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As promised in the preface, this chapter is the party at the end of the book. We have chosen six of our favorite topics in extended modal logic, and we are going to tell you a little about them. There is no point in offering detailed advice here: simply read these introductory remarks and the following Chapter Guide and turn to whatever catches your fancy.
Roughly speaking, the chapter works its way from fairly concrete to more abstract. A recurrent theme is the interplay between modal and first-order ideas. We start by introducing a number of important logical modalities (and learn that we have actually been using logical modalities all through the book). We then examine languages containing the since and until operators, and show that first-order expressive completeness can be used to show modal deductive completeness. We then explore two contrasting strategies, namely the strategy underlying hybrid logic (import first-order ideas into modal logic, notably the ability to refer to worlds) and the strategy that leads to the guarded fragment of first-order logic (export the modal locality intuition to classical logic). Following this we discuss multi-dimensional modal logic (in which evaluation is performed at a sequence of states), and see that first-order logic itself can be viewed as modal logic. We conclude by proving a Lindström Theorem for modal logic.
Chapter guide
Section 7.1: Logical Modalities (Basic track). Logical modalities have a fixed interpretation in every model.
Languages of propositional modal logic are propositional languages to which sentential operators (usually called modalities or modal operators) have been added. In spite of their syntactic simplicity, such languages turn out to be useful tools for describing and reasoning about relational structures. A relational structure is a non-empty set on which a number of relations have been defined; they are widespread in mathematics, computer science, artificial intelligence and linguistics, and are also used to interpret first-order languages.
Now, when working with relational structures we are often interested in structures possessing certain properties. Perhaps a certain transitive binary relation is particularly important. Or perhaps we are interested in applications where ‘dead ends,’ ‘loops,’ and ‘forkings’ are crucial, or where each relation is a partial function. Wherever our interests lie, modal languages can be useful, for modal operators are essentially a simple way of accessing the information contained in relational structures. As we will see, the local and internal access method that modalities offer is strong enough to describe, constrain, and reason about many interesting and important aspects of relational structures.
Much of this book is essentially an exploration and elaboration of these remarks. The present chapter introduces the concepts and terminology we will need, and the concluding section places them in historical context.
Chapter guide
Section 1.1: Relational Structures. Relational structures are defined, and a number of examples are given.
Section 1.2: Modal Languages. We define the basic modal language and some of its extensions.
In this appendix we introduce the basic ideas of computability theory (the study of which problems are, and which problems are not, computationally solvable), and provide some background information on complexity theory (the study of the computational resources required to solve problems).
For detailed discussions of computability, see Rogers [391] or Odifreddi [343]. For accessible introductions to the subject, see Boolos and Jeffrey [70], or Cutland [103]. But the single most useful source is probably the (second edition of) Lewis and Papadimitriou [301]; this introduces computability theory, and then goes on to treat computational complexity. For more on computational complexity, try Garey and Johnson [163] and Papadimitriou [352]. Garey and Johnson's book is a source for information on NP-complete problems, but it discusses the basic ideas of computational complexity lucidly, and gives background information on other complexity classes. Papadimitriou's book is a well-written introduction to computational complexity covering far more than is needed to understand Chapter 6; if you want to go deeper into computational complexity, it is a good place to start.
Computability and Uncomputability
To prove theorems about computability — and in particular to prove that some problem is not computable — we need a robust mathematical model of computability. One of the most widely used models is the Turing machine. A Turing machine is a device which manipulates symbols written on a tape. The symbols are taken from some alphabet fixed in advance (often the alphabet simply consists of the two symbols 0 and 1).
Constraint Logic Programming (CLP) and Hereditary Harrop formulas (HH) are two well known ways to enhance the expressivity of Horn clauses. In this paper, we present a novel combination of these two approaches. We show how to enrich the syntax and proof theory of HH with the help of a given constraint system, in such a way that the key property of HH as a logic programming language (namely, the existence of uniform proofs) is preserved. We also present a procedure for goal solving, showing its soundness and completeness for computing answer constraints. As a consequence of this result, we obtain a new strong completeness theorem for CLP that avoids the need to build disjunctions of computed answers, as well as a more abstract formulation of a known completeness theorem for HH.
Hurwitz's extension of Abel's binomial theorem defines a probability distribution on the set of integers from 0 to n. This is the distribution of the number of non-root vertices of a fringe subtree of a suitably defined random tree with n + 2 vertices. The asymptotic behaviour of this distribution is described in a limiting regime in which the fringe subtree converges in distribution to a Galton–Watson tree with a mixed Poisson offspring distribution.
Let (Xn) be a residual allocation model with i.i.d. residual fractions Un: For W a random variable with values in [0; 1] and independent of (Xn), we define another sequence (Yn) by setting
(formula here)
Under minor regularity assumptions we show that (Xn) and (Yn) have the same probability law if and only if this law is a GEM distribution. In this case, the distribution of W and the Uns is Beta(1; θ) for some θ > 0.
Some normal logic programs under the answer set (or stable model) semantics lack the appealing property of ‘cautious monotonicity.’ That is, augmenting a program with one of its consequences may cause it to lose another of its consequences. The syntactic condition of ‘order-consistency’ was shown by Fages to guarantee existence of an answer set. This note establishes that order-consistent programs are not only consistent, but cautiously monotonic. From this it follows that they are also ‘cumulative’. That is, augmenting an order-consistent program with some of its consequences does not alter its consequences. In fact, as we show, its answer sets remain unchanged.
We present verification methods for logic programs with delay declarations. The verified properties are termination and freedom from errors related to built-ins. Concerning termination, we present two approaches. The first approach tries to eliminate the well-known problem of speculative output bindings. The second approach is based on identifying the predicates for which the textual position of an atom using this predicate is irrelevant with respect to termination. Three features are distinctive of this work: it allows for predicates to be used in several modes; it shows that block declarations, which are a very simple delay construct, are sufficient to ensure the desired properties; it takes the selection rule into account, assuming it to be as in most Prolog implementations. The methods can be used to verify existing programs and assist in writing new programs.
In the design of algorithms, the greedy paradigm provides a powerful tool for solving efficiently classical computational problems, within the framework of procedural languages. However, expressing these algorithms within the declarative framework of logic-based languages has proven a difficult research challenge. In this paper, we extend the framework of Datalog-like languages to obtain simple declarative formulations for such problems, and propose effective implementation techniques to ensure computational complexities comparable to those of procedural formulations. These advances are achieved through the use of the choice construct, extended with preference annotations to effect the selection of alternative stable-models and nondeterministic fixpoints. We show that, with suitable storage structures, the differential fixpoint computation of our programs matches the complexity of procedural algorithms in classical search and optimization problems.
The expectation of the descent number of a random Young tableau of a fixed shape is given, and concentration around the mean is shown. This result is generalized to the major index and to other descent functions. The proof combines probabilistic arguments together with combinatorial character theory. Connections with Hecke algebras are mentioned.
Let G be a planar graph without 6-cycles. We investigate structural properties of G and show that G is edge-(Δ(G) + 1)-choosable when its maximum degree Δ(G) is not 5. We also study the 3-degeneracy property of G.
We present a combinatorial lemma that provides a new approach to the two-sided exit problem and related questions for left-continuous random walks (i.e., random walks on the integers whose negative steps have size − 1). Some applications to random walks on the circle are also derived.
For each integer n, there is a natural family of probability distributions on the set of topologies on a set of n elements, parametrized by an integer variable, m. We will describe how these are constructed and analysed, and find threshold functions (for m in terms of n) for various topological properties; we focus attention on connectivity and the size of the largest component.
We use entropy ideas to study hard-core distributions on the independent sets of a finite, regular bipartite graph, specifically distributions according to which each independent set I is chosen with probability proportional to λ[mid ]I[mid ] for some fixed λ > 0. Among the results obtained are rather precise bounds on occupation probabilities; a ‘phase transition’ statement for Hamming cubes; and an exact upper bound on the number of independent sets in an n-regular bipartite graph on a given number of vertices.
This paper presents the multi-threading and internet message communication capabilities of Qu-Prolog. Message addresses are symbolic and the communications package provides high-level support that completely hides details of IP addresses and port numbers as well as the underlying TCP/IP transport layer. The combination of the multi-threads and the high level inter-thread message communications provide simple, powerful support for implementing internet distributed intelligent applications.
We describe a scheme for moving living code between a set of distributed processes coordinated with unification based Linda operations, and its application to building a comprehensive Logic programming based Internet programming framework. Mobile threads are implemented by capturing first order continuations in a compact data structure sent over the network. Code is fetched lazily from its original base turned into a server as the continuation executes at the remote site. Our code migration techniques, in combination with a dynamic recompilation scheme, ensure that heavily used code moves up smoothly on a speed hierarchy while volatile dynamic code is kept in a quickly updatable form. Among the examples, we describe how to build programmable client and server components (Web servers, in particular) and mobile agents.