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Chapter 3 introduced the accessibility relation R on the set of worlds W in defining the truth condition for the generic modal operator. In K-models, the frame <W, R> of the model was completely arbitrary. Any nonempty set W and any binary relation R on W counts as a frame for a K-model. However, when we actually apply modal logic to a particular domain and give □ a particular interpretation, the frame <W, R> may take on special properties. Variations in the principles appropriate for a given modal logic will depend on what properties the frame should have. The rest of this chapter explains how various conditions on frames emerge from the different readings we might choose for □.
Conditions Appropriate for Tense Logic
In future-tense logic, □ reads ‘it will always be the case that’. Given (□), we have that □A is true at w iff A is true at all worlds v such that wRv. According to the meaning assigned to □, R must be the relation earlier than defined over a set W of times. There are a number of conditions on the frame <W, R> that follow from this interpretation. One fairly obvious feature of earlier than is transitivity.
We will begin our study of modal logic with a basic system called K in honor of the famous logician Saul Kripke. K serves as the foundation for a whole family of systems. Each member of the family results from strengthening K in some way. Each of these logics uses its own symbols for the expressions it governs. For example, modal (or alethic) logics use □ for necessity, tense logics use H for what has always been, and deontic logics use O for obligation. The rules of K characterize each of these symbols and many more. Instead of rewriting K rules for each of the distinct symbols of modal logic, it is better to present K using a generic operator. Since modal logics are the oldest and best known of those in the modal family, we will adopt □ for this purpose. So □ need not mean necessarily in what follows. It stands proxy for many different operators, with different meanings. In case the reading does not matter, you may simply call □A ‘box A’.
First we need to explain what a language for propositional modal logic is. The symbols of the language are ⊥, →, □; the propositional variables: p, q, r, p′, and so forth; and parentheses. The symbol ⊥ represents a contradiction, → represents ‘if . . then’, and □ is the modal operator. A sentence of propositional modal logic is defined as follows:
⊥ and any propositional variable is a sentence.
If A is a sentence, then □A is a sentence.
If A is a sentence and B is a sentence, then (A→B) is a sentence.
No other symbol string is a sentence.
In this book, we will use letters ‘A’, ‘B’, ‘C’ for sentences. So A may be a propositional variable, p, or something more complex like (p→q), or ((p→ ⊥)→q). To avoid eyestrain, we usually drop the outermost set of parentheses. So we abbreviate (p→q) to p→q. (As an aside for those who are concerned about use-mention issues, here are the conventions of this book. We treat ‘⊥’, ‘→’, ‘□’, and so forth as used to refer to symbols with similar shapes. It is also understood that ‘□A’, for example, refers to the result of concatenating □ with the sentence A.)
Since there are so many different possible systems for modal logic, it is important to determine which system are equivalent, and which ones distinct from others. Figure 11.1 (on the next page) lays out these relationships for some of the best-known modal logics. It names systems by listing their axioms. So, for example, M4B is the system that results from adding (M), (4), and (B) to K. In boldface, we have also indicated traditional names of some systems, namely, S4, B, and S5. When system S appears below and/or to the left of S′ connected by a line, then S′ is an extension of S. This means that every argument provable in S is provable in S′, but S is weaker than S′, that is, not all arguments provable in S′ are provable in S.
Showing Systems Are Equivalent
One striking fact shown in Figure 11.1 is the large number of alternative ways of formulating S5. It is possible to prove these formulations are equivalent by proving the derivability of the official axioms of S5 (namely, (M) and (5)) in each of these systems and vice versa. However, there is an easier way. By the adequacy results given in Chapter 8 (or Chapter 9), we know that for each collection of axioms, there is a corresponding concept of validity. Adequacy guarantees that these notions of provability and validity correspond. So if we can show that two forms of validity are equivalent, then it will follow that the corresponding systems are equivalent. Let us illustrate with an example.
There are a number of different approaches one can take to giving the semantics for the quantifiers. The simplest method uses truth value semantics with the substitution interpretation of the quantifiers (Leblanc, 1976). Although the substitution interpretation can be criticized, it provides an excellent starting point for understanding the alternatives, since it avoids a number of annoying technical complications. For students who prefer to learn the adequacy proofs in easy stages, it is best to master the reasoning for the substitution interpretation first. This will provide a core understanding of the basic strategies, which may be embellished (if one wishes) to accommodate more complex treatments of quantification.
Truth Value Semantics with the Substitution Interpretation
The substitution interpretation is based on the idea that a universal sentence ∀xAx is true exactly when each of its instances Aa, Ab, Ac, . . , is true. For classical logic, ∀xAx is T if and only if Ac is T for each constant c of the language. In the case of free logic, the truth condition states that ∀xAx is T if and only if Ac is T for all constants that refer to a real object. Since the sentence Ec indicates that c refers to a real object, the free logic truth condition should say that Ac is T for all those constants c such that Ec is also true.
A pervasive feature of natural languages is that sentences depend for their truth value on the context or situation in which they are evaluated. For example, sentences like ‘It is raining’ and ‘I am glad’ cannot be assigned truth values unless the time, place of utterance, and the identity of the speaker are known. The same sentence may be true in one situation and false in another. In modal language, where we consider how things might have been, sentences may be evaluated in different possible worlds.
In the standard extensional semantics, truth values are assigned directly to sentences, as if the context had no role to play in their determination. This conflicts with what we know about ordinary language. There are two ways to solve the problem. The first is to translate the content of a sentence uttered in a given context into a corresponding sentence whose truth value does not depend on the context. For example, ‘It is raining’ might be converted into ‘It is raining in Houston at 12:00 EST on Dec. 9, 1997..’. The dots here indicate that the attempt to eliminate all context sensitivity may be a never-ending story. For instance, we forgot to say that we are using the Gregorian calendar, or that the sentence is to be evaluated in the real world.
English phrases that begin with ‘the’, such as ‘the man’ and ‘the present king of France’, are called definite descriptions (or descriptions, for short). So far, we have no adequate logical notation for descriptions. It is possible to translate ‘the man is bald’ by choosing a constant c for ‘the man’, a predicate letter P for ‘is bald’, and writing: Pc. However, treating the description as if it were a constant will cause us to classify some valid arguments as invalid.
For example, it should be clear that (1) entails (2).
(1) Aristotle is the philosopher who taught Alexander the Great.
(2) Aristotle taught Alexander the Great.
If we choose the constants: a for Aristotle, and g for Alexander the Great, we might notate (2) as (2′).
(2′) Tag
If we treat ‘the philosopher who taught Alexander the Great’ as a constant g, then (1) is notated by (1′).
(1′) a≈g
However, there is no logical relationship between the atomic sentences (1′) and (2′) that would cause us to recognize that the argument from (1′) to (2′) is valid. Clearly we need a way to notate the internal structure of ‘the philosopher who taught Alexander the Great’ if we are ever to show that (1) entails (2) in logic.
We have already encountered the de re – de dicto distinction at a number of points in this book. In this section, we will investigate the distinction more carefully, explain methods used to notate it, and develop quantified modal logics that are adequate for arguments involving the new notation.
Some of the best illustrations of the de re – de dicto distinction can be found among sentences of tense logic. For example, consider (S).
(S) The president was a crook.
This sentence is ambiguous. It might be taken to claim of the present president that he (Obama at the time this was written) used to be a crook. On the other hand, it might be read ‘At some time in the past the president (at that time) was a crook’. On this last reading, we are saying that the whole sentence (or dictum, in Latin) ‘the president is a crook’ was true at some past time. This is the de dicto reading of (S). Here both ‘the president’ and ‘is a crook’ are read in the past tense. We can represent this interpretation of (S) by applying the past tense operator P to the sentence ‘the president is a crook’, so that both ‘the president’ and ‘is a crook’ lie in its scope.
P(the president is a crook) de dicto reading of (S)
On the first reading of (S), we are saying a certain thing (in Latin, res) has a past tense property: of having been a crook. This is the de re reading of (S). Here we read ‘the president’ in the present tense, and ‘is a crook’ in the past tense. We can represent this reading by restricting the scope of the past tense operator P to the predicate ‘is a crook’.
the president P(is a crook) de re reading of (S)
The distinction between these two readings of (S) is a crucial one, for given that Obama never was a crook, and that Nixon was, the de dicto version of (S) is true, while the de re version is false.
The aim of this study was twofold: we investigated (a) the effect of two types of captioned video (i.e., on-screen text in the same language as the video) on listening comprehension; (b) L2 learners’ perception of the usefulness of captions while watching L2 video. The participants, 226 university-level students from a Flemish university, watched three short French clips in one of three conditions: the control group watched the clips without captions (N = 70), the second group had fully captioned clips (N = 81), the third group had keyword captioned clips (N = 75). After each clip, all participants took a listening comprehension test, which consisted of global and detailed questions. To answer the detailed questions, participants had access to an audio passage of the corresponding clip. At the end of the experiment, participants completed a questionnaire and open-ended survey questions about their perception of captions. Our findings revealed that the full captioning group outperformed both the no captioning and the keyword captioning group on the global comprehension questions. However, no difference was found between the keyword captioning and the no captioning group. Results of the detailed comprehension questions (with audio) revealed no differences between the three conditions. A content-analysis approach to the questionnaire indicated that learners’ perceived need for full captions is strong. Participants consider captions useful for speech decoding and meaning-making processes. Surprisingly, keyword captions were considered highly distracting. These findings suggest that full rather than keyword captioning should be considered when proposing video-based listening comprehension activities to L2 learners.
This study concerned multiple exposures to English before writing and aimed to explore the possibility of an increase in free active vocabulary with a focus on latent productive vocabulary beyond the first 2,000 most frequent words. The researcher incorporated online video into her college freshman composition class and examined its effects on non-basic vocabulary use. To activate previously known vocabulary, a variety of audiovisual modes before writing were applied to four groups alternately: (1) video with captions, (2) video without captions, (3) silent video with captions, and (4) video with screen off (soundtrack only). The results show that the writing involving non-captioned videos contained a higher percentage of advanced vocabulary than that with the other three conditions (specifically, 12.45% versus 11.33% with captioned videos, 5.2% with silent but captioned videos and 8.63% with audio only). Drawing upon the dual-coding theory, this study also points out some pedagogical implications for a video-based writing course.
Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation.
For many EFL learners, listening poses a grave challenge. The difficulty in segmenting a stream of speech and limited capacity in short-term memory are common weaknesses for language learners. Specifically, reduced forms, which frequently appear in authentic informal conversations, compound the challenges in listening comprehension. Numerous interventions have been implemented to assist EFL language learners, and of these, the application of captions has been found highly effective in promoting learning. Few studies have examined how different modes of captions may enhance listening comprehension. This study proposes three modes of captions: full, keyword-only, and annotated keyword captions and investigates their contribution to the learning of reduced forms and overall listening comprehension. Forty-four EFL university students participated in the study and were randomly assigned to one of the three groups. The results revealed that all three groups exhibited improvement on the pre-test while the annotated keyword caption group exhibited the best performance with the highest mean score. Comparing performances between groups, the annotated keyword caption group also emulated both the full caption and the keyword-only caption groups, particularly in the ability to recognize reduced forms. The study sheds light on the potential of annotated keyword captions in enhancing reduced forms learning and overall listening comprehension.
We study a discrete time self-interacting random process on graphs, which we call greedy random walk. The walker is located initially at some vertex. As time evolves, each vertex maintains the set of adjacent edges touching it that have not yet been crossed by the walker. At each step, the walker, being at some vertex, picks an adjacent edge among the edges that have not traversed thus far according to some (deterministic or randomized) rule. If all the adjacent edges have already been traversed, then an adjacent edge is chosen uniformly at random. After picking an edge the walker jumps along it to the neighbouring vertex. We show that the expected edge cover time of the greedy random walk is linear in the number of edges for certain natural families of graphs. Examples of such graphs include the complete graph, even degree expanders of logarithmic girth, and the hypercube graph. We also show that GRW is transient in $\mathbb{Z}^d$ for all d ≥ 3.