A general game player automatically learns to play arbitrary new games solely by being told their rules. For this purpose games are specified in the game description language GDL, a variant of Datalog with function symbols and a few known keywords. In its latest version GDL allows to describe nondeterministic games with any number of players who may have imperfect, asymmetric information. We analyse the epistemic structure and expressiveness of this language in terms of epistemic modal logic and present two main results: The operational semantics of GDL entails that the situation at any stage of a game can be characterised by a multi-agent epistemic (i.e., S5-) model; (2) GDL is sufficiently expressive to model any situation that can be described by a (finite) multi-agent epistemic model.

In this paper, we extend the progression semantics for first-order disjunctive logic programs and show that it coincides with the stable model semantics. Based on it, we further show how disjunctive answer set programming is related to Satisfiability Modulo Theories.

We present a new approach to characterizing the semantics for the integration of rules and first-order logic in general, and description logics in particular, based on a circumscription characterization of answer set programming, introduced earlier by Lin and Zhou. We show that both Rosati's semantics based on NM-models and Lukasiewicz's answer set semantics can be characterized by circumscription, and the difference between the two can be seen as a matter of circumscription policies. This approach leads to a number of new insights. First, we rebut a criticism on Lukasiewicz's semantics for its inability to reason for negative consequences. Second, our approach leads to a spectrum of possible semantics based on different circumscription policies, and shows a clear picture of how they are related. Finally, we show that the idea of this paper can be applied to first-order general stable models.

We introduce aProbLog, a generalization of the probabilistic logic programming language ProbLog. An aProbLog program consists of a set of definite clauses and a set of algebraic facts; each such fact is labeled with an element of a semiring. A wide variety of labels is possible, ranging from probability values to reals (representing costs or utilities), polynomials, Boolean functions or data structures. The semiring is then used to calculate labels of possible worlds and of queries. We formally define the semantics of aProbLog and study the aProbLog inference problem, which is concerned with computing the label of a query. Two conditions are introduced that allow one to simplify the inference problem, resulting in four different algorithms and settings. Representative basic problems for each of these four settings are: is there a possible world where a query is true (SAT), how many such possible worlds are there (#SAT), what is the probability of a query being true (PROB), and what is the most likely world where the query is true (MPE). We further illustrate these settings with a number of tasks requiring more complex semirings.

Cluedo) serves as a fun when it comes to designing an optimal (or even practicable) focus problem for this introduction to propositional knowledge solution. The potential solutions also touch on many representation and reasoning. After covering fundamentals areas of AI, so the students can be creative in applying and of propositional logic, students first solve basic synthesizing what they've learned to a new problem. The logic problems with and without the aid of a satisfiability three challenges give the students the opportunity to choose solver (e.g.

We present a computational method to generate explanations to answers of complex queries over biomedical ontologies and databases, using the high-level representation and efficient automated reasoners of Answer Set Programming. We show the applicability of our approach with some queries related to drug discovery over PHARMGKB, DRUGBANK, BIOGRID, CTD and SIDER.

Drescher and Walsh, to satisfiability modulo theories, the key idea is to incorporate 2010). Then, constraint answer sets of the resulting program theory-specific predicates into propositional formulas, can be characterized via Boolean assignments over and extending an ASP solver's decision engine for a atom(Π) body(Π) that do not violate a set of nogoods more high-level proof procedure. A promising approach to imposed by Π. Formally, a Boolean assignment A is a sequence constraint answer set programming (CASP) has been presented (σ

Medical treatment decision making is a good application of knowledge representation and reasoning. We are particularly interested in using it to resolve treatment conflicts, a complicated condition when two treatments cannot be given simultaneously to a patient of multiple symptoms. The logic system is required to reason on cases with and without treatment conflicts. Thanks to the nonmonotonicity of Answer Set Programming (ASP), we elegantly automate medical treatment conflict resolving on an example problem and show the importance of nonmonotonicity in medical reasoning.

Spread of infections (diseases, ideas, etc.) in a network can be modeled as the evolution of states of nodes in a graph as a function of the states of their neighbors. Given an initial configuration of a network in which a subset of the nodes have been infected, and an infection propagation function that specifies how the states of the nodes evolve over time, we show how to use model checking to identify, verify, and evaluate the effectiveness of intervention policies for containing the propagation of infection over such networks.

Recently the general Game Description Language (GDL) has been extended so as to cover arbitrary games with incomplete/imperfect information. Learning — without human intervention — to play such games poses a reasoning challenge for general game-playing systems that is much more intricate than in case of complete information games. Action formalisms like the Situation Calculus have been developed for precisely this purpose. In this paper we present a full embedding of the Game Description Language into the Situation Calculus (with Scherl and Levesque's knowledge fluent ). We formally prove that this provides a sound and complete reasoning method for players' knowledge about game states as well as about the knowledge of the other players.