Automatically deriving properties of new games is one of the fundamental challenges for general game-playing systems, whose task is to learn to play any previously unknown game solely by being given the rules of that game. A recently developed method uses Answer Set Programming for verifying finitely-bounded temporal invariance properties against a given game description by structural induction. Addressing the new challenge posed by the recent extension of the general Game Description Language to include games with imperfect information and randomness, we extend this method to epistemic properties about games. We formally prove this extension to be correct, and we report on experiments that show its practical applicability.

Only-knowing was originally introduced by Levesque to capture the beliefs of an agent in the sense that its knowledge base is all the agent knows. When a knowledge base contains defaults Levesque also showed an exact correspondence between only-knowing and autoepistemic logic. Later these results were extended by Lakemeyer and Levesque to also capture a variant of autoepistemic logic proposed by Konolige and Reiter's default logic. One of the benefits of such an approach is that various nonmonotonic formalisms can be compared within a single monotonic logic leading, among other things, to the first axiom system for default logic. In this paper, we will bring another large class of nonmonotonic systems, which were first studied by McDermott and Doyle, into the only-knowing fold. Among other things, we will provide the first possible-world semantics for such systems, providing a new perspective on the nature of modal approaches to nonmonotonic reasoning.

In this paper we introduce an abstract theory of normative reasoning, whose central notion is the generation of obligations, permissions and institutional facts from conditional norms. We present various semantics and their proof systems. The theory can be used to classify and compare new candidates for standards of normative reasoning, and to explore more elaborate forms of normative reasoning than studied thus far.

Possibilistic logic is a well-known logic for reasoning under uncertainty, which is based on the idea that the epistemic state of an agent can be modeled by assigning to each possible world a degree of possibility, taken from a totally ordered, but essentially qualitative scale. Recently, a generalization has been proposed that extends possibilistic logic to a meta-epistemic logic, endowing it with the capability of reasoning about epistemic states, rather than merely constraining them. In this paper, we further develop this generalized possibilistic logic (GPL). We introduce an axiomatization showing that GPL is a fragment of a graded version of the modal logic KD, and we prove soundness and completeness w.r.t. a semantics in terms of possibility distributions. Next, we reveal a close link between the well-known stable model semantics for logic programming and the notion of minimally specific models in GPL. More generally, we analyze the relationship between the equilibrium logic of Pearce and GPL, showing that GPL can essentially be seen as a generalization of equilibrium logic, although its notion of minimal specificity is slightly more demanding than the notion of minimality underlying equilibrium logic.

In this paper, we propose a semantic forgetting for arbitrary logic programs(or propositional theories) under answer set semantics,called HT-forgetting. The HT-forgetting preserves strong equivalence in the sense that strongly equivalent logic programs will remain strongly equivalent after forgetting the same set of atoms. The result of an HT-forgetting is always expressible by a logic program, and in particular, the result of an HT-forgetting in a Horn program is expressible in a Horn program; and a representation theorem shows that HT-forgetting can be precisely characterized by Zhang-Zhou's four forgetting postulates under the logic of here-and-there. We also reveal underlying connections between HT-forgetting and classical forgetting, and provide complexity results for decision problems.

We present a system capable of automatically solving combinatorial logic puzzles given in (simplified) English. It uses an ontology to represent the puzzles in ASP which is applicable to a large set of logic puzzles. To translate the English descriptions of the puzzles into this ontology, we use a lambda-calculus based approach using Probabilistic Combinatorial Categorial Grammars (PCCG) where the meanings of words are associated with parameters to be able to distinguish between multiple meanings of the same word.

The paper introduces a worst-case optimal tableau algorithm for reasoning with Forest Logic Programs, a decidable fragment of Open Answer Set Programming. FoLPs are a useful device for tight integration of the Description Logic and the Logic Programming worlds: reasoning with the DL SHOQ can be simulated within the fragment. The algorithm reuses a knowledge compilation technique previously introduced, but improves on previous results by decreasing the worst-case running time with one exponential level. The decrease in complexity is due to the usage in conjunction of a new redundancy and of a new caching rule.

Answer Set Programming (ASP) is a fully-declarative logic programming paradigm, which has been proposed in the area of knowledge representation and non-monotonic reasoning. Nowadays, the formal properties of ASP are well-understood, efficient ASP systems are available, and, recently, ASP has been employed in a few industrial applications. However, ASP technology is not mature for a successful exploitation in industry yet; mainly because ASP technologies are not integrated in the well-assessed development processes and platforms which are tailored for imperative/object-oriented programming languages. In this paper we present a new programming framework blending ASP with Java. The framework is based on JASP, an hybrid language that transparently supports a bilateral interaction between ASP and Java. JASP specifications are compliant with the JPA standard to perfectly fit extensively-adopted enterprise application technologies. The framework also encompasses an implementation of JASP as a plug-in for the Eclipse platform, called JDLV, which includes a compiler from JASP to Java. Moreover, we show a real-world application developed with JASP and JDLV, which highlights the effectiveness of our ASP–Java integration framework.

In software testing, the small-scope hypothesis states that a high proportion of errors can be found by testing a program for all test inputs within some small scope. In this paper, we evaluate the small-scope hypothesis for answer-set programming (ASP). To this end, we follow work in traditional testing and base our evaluation on mutation analysis. In fact, we show that a rather limited scope is sufficient for testing ASP encodings from a representative set of benchmark problems. Our experimental evaluation facilitates effective methods for testing in ASP. Also, it gives some justification to analyse programs at the propositional level after grounding them over a small domain.

In this paper we encode some of the reasoning methods used in frame based knowledge representation languages in answer set programming (ASP). In particular, we show how ``cloning'' and ``unification'' in frame based systems can be encoded in ASP. We then show how some of the types of queries with respect to a biological knowledge base can be encoded using our methodology. We also provide insight on how the reasoning can be done more efficiently when dealing with a huge knowledge base.