This paper develops an inductive theory of predictive common sense reasoning. The theory provides the basis for an integrated solution to the three traditional problems of reasoning about change; the frame, qualification, and ramification problems. The theory is also capable of representing non-deterministic events, and it provides a means for stating defeasible preferences over the outcomes of conflicting simultaneous events.

Exact Max-SAT solvers, compared with SAT solvers, apply little inference at each node of the proof tree. Commonly used SAT inference rules like unit propagation produce a simplified formula that preserves satisfiability but, unfortunately, solving the Max-SAT problem for the simplified formula is not equivalent to solving it for the original formula. In this paper, we define a number of original inference rules that, besides being applied efficiently, transform Max-SAT instances into equivalent Max-SAT instances which are easier to solve. The soundness of the rules, that can be seen as refinements of unit resolution adapted to Max-SAT, are proved in a novel and simple way via an integer programming transformation. With the aim of finding out how powerful the inference rules are in practice, we have developed a new Max-SAT solver, called MaxSatz, which incorporates those rules, and performed an experimental investigation. The results provide empirical evidence that MaxSatz is very competitive, at least, on random Max-2SAT, random Max-3SAT, Max-Cut, and Graph 3-coloring instances, as well as on the benchmarks from the Max-SAT Evaluation 2006.

It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN.

Linear Temporal Logic (LTL) is widely used for defining conditions on the execution paths of dynamic systems. In the case of dynamic systems that allow for nondeterministic evolutions, one has to specify, along with an LTL formula f, which are the paths that are required to satisfy the formula. Two extreme cases are the universal interpretation A.f, which requires that the formula be satisfied for all execution paths, and the existential interpretation E.f, which requires that the formula be satisfied for some execution path. When LTL is applied to the definition of goals in planning problems on nondeterministic domains, these two extreme cases are too restrictive. It is often impossible to develop plans that achieve the goal in all the nondeterministic evolutions of a system, and it is too weak to require that the goal is satisfied by some execution. In this paper we explore alternative interpretations of an LTL formula that are between these extreme cases. We define a new language that permits an arbitrary combination of the A and E quantifiers, thus allowing, for instance, to require that each finite execution can be extended to an execution satisfying an LTL formula (AE.f), or that there is some finite execution whose extensions all satisfy an LTL formula (EA.f). We show that only eight of these combinations of path quantifiers are relevant, corresponding to an alternation of the quantifiers of length one (A and E), two (AE and EA), three (AEA and EAE), and infinity ((AE)* and (EA)*). We also present a planning algorithm for the new language that is based on an automata-theoretic approach, and study its complexity.

The Internet contains a very large number of information sources providing many types of data from weather forecasts to travel deals and financial information. These sources can be accessed via Web-forms, Web Services, RSS feeds and so on. In order to make automated use of these sources, we need to model them semantically, but writing semantic descriptions for Web Services is both tedious and error prone. In this paper we investigate the problem of automatically generating such models. We introduce a framework for learning Datalog definitions of Web sources. In order to learn these definitions, our system actively invokes the sources and compares the data they produce with that of known sources of information. It then performs an inductive logic search through the space of plausible source definitions in order to learn the best possible semantic model for each new source. In this paper we perform an empirical evaluation of the system using real-world Web sources. The evaluation demonstrates the effectiveness of the approach, showing that we can automatically learn complex models for real sources in reasonable time. We also compare our system with a complex schema matching system, showing that our approach can handle the kinds of problems tackled by the latter.

In this paper we present efficient evaluation algorithms for the Horn Transaction Logic (a generalization of the regular Horn logic programs with state updates). We present two complementary methods for optimizing the implementation of Transaction Logic. The first method is based on tabling and we modified the proof theory to table calls and answers on states (practically, equivalent to dynamic programming). The call-answer table is indexed on the call and a signature of the state in which the call was made. The answer columns contain the answer unification and a signature of the state after the call was executed. The states are signed efficiently using a technique based on tries and counting. The second method is based on incremental evaluation and it applies when the data oracle contains derived relations. The deletions and insertions (executed in the transaction oracle) change the state of the database. Using the heuristic of inertia (only a part of the state changes in response to elementary updates), most of the time it is cheaper to compute only the changes in the state than to recompute the entire state from scratch. The two methods are complementary by the fact that the first method optimizes the evaluation when a call is repeated in the same state, and the second method optimizes the evaluation of a new state when a call-state pair is not found by the tabling mechanism (i.e. the first method). The proof theory of Transaction Logic with the application of tabling and incremental evaluation is sound and complete with respect to its model theory.

This article presents a technique for proving problems hard for classes of the polynomial hierarchy or for PSPACE. The rationale of this technique is that some problem restrictions are able to simulate existential or universal quantifiers. If this is the case, reductions from Quantified Boolean Formulae (QBF) to these restrictions can be transformed into reductions from QBFs having one more quantifier in the front. This means that a proof of hardness of a problem at level n in the polynomial hierarchy can be split into n separate proofs, which may be simpler than a proof directly showing a reduction from a class of QBFs to the considered problem.

In this paper, we present two alternative approaches to defining answer sets for logic programs with arbitrary types of abstract constraint atoms (c-atoms). These approaches generalize the fixpoint-based and the level mapping based answer set semantics of normal logic programs to the case of logic programs with arbitrary types of c-atoms. The results are four different answer set definitions which are equivalent when applied to normal logic programs. The standard fixpoint-based semantics of logic programs is generalized in two directions, called answer set by reduct and answer set by complement. These definitions, which differ from each other in the treatment of negation-as-failure (naf) atoms, make use of an immediate consequence operator to perform answer set checking, whose definition relies on the notion of conditional satisfaction of c-atoms w.r.t. a pair of interpretations. The other two definitions, called strongly and weakly well-supported models, are generalizations of the notion of well-supported models of normal logic programs to the case of programs with c-atoms. As for the case of fixpoint-based semantics, the difference between these two definitions is rooted in the treatment of naf atoms. We prove that answer sets by reduct (resp. by complement) are equivalent to weakly (resp. strongly) well-supported models of a program, thus generalizing the theorem on the correspondence between stable models and well-supported models of a normal logic program to the class of programs with c-atoms. We show that the newly defined semantics coincide with previously introduced semantics for logic programs with monotone c-atoms, and they extend the original answer set semantics of normal logic programs. We also study some properties of answer sets of programs with c-atoms, and relate our definitions to several semantics for logic programs with aggregates presented in the literature.

This is to present work on modifying the Aleph ILP system so that it evaluates the hypothesised clauses in parallel by distributing the data-set among the nodes of a parallel or distributed machine. The paper briefly discusses MPI, the interface used to access messagepassing libraries for parallel computers and clusters. It then proceeds to describe an extension of YAP Prolog with an MPI interface and an implementation of data-parallel clause evaluation for Aleph through this interface. The paper concludes by testing the data-parallel Aleph on artificially constructed data-sets. Inductive Logic Programming (ILP) is the Machine Learing discipline that deals with the induction of Logic Programmes from examples.

In this article, we study translations between variants of defaults logics such that the extensions of the theories that are the input and the output of the translation are in a bijective correspondence. We assume that a translation can introduce new variables and that the result of translating a theory can either be produced in time polynomial in the size of the theory or its output is polynomial in that size; we however restrict to the case in which the original theory has extensions. This study fills a gap between two previous pieces of work, one studying bijective translations among restrictions of default logics, and the other one studying non-bijective translations between default logics variants.