; ab\leftrightarrow\bot\,\}$$

does not derive “ $p\leftrightarrow\bot$ ." They then interpret “ $q\leftarrow p\wedge\neg ab$ " as an integrity constraint meaning that “if q succeeds (resp. fails) then “ $p\wedge\neg ab$ ” succeeds (resp. fails)" to get the AC consequence p (resp. DC consequence $\neg p$ ).

Stenning and Lambalgen (2008) characterize the suppression task in their formulation. The sentence “If she has an essay to write then she will study late in the library" is represented as:

$$ library\;\leftarrow\; essay\wedge \neg ab_1.$$

Given the negation of antecedent $\neg essay$ (or equivalently, the CWA rule “ $essay\leftarrow\bot$ "), the completed program:

\begin{eqnarray*} && library\;\leftrightarrow essay\wedge \neg ab_1,\\ && ab_1\leftrightarrow\bot,\\ && essay\leftrightarrow\bot\end{eqnarray*}

derives “ $library\leftrightarrow\bot$ ". Next, suppose that the conditional with an alternative antecedent: “If she has some textbooks to read then she will study late in the library" is given. The program becomes

\begin{eqnarray*}&& library\;\leftarrow\; essay\wedge \neg ab_1,\\&& library\;\leftarrow\; text\wedge \neg ab_2.\end{eqnarray*}

Given the fact $\neg essay$ (or equivalently, the CWA rule “ $essay\leftarrow\bot$ "), the completed program:

\begin{eqnarray*} && library\;\leftrightarrow\; (essay\wedge \neg ab_1)\vee (text\wedge \neg ab_2),\\ && ab_1\leftrightarrow\bot,\\ && ab_2\leftrightarrow\bot,\\ && essay\leftrightarrow\bot\end{eqnarray*}

does not derive “ $library\leftrightarrow\bot$ ". Thus, the DA inference is suppressed. They also characterize Byrne’s suppression of valid inference. Suppose the conditional with an additional antecedent: “If the library stays open then she will study late in the library." The program becomes

\begin{eqnarray*}&& library\;\leftarrow\; essay\wedge \neg ab_1,\\&& library\;\leftarrow\; open\wedge \neg ab_3.\end{eqnarray*}

They also introduce interaction of abnormality atoms as

\begin{eqnarray*}&& ab_1\leftarrow \neg open,\\&& ab_3\leftarrow \neg essay.\end{eqnarray*}

Completing these four rules with “ $ab_1\leftarrow \bot$ " and “ $ab_3\leftarrow\bot$ " produces

\begin{eqnarray*}&& library\leftrightarrow (essay\wedge \neg ab_1)\vee (open\wedge \neg ab_3), \\&& ab_1\leftrightarrow \neg open\vee\bot,\\&& ab_3\leftrightarrow \neg essay\vee\bot,\end{eqnarray*}

which reduces to

$$ library\leftrightarrow open\wedge essay. $$

Then, essay alone does not deduce library, so the AA inference is suppressed. Stenning and Lambalgen (2008) argue that most people represent the effect of an additional premise formally as “ $p\leftarrow q\wedge r$ " and that of an alternative premise formally as “ $p\leftarrow q\vee r$ ." This argument coincides with our view addressed in Section 5.1.

Dietz et al. (Reference Dietz, Hölldobler and Ragni2012) point out a technical flaw in the formulation by Stenning and Lambalgen (2008). In the above example, open and library are unknown ( U) under the 3-valued logic, then the rule “ $library\,\leftarrow\, open\wedge \neg ab_3$ " becomes “ ${\sf U}\leftarrow {\sf U.}$ " Under the Fitting semantics, however, the truth value of the rule “ ${\sf U}\leftarrow {\sf U}$ " is ${\sf U}$ , then it does not represent the truth of the rule “ $library\,\leftarrow\, open\wedge \neg ab_3$ ." To remedy the problem, they employ Łukasiewicz’s 3-valued logic which maps “ ${\sf U}\leftarrow {\sf U}$ " to $\top$ . Dietz et al. (Reference Dietz, Hölldobler and Ragni2012) also characterize the suppression effects in AC or DC using an abductive logic program $\langle\,\Pi, \Gamma\,\rangle$ with abducibles $\Gamma=\{\, p\leftarrow\bot,\;\; p\leftarrow\top\,\}$ . Consider $\langle\,\Pi_1, \Gamma_1\,\rangle$ where

\begin{eqnarray*}\Pi_1:&& library\leftarrow essay\wedge \neg ab_1,\\&& ab_1\leftarrow\bot,\\\Gamma_1:&& essay\leftarrow\bot,\;\;\; essay\leftarrow\top,\end{eqnarray*}

the weakly completed program of $\Pi_1$ becomes

\begin{eqnarray*} && library\leftrightarrow essay\wedge \neg ab_1,\\ && ab_1\leftrightarrow\bot.\end{eqnarray*}

The observation $O=(library\leftrightarrow\top)$ derives “ $essay\leftrightarrow\top$ ," then “ $essay\leftarrow\top$ " is the skeptical explanation of O. When the additional rules and abducibles

\begin{eqnarray*}\Pi_2: && library\leftarrow text\wedge \neg ab_2,\\&& ab_2\leftarrow\bot,\\\Gamma_2: && text\leftarrow\bot,\;\;\; text\leftarrow\top\end{eqnarray*}

are given, the weakly completed program of $\Pi_1\cup\Pi_2$ becomes

\begin{eqnarray*} && library\leftrightarrow (essay\wedge \neg ab_1)\vee (text\wedge\neg ab_2),\\ && ab_1\leftrightarrow\bot,\\ && ab_2\leftrightarrow\bot.\end{eqnarray*}

The observation $O=(library\leftrightarrow\top)$ derives “ $essay\vee text\leftrightarrow\top,$ " and there are two credulous explanations “ $essay\leftarrow\top$ " and “ $text\leftarrow\top$ " in $\Gamma_1\cup\Gamma_2$ . In this case, “ $essay\leftarrow\top$ " is not concluded under skeptical reasoning, which represents the suppression of AC.

Comparing the above mentioned two studies with our approach, there are several differences. First, they translate a conditional sentence “if p then q" into the rule “ $q\leftarrow p\wedge \neg\,ab.$ " However, it is unlikely that people who commit logical fallacies, especially younger children (Rumain et al. Reference Rumain, Connell and Braine1983), translate the conditional sentence into the rule of the above complex form in their mind. We represent the conditional sentence directly as “ $q\leftarrow p$ " and assume that people would interpret it as bi-conditional depending on the context in which it is used. Second, in order to characterize AC or DC reasoning, Stenning and Lambalgen (2008) interpret a conditional sentence as an integrity constraint, while Dietz et al. (Reference Dietz, Hölldobler and Ragni2012) use abductive logic programs. Our framework does not need a specific interpretation of rules (such as integrity constraints) nor need an extra mechanism of abductive logic programs. Third, they use a single (weak) completion for all AC/DA/DC reasoning, while we introduce different types of completions for each inference. By separating respective completions, individual inferences are realized in a modular way and freely combined depending on their application context. For instance, we use AC and (S)DA completion to characterize the suppression task (Section 5.1), while use AC and (W)DC completion to characterize Wason selection task (Section 5.2). Fourth, they handle normal logic programs, while our framework can handle a more general class of logic programs containing disjunction and two different types of negation. For instance, consider the rule: “ $buy\_car \,;\, buy\_house \leftarrow win\_lottery$ " (if she wins the lottery, she buys a car or a house). When it is known that $buy\_car$ is true, one may infer $win\_lottery$ by AC inference. The AC completion realizes such an inference by introducing rules: “ $win\_lottery\leftarrow buy\_car$ " and “ $win\_lottery\leftarrow buy\_house.$ "

Cramer et al. (Reference Cramer, Hölldobler and Ragnl2021) represent conditionals as in Stenning and Lambalgen (2008) and use the weak completion and abductive logic programs as in Dietz et al. (Reference Dietz, Hölldobler and Ragni2012). They formulate different types of conditionals based on their contexts and argue in which case AC or DC is more likely to happen. More precisely, a conditional sentence whose consequent appears to be obligatory given the antecedent is called an obligation conditional. An example of an obligation conditional is that “ if Paul rides a motorbike, then he must wear a helmet." If the consequence of a conditional is not obligatory, then it is called a factual conditional. The antecedent A of a conditional sentence is said to be necessary iff its consequent C cannot be true unless A is true. For example, the library’s being open is a necessary antecedent for studying in the library. Cramer $et\,al.$ argue that AA and DA occur independently of the type of a conditional. On the other hand, in AC most people will conclude A from “ $A\Rightarrow C$ " and C, while the number of people who conclude nothing will increase if A is a non-necessary antecedent. In DC, most people will conclude $\neg\,A$ from “ $A\Rightarrow C$ " and $\neg\,C$ , while the number of people who conclude nothing will increase if the conditional is factual. Those assumptions are verified by questioning participants who do not receive any education in logic beyond high school training. They then formulate the situation by introducing the abducible “ $C\leftarrow\top$ " if the antecedent is non-necessary, and “ $ab\leftarrow\top$ " if the conditional is factual. In the former case, the observation C does not imply A because the additional “ $C\leftarrow\top$ " can make C explainable by itself. As a result, A is not a skeptical explanation of C. In the latter case, the observation $\neg\, C$ does not imply $\neg\, A$ because if one employs the explanation “ $ab\leftarrow\top$ " then “ $C\leftarrow A\wedge \neg ab$ " does not produce “ $C\leftrightarrow A$ ."

Dietz et al. (Reference Dietz, Fichte and Hamiti2022) use logic programming rules to represent different types of conditionals. For instance,

$$ {\sf concl}\leftarrow {\sf prem(x)},\, {\sf sufficient(x)} $$

represents MP that ${\sf concl}$ follows if a sufficient premise is asserted to be true. By contrast,

$$ {\sf not\_concl}\leftarrow {\sf not\_prem(x)},\, {\sf necessary(x)} $$

represents DA that ${\sf concl}$ does not follow if a necessary premise is asserted to be false. With these rules, Byrne’s suppression effect is represented as follows. First, given the fact ${\sf prem(essay)}$ and ${\sf sufficient(essay)}$ ,

$$ {\sf library}\leftarrow {\sf prem(essay)},\, {\sf sufficient(essay)} $$

implies ${\sf library}$ . Next, given the additional fact ${\sf necessary(open)}$ and in the absence of ${\sf prem(open)}$

$$ {\sf not\_library}\leftarrow {\sf not\_prem(open)},\, {\sf necessary(open)}$$

has the effect of withdrawing ${\sf library}$ . In the current study, we do not distinguish different types of conditionals as in Cramer et al. (Reference Cramer, Hölldobler and Ragnl2021) and Dietz et al. (Reference Dietz, Fichte and Hamiti2022). However, completion is done for individual rules, so we could realize partial completion by selecting rules $\Pi'\subseteq \Pi$ that are subject to be completed in practice. More precisely, if a program $\Pi$ consists of rules $R_1$ having necessary antecedents and $R_2$ having non-necessary antecedents, apply AC completion to $R_1$ while keep $R_2$ as they are. The resulting program then realizes AC inference using $R_1$ only. Likewise, if a program $\Pi$ consists of rules $R_3$ having obligatory consequents and $R_4$ having factual consequents, apply DC completion to $R_3$ while keep $R_4$ as they are. The resulting program then realizes DC inference using $R_3$ only. Such a partial completion is also effectively used for commonsense reasoning in this paper (Proposition 6.2(ii), Definition 6.3).

7.3 Commonsense reasoning

Console et al. (Reference Console, Dupré and Torasso1991) and Fung and Kowalski (Reference Fung and Kowalski1997) compute abduction by deduction using Clark completion. Abduction using AC and DC completion is close to those approaches, while the approach based on Clark completion is restricted to normal logic programs (NLPs). We argued that a (positive) observation $O=\neg G$ is distinguished from a negative observation $O=not\,G$ , but such a distinction is not considered in NLPs handling only default negation. Inoue and Sakama (Reference Inoue and Sakama1999) introduce transaction programs to compute extended abduction. Extended abduction computes explanations for not only (positive) observations but also negative ones. A transaction program is constructed based on the converse of conditionals, and its semantics is operationally given as a fixpoint of the program. A transaction program is a meta-level specification of the procedure and is different from the current approach. Moreover, a transaction program is defined for NLPs only.

Pereira et al. (Reference Pereira, Aparício and Alferes1991, 2017) realize counterfactual reasoning in logic programming. In Pereira et al. (Reference Pereira, Aparício and Alferes1991), a counterfactual conditional “ ${\varphi} >\psi$ " (meaning ${\varphi}\,\Box\!\!\!\rightarrow\,\psi$ ) is evaluated in a program $\Pi$ by adding ${\varphi}$ to $\Pi$ and computing the maximal non-contradictory submodels of the new program. Then, the counterfactual conditional is true iff $\psi$ holds in all such submodels. Pereira and Saptawijaya (Reference Pereira and Saptawijaya2017) first use abduction to compute possible causes of an event. Next, they assume counterfactual assumption and verify whether an expected outcome will happen under possible causes. These studies consider extended/normal logic programs under the well-founded semantics and realize counterfactual reasoning via program revision or abductive reasoning. Unlike our approach, they do not introduce new rules for DA inference in counterfactual reasoning.

Gaasterland et al. (Reference Gaasterland, Godfrey and Minker1992) introduce neighborhood inference for query answering in Horn logic programs. The scope of a query is expanded by relaxing the specification, which allows a program to return answers related to the original query. They introduce a meta-interpreter to realize it and argue for control strategies. We show that similar reasoning is simulated in ASP using the AC completion.