In this work we propose an approach for generalization in continuous domain Reinforcement Learning that, instead of using a single function approximator, tries many different function approximators in parallel, each one defined in a different region of the domain. Associated with each approximator is a relevance function that locally quantifies the quality of its approximation, so that, at each input point, the approximator with highest relevance can be selected. The relevance function is defined using parametric estimations of the variance of the q-values and the density of samples in the input space, which are used to quantify the accuracy and the confidence in the approximation, respectively. These parametric estimations are obtained from a probability density distribution represented as a Gaussian Mixture Model embedded in the input-output space of each approximator. In our experiments, the proposed approach required a lesser number of experiences for learning and produced more stable convergence profiles than when using a single function approximator.

The generation of route descriptions is a fundamental task of navigation systems. A particular problem in this context is to identify routes that can easily be described and processed by users. In this work, we present a framework for representing route networks with the qualitative information necessary to evaluate and optimize route descriptions with regard to ambiguities in them. We identify different agent models that differ in how agents are assumed to process route descriptions while navigating through route networks. Further, we analyze the computational complexity of matching route descriptions and paths in route networks in dependency of the agent model. Finally we empirically evaluate the influence of the agent model on the optimization and the processing of route instructions.

Carneades is a recently proposed formalism for structured argumentation with varying proof standards. An open question is its relation with Dung's seminal abstract approach to argumentation. In this paper the two formalisms are formally related by translating Carneades into ASPIC+, another recently proposed formalism for structured argumentation. Since ASPIC+ is defined to generate Dung-style abstract argumentation frameworks, this in effect translates Carneades graphs into abstract argumentation frameworks. It is proven that Carneades always induces a unique Dung extension, which is the same in all of Dung's semantics.

The Game Description Language is a high-level, rule-based formalisms for communicating the rules of arbitrary  games to general game-playing systems, whose challenging task is to learn to play previously unknown games without human intervention. Originally designed for deterministic games with complete information about the game state, the language was recently extended to include randomness and imperfect information. However, determining the extent to which this enhancement allows to describe truly arbitrary games was left as an open problem. We provide a positive answer to this question by relating the extended Game Description Language to the universal, mathematical concept of extensive-form games, proving that indeed just any such game can be described faithfully.

Consequence-based ontology reasoning procedures have so far been known only for Horn ontology languages. A difficulty in extending such procedures is that non-Horn axioms seem to require reasoning by case, which causes non-determinism in tableau-based procedures. In this paper we present a consequence-based procedure for ALCH that overcomes this difficulty by using rules similar to ordered resolution to deal with disjunctive axioms in a deterministic way; it retains all the favourable attributes of existing consequence-based procedures, such as goal-directed “one pass” classification, optimal worst-case complexity, and “pay-asyou- go” behaviour. Our preliminary empirical evaluation suggests that the procedure scales well to non-Horn ontologies.

The paper introduces a logical framework for negotiation among dishonest agents. The framework relies on the use of abductive logic programming as a knowledge representation language for agents to deal with incomplete information and preferences. The paper shows how intentionally false or inaccurate information of agents could be encoded in the agents' knowledge bases. Such disinformation can be effectively used in the process of negotiation to have desired outcomes by agents. The negotiation processes are formulated under the answer set semantics of abductive logic programming and enable the exploration of various strategies that agents can employ in their negotiation

This paper studies a computational logic for dishonest reasoning. We introduce logic programs with disinformation to represent and reason with dishonesty. We then consider two different cases of dishonesty: deductive dishonesty and abductive dishonesty. The former misleads another agent to deduce wrong conclusions, while the latter interrupts another agent to abduce correct explanations. In deductive or abductive dishonesty, an agent can perform different types of dishonest reasoning such as lying, bullshitting, and withholding information. We show that these different types of dishonest reasoning are characterized by extended abduction, and address their computational methods using abductive logic programming.

We study the problem of dealing with inconsistency in Description Logic (DL) ontologies. We consider inconsistency-tolerant semantics recently proposed in the literature, called AR-semantics and CAR-semantics, which are based on repairing (i.e., modifying) in a minimal way the extensional knowledge (ABox) while keeping the intensional knowledge (TBox) untouched. We study instance checking and conjunctive query entailment under the above inconsistency-tolerant semantics for a wide spectrum of DLs, ranging from tractable ones (EL) to very expressive ones (SHIQ), showing that reasoning under the above semantics is inherently intractable, even for very simple DLs. To the aim of overcoming such a high computational complexity of reasoning, we study sound approximations of the above semantics. Surprisingly, our computational analysis shows that reasoning under the approximated semantics is intractable even for tractable DLs. Finally, we identify suitable language restrictions of such DLs allowing for tractable reasoning under inconsistency-tolerant semantics.

As a doxastic counterpart to epistemic logic based on S5 we study the modal logic KSD that can be viewed as an approach to modelling a kind of objective and fair belief. We apply KSD to the problem of minimal belief and develop an alterna- tive approach to nonmonotonic modal logic using a weaker concept of expansion. This corresponds to a certain minimal kind of KSD model and yields a new type of nonmonotonic doxastic reasonin

As more and more application areas require higher scalability, the study of fragments of expressive The high computational complexity of the expressive DLs with better computational properties has become an Description Logics (DLs) that underlie the important area of research. OWL standard has motivated the study of their Horn fragments of DLs, which are obtained by restricting Horn fragments, which are usually tractable in data the syntax of a DL in such a way that disjunction is not complexity and can also have lower combined complexity, expressible, were first considered in [Hustadt et al., 2005] particularly for query answering. In this paper as expressive fragments with tractable data complexity (see we provide algorithms for answering conjunctive also [Krötzsch et al., 2007]). It was later identified that they 2-way regular path queries (2CRPQs), a nontrivial can also exhibit lower combined complexity when it comes generalization of plain conjunctive queries, to query answering. Indeed, answering conjunctive queries in the Horn fragments of the DLs SHOIQ and (CQs), a kind of database-inspired queries that have become SROIQ underlying OWL 1 and OWL 2. We show the standard for querying DLs, is in ExpTime for the Horn that the combined complexity of the problem is ExpTime-complete fragment of the prominent SHIQ [Eiter et al., 2008], while it for Horn-SHOIQ and 2ExpTimecomplete is already 2ExpTime-hard for quite restricted (non Horn) fragments for the more expressive Horn-SROIQ, of SHIQ, like ALCI [Lutz, 2008] and SH [Eiter et but is PTime-complete in data complexity for both.