Catálogo de publicaciones - libros

Abductive logic programming (ALP) can be used to model reactive, proactive and pre-active thinking in intelligent agents. Reactive thinking assimilates observations of changes in the environment, whereas proactive thinking reduces goals to sub-goals and ultimately to candidate actions. Pre-active thinking generates logical consequences of candidate actions, to help in deciding between the alternatives. These different ways of thinking are compatible with any way of deciding between alternatives, including the use of both decision theory and heuristics. The different forms of thinking can be performed as they are needed, or they can be performed in advance, transforming high-level goals and beliefs into lower-level condition-action rule form, which can be implemented in neural networks. Moreover, the higher-level and lower-level representations can operate in tandem, as they do in dual-process models of thinking. In dual process models, intuitive processes form judgements rapidly, sub-consciously and in parallel, while deliberative processes form and monitor judgements slowly, consciously and serially. ALP used in this way can not only provide a framework for constructing artificial agents, but can also be used as a cognitive model of human agents. As a cognitive model, it combines both a descriptive model of how humans actually think with a normative model of humans can think more effectively.

This paper studies the notion of ability and its relation with the notion of action in a multi-agent context. It introduces the distinction between two notions respectively called “theoretical ability” and “ability”. The main contribution of this paper is a model of these notions in the Situation Calculus.

Modal logic programming is one of appropriate approaches to deal with reasoning about epistemic states of agents. We specify here the least model semantics, the fixpoint semantics, and an SLD-resolution calculus for modal logic programs in the multimodal logic KD 4 I _ g 5_ a , which is intended for reasoning about belief and common belief of agents. We prove that the presented SLD-resolution calculus is sound and complete. We also present a formalization of the wise men puzzle using a modal logic program in KD 4 I _ g 5_ a . This shows that it is worth to study modal logic programming for multi-agent systems.

Syntactic structures based on standard syntactic assignments model knowledge directly rather than as truth in all possible worlds as in modal epistemic logic, by assigning arbitrary truth values to atomic epistemic formulae. This approach to epistemic logic is very general and is used in several logical frameworks modeling multi-agent systems, but has no interesting logical properties — partly because the standard logical language is too weak to express properties of such structures. In this paper we extend the logical language with a new operator used to represent the proposition that an agent “knows at most” a given finite set of formulae and study the problem of strongly complete axiomatization of syntactic structures in this language. Since the logic is not semantically compact, a strongly complete finitary axiomatization is impossible. Instead we present, first, a strongly complete infinitary system, and, second, a strongly complete finitary system for a slightly weaker variant of the language.

We present a weak multi-agent system of Only knowing and an analysis of the logical spaces that can be defined in it. The logic complements the approach to generalizing Levesque‘s All I Know system made by Halpern and Lakemeyer. A novel feature of our approach is that the logic is defined entirely at the object level with no reference to meta-concepts in the definition of the axiom system. We show that the logic of Halpern and Lakemeyer can be encoded in our system in the form of a particular logical space.

We present a framework for reasoning about information sources, with application to conflict resolution and belief formation at various degrees of reliability. On the basis of an assignment of relative trustworthiness to sets of information sources, a lattice of degrees of trustworthiness is constructed; from this, a priority structure is derived and applied to the problem of forming the right opinion. Consolidated with an unquestioned knowledge base, this provides an unambiguous account of what an agent should believe, conditionally on which information sources are trusted. Applications in multi-agent doxastic logic are sketched.

A deduction-based decision procedure is presented for a fragment of mutual belief logic with quantified agent variables ( MBQL ). The language of MBQL contains belief, everybody believes and mutual belief modalities, variables and constants for agents. The language of MBQL is convenient to describe the properties of rational agents when the number of agents is not known in advance. On the other hand, even if the exact number of agents is known, a language with quantified agent variables allows us to use more compact expressions. For the MBQL a sequent calculus MBQ _* with invertible (in some sense) rules is proposed. The presented decision procedure is realized by means of the calculus MBQ _* that allows us to simplify a procedure of loop-check sharply. For a fragment of MBQL (without positive occurrences of mutual belief modality), the loop-check-free sequent calculus is proposed. To this end, special rules for belief and everybody believes modalities (introducing marked modalities and indices) and special sequents serving as a termination criterion for non-derivability are introduced. For sequents containing positive occurrences of mutual belief modality sequents of special shape are used to specialize a loop-check and to find non-logical (loop-type) axioms.

In this article I will present an overview of a selection of tools for execution and proof based on temporal logic, and outline both the general techniques used and problems encountered in implementing them. This selection is quite subjective, mainly concerning work that has involved researchers I have collaborated with at Liverpool (and, previously, Manchester). The tools considered will mainly be theorem-provers and (logic-based) agent programming languages

This paper is based on the tutorial given as part of the tutorial programme of CLIMA-VI. The tutorial aimed at giving an overview of the various features available in Jason , a multi-agent systems development platform that is based on an interpreter for an extended version of AgentSpeak. The BDI architecture is the best known and most studied architecture for cognitive agents, and AgentSpeak is an elegant, logic-based programming language inspired by the BDI architecture.

This paper is a tutorial describing the main features of the KGP (Knowledge-Goals-Plan) model of agency and giving user guidance on how the model can be used to develop applications. The KGP model is based on computational logic. It consists of an abstract component, a computational component and an implementation. This paper concentrates on the abstract component, which consists of formal specifications of a number of different modules, including the knowledge bases, capabilities, transitions and control. For each of these we summarise what is provided by the model, and through the platform implementing the model, and what is left to the users to specify according to the requirements of the applications for which they wish to use the KGP model to design agents.