Ideal and Real Belief about Belief

The goal of this paper is to provide a formalization of monotonic belief and belief about belief in a multiagent environment. We distinguish between ideal beliefs, i.e., those beliefs which satisfy certain ``idealized'' properties which are unlikely to be possessed by real agents, and real beliefs. Our formalization is based on a set-theoretic specification of beliefs and, then, on the definition of the appropriate constructors which present the sets identified. This allows us to provide a uniform and taxonomic characterization of the possible ways in which ideal and real beliefs can arise. We provide intuitions about the conceptual importance of the cases analyzed by proving and discussing some equivalence results with some important modal systems modeling various forms of (non) logical omniscience

SAT-Based Decision Procedures for Automated Reasoning: a Unifying Perspective

Propositional reasoning (SAT) is an essential part of many reasoning tasks. Many problems in computer science can be compiled to SAT and then effectively decided using state-of-the-art solvers. Alternatively, if reduction to SAT is not feasible, the ideas and technology of state-of-the-art SAT solvers can be useful in deciding the propositional component of the reasoning task being considered. This last approach has been used in different contexts by different authors, many times by authors of this paper. Because of the essential role played by the SAT solver, these decision procedures have been called "SAT-based". SAT-based decision procedures have been proposed for various logics, but also in other areas such as planning. In this paper we present a unifying perspective on the various SAT-based approaches to these different reasoning tasks

SAT-Based Decision Procedures for Classical Modal Logics

We present a set of SAT-based decision procedures for various classical modal logics. By SAT-based, we mean built on top of a SAT solver. We show how the SAT-based approach allows for a modular implementation for these logics. For some of the logics we deal with, we are not aware of any other implementation. For the others, we define a testing methodology which generalizes the 3CNFK methodology by Giunchiglia and Sebastiani. The experimental evaluation shows that our decision procedures perform better than or as well as other state-of-the-art decision procedures

Personal Agents for Implicit Culture Support

We present an implementation of a multi-agent system that aims at solving the problem of tacit knowledge transfer by means of experiences sharing. In particular, we consider experiences of use of pieces of information. Each agent incorporates a system for implicit culture support (SICS) whose goal is to realize the acceptance of the suggested information. The SICS permits a transparent (implicit) sharing of the information about the use, e.g., requesting and accepting pieces of information

Answer Set Programming based on Propositional Satisfiability

Answer set programming (ASP) emerged in the late 1990s as a new logic programming paradigm that has been successfully applied in various application domains. Also motivated by the availability of efficient solvers for propositional satisfiability (SAT), various reductions from logic programs to SAT were introduced. All these reductions, however, are limited to a subclass of logic programs or introduce new variables or may produce exponentially bigger propositional formulas. In this paper, we present a SAT-based procedure, called ASPSAT, that (1) deals with any (nondisjunctive) logic program, (2) works on a propositional formula without additional variables (except for those possibly introduced by the clause form transformation), and (3) is guaranteed to work in polynomial space. From a theoretical perspective, we prove soundness and completeness of ASPSAT. From a practical perspective, we have (1) implemented ASPSAT in Cmodels, (2) extended the basic procedures in order to incorporate the most popular SAT reasoning strategies, and (3) conducted an extensive comparative analysis involving other state-of-the-art answer set solvers. The experimental analysis shows that our solver is competitive with the other solvers we considered and that the reasoning strategies that work best on ‘small but hard’ problems are ineffective on ‘big but easy’ problems and vice versa

Interactive Planning for Space Applications

No abstract availabl

The SAT-based Approach to Separation Logic

The SAT-based approach to the decision problem for expressive, decidable, quantifier-free first-order theories has been investigated with remarkable results at least since 1993. One such theory, successfully employed in the formal verification of complex, infinite state systems, is Separation Logic (SL), which combines Boolean logic with arithmetic constraints of the form x − y ⋈ c, where ⋈ is ≤, , ≥, =, or ≠. The SAT-based approach to SL was first proposed and implemented in 1999: the results in terms of performance were good, and since then a number of other systems for SL have appeared. In this paper we focus on the problem of building efficient SAT-based decision procedures for SL. We present the basic procedure and four optimizations that improve dramatically its effectiveness in most cases: (a) IS 2 preprocessing, (b) early pruning, (c) model reduction, and (d) best reason detection. For each technique we give an example of how it might improve the performance. Furthermore, for the first three techniques, we give a pseudo-code representation and formally state the soundness and completeness of the resulting optimized procedure. We also show how it is possible to check the satisfiability of valuations involving constraints of the form x − y < c using the Bellman-Ford algorithm. Lastly, we present an extensive comparative experimental analysis, showing that our solver TSAT++, built along the lines described in this paper, is currently the state of the art on various classes of problems, including randomly generated, hand-made, and real-world instance

Causal Laws and Multi-Valued Fluents

This paper continues the line of work on representing properties of actions in nonmonotonic formalisms that stresses the distinction between being "true" and being "caused", as in the system of causal logic introduced by McCain and Turner and in the action language C proposed by Giunchiglia and Lifschitz. The only fluents directly representable in language C+ are truth-valued fluents, which is often inconvenient. We show that both causal logic and language C can be extended to allow values from arbitrary nonempty sets. Our extension of language C, called C+, also makes it possible to describe actions in terms of their attributes, which is important from the perspective of elaboration tolerance. We describe an embedding of C+ in causal theories with multi-valued constants, relate C+ to Pednault's action language ADL, and show how multi-valued constants can be eliminated in favor of Boolean constants.Comment: 7 pages, In Proceedings of Workshop on Nonmonotonic Reasoning, Action and Change (NRAC 2001

Experiments with SAT-based Answer Set Programming

Answer Set Programming (ASP) emerged in the late 1990s as a new logic programming paradigm which has been successfully applied in various application domains. Propositional satisfiability (SAT) is one of the most studied problems in Computer Science. ASP and SAT are closely related: Recent works have studied their relation, and efficient SAT-based ASP solvers (like assat and Cmodels) exist. In this paper we report about (i) the extension of the basic procedures in Cmodels in order to incorporate the most popular SAT reasoning strategies, and (ii) an extensive comparative analysis involving also other state-of-the-art answer set solvers. The experimental analysis points out, besides the fact that Cmodels is highly competitive, that the reasoning strategies that work best on “small but hard” problems are ineffective on “big but easy” problems and vice-versa

JERRY: An Interactive Planning Tool for Space Robotics

JERRY is a modular system for the interactive design, planning, control and supervision of the operation of autonomous robot systems in space. In such a highly critical environment, JERRY can effectively support the robot operators in both ordinary and emergency situations and make their work easier, safer and faster. JERRY can also provide scientists with no specific competence in robotics with a higher-level support for the automated execution of complex robot activities, with limited contributions from specialized operators