Abstract This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.

The paper proposes a framework for capturing how an agent’s beliefs evolve over time in response to observations and for answering the question of whether statements made by a third party can be believed. The basic components of the framework are a formalism for reasoning about actions, changes, and observations and a formalism for default reasoning. The paper describes a concrete implementation that leverages answer set programming for determining the evolution of an agent's ``belief state'', based on observations, knowledge about the effects of actions, and a theory about how these influence an agent's beliefs. The beliefs are then used to assess whether statements made by a third party can be accepted as truthful. The paper investigates an application of the proposed framework in the detection of man-in-the-middle attacks targeting computers and cyber-physical systems. Finally, we briefly discuss related work and possible extensions.

Abstract In this article, we present a new version of the language of Epistemic Specifications. The goal is to simplify and improve the intuitive and formal semantics of the language. We describe an algorithm for computing solutions of programs written in this new version of the language. The new semantics is illustrated by a number of examples, including an Epistemic Specifications-based framework for conformant planning. In addition, we introduce the notion of an epistemic logic program with sorts . This extends recent efforts to define a logic programming language that includes the means for explicitly specifying the domains of predicate parameters. An algorithm and its implementation as a solver for epistemic logic programs with sorts is also discussed.

This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and non-deterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, with a fine-resolution transition diagram defined as a refinement of a coarse-resolution transition diagram of the domain. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. A probabilistic representation of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution system description, and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.

Abstract The paper introduces a new modular action language, ${\mathcal ALM}$ , and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993,Journal of Logic Programming 17, 2–4, 301–321; 1998,Electronic Transactions on AI 3, 16, 193–210) in which a high-level action language is used as a front end for a logic programming system description. The resulting logic programming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that requirestructuring of knowledge. $\mathcal{ALM}$ is meant to remedy this problem. Structuring of knowledge in ${\mathcal ALM}$ is supported by the concepts ofmodule(a formal description of a specific piece of knowledge packaged as a unit),module hierarchy, andlibrary, and by the division of a system description of ${\mathcal ALM}$ into two parts:theoryandstructure. Atheoryconsists of one or more modules with a common theme, possibly organized into a module hierarchy based on adependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them.Structuresare used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries.

Abstract The paper presents a knowledge representation language $\mathcal{A}log$ which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of $\mathcal{A}log$ , an algorithm for computing its answer sets, and comparison with other approaches.

Following the initiative in 2010 taken by the Association for Logic Programming and Cambridge University Press, the full papers accepted for the International Conference on Logic Programming again appear as a special issue of Theory and Practice of Logic Programming (TPLP)—the 27th International Conference on Logic Programming Special Issue. Papers describing original, previously unpublished research and not simultaneously submitted for publication elsewhere were solicited in all areas of logic programming including but not restricted to: Theory: Semantic Foundations, Formalisms, Non- monotonic Reasoning, Knowledge Representation. Implementation: Compilation, Memory Management, Virtual Machines, Parallelism. Environments: Program Analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing. Language Issues: Concurrency, Objects, Coordination, Mobility, Higher Order, Types, Modes, Assertions, Programming Techniques. Related Paradigms: Abductive Logic Programming, Inductive Logic Programming, Constraint Logic Programming, Answer-Set Programming. Applications: Databases, Data Integration and Federation, Software Engineering, Natural Language Processing, Web and Semantic Web, Agents, Artificial Intelligence, Bioinformatics.

Abstract This paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for knowledge representation and updating of knowledge. We argue that our approach to updates is more appealing than existing approaches. We give sufficiency conditions for the coherency of P-log programs and show that Bayes nets can be easily mapped to coherent P-log programs.

In this paper, we suggest an architecture for a software agent which operates a physical device and is capable of making observations and of testing and repairing the device's components. We present simplified definitions of the notions of symptom, candidate diagnosis, and diagnosis which are based on the theory of action language . The definitions allow one to give a simple account of the agent's behavior in which many of the agent's tasks are reduced to computing stable models of logic programs.

This paper is an attempt at mathematical investigation of software development process in the context of declarative logic programming. We introduce notions of specification and specification constructor which are developed from natural language description of a problem. Generalizations of logic programs, called lp-functions are introduced to represent these specifications. We argue that the process of constructing lp-function representing a specification S should be supported by certain types of mathematical results which we call representation theorems. We present two such theorems to illustrate the idea.

Journal Article Editorial Get access M. GELFOND M. GELFOND Executive Editor Search for other works by this author on: Oxford Academic Google Scholar Journal of Logic and Computation, Volume 2, Issue 1, March 1992, Pages 1–4, https://doi.org/10.1093/logcom/2.1.1 Published: 01 March 1992

The purpose of this article is to compare three types of nonmonotonic semantics: (a) proof-theoretic semantics based on the closed world assumption, (b) model-theoretic semantics based on the notion of a minimal model, and (c) model-theoretic semantics based on the notion of a minimal Herbrand model. All of these semantics capture the nonmonotonicity of commonsense reasoning, that is, the ability to withdraw conclusions after some new information is added to the original theories, and proved to be powerful enough to handle most examples of such reasoning presented in the literature. However, since these formalizations are based on different intuitions and often produce different results, the problem of understanding the relationship between them is especially important. In the first part of the article we concentrate on the class of positive logic programs, also called definite theories. Although the three semantics usually differ for universal sentences, our main result shows that they always coincide for existential queries. This result is particularly significant in view of the fact that in many applications existential queries are of main interest. It also plays an important role in the problem of finding a suitable declarative semantics for logic programs. In the second part we investigate arbitrary universal theories and we show that subtle differences exist between the three approaches and therefore no straightforward generalization of the results from the first part can be obtained.

The purpose of this paper is to investigate the relationship between two approaches to the formalization of non-monotonic reasoning - J. McCarthy's approach based on the notion of Circumscription [Mc] and the Autoepistemic Logic approach of R. Moore [Mr]. Since these two approaches differ considerably in scope, we will limit our attention to the situation where some common ground can be found. Namely, we consider only the propositional case of parallel circumpsription, when all predicates of a formula T are circumscribed simultaneously.