RCC8 is a well-known constraint language for expressing and reasoning about spatial knowledge. We state a simple and concise Horn theory for RCC8 analogous to the ORD-Horn theory for temporal reasoning. This theory allows for expressing RCC8 and retains tractability of the well-known Horn reduct of RCC8. Further, it is much more adequate for practical purposes in the area of logic programming and surpasses previous attempts.

This paper introduces MARCO, a hybrid, chess playing agent equipped with a custom-built robotic arm and an emotionally expressive, virtual face presented on a small, servo-controlled display. MARCO was built to investigate the hypothesis that hybrid systems capable of displaying emotions make playing chess more personal and enjoyable. In addition, it is our aim to realize emotional contagion between man and machine in that the agent has the power to influence the human player on an emotional level and vice versa. The hardware components consist of eight Dynamixel servos, an Arduino-based control board, a 5.6 inch display, and a DGT chessboard. The software components run concurrently as separate processes. The main components are the virtual agent framework MARC, the WASABI Affect Simulation architecture, and the TSCP chess engine.

We present MARCO, a hybrid, chess playing agent equipped with a custom-built robotic arm and a virtual agent's face displaying emotions. MARCO was built to investigate the hypothesis that hybrid agents capable of displaying emotions make playing chess more personal and enjoyable. In addition, we aim to explore means of achieving emotional contagion between man and machine.

Several studies in Qualitative Spatial and Temporal Reasoningdiscuss translations of the satisfiability problem on qualitativeconstraint languages into propositional SAT. Most of these encodings focus on compactness, while propagation strength is seldom discussed. In this work, we focus on temporal reasoning with the Point Algebra and Allen's Interval Algebra. We understand all encodings as a combination of propagation andsearch. We first give a systematic analysis of existing propagation approachesfor these constraint languages. They are studied and ordered with respect to their propagation strengthand refutation completeness for classes of input instances. Secondly, we discuss how existing encodings can be derived fromsuch propagation approaches. We conclude our work with an empirical evaluation which shows that theolder ORD-encoding by Nebel and Bürckert performs better than more recently suggested encodings.

Qualitative Spatial and Temporal Reasoning is a central topic in Artificial Intelligence. In particular, it is aimed at application scenarios dealing with uncertain information and thus needs to be able to handle dynamic beliefs. This makes merging and revision of qualitative information important topics. While merging has been studied extensively, revision which describes what is happening when one learns new information about a static world has been overlooked. In this paper, we propose to fill the gap by providing two revision operations for qualitative calculi. In order to implement these operations, we give algorithms for revision and analyze the computational complexity of these problems. Finally, we present an implementation of these algorithms based on a qualitative constraint solver and provide an experimental evaluation.

Qualitative spatial and temporal reasoning is a research field that studies relational, constraint-based formalisms for representing, and reasoning about, spatial and temporal information. The standard approach for checking consistency is based on an exhaustive representation of possible configurations between three entities, the so-called composition tables. These tables, however, encode semantic background knowledge in a redundant way, which becomes a size and efficiency issue, when the composition table needs to be grounded as done in SAT encodings of problem instances. % In this paper, we present a new framework that allows for decomposing composition tables into logically simpler parts, while preserving logical equivalence, e.g., the decomposition in start- and end-points for Allen's Interval Calculus. We show that finding such decompositions is an NP-complete problem and present a SAT-based method to generate decompositions. Finally, we discuss the impact of our decomposition method on SAT encodings of problem instances, and present a reasoning system built on decompositions that compares favorably with state-of-the-art solvers.

Possibilistic logic is a well-known framework for dealing with uncertainty and reasoning under inconsistent knowledge bases. Standard possibilistic logic expressions are propositional logic formulas associated with positive real degrees belonging to [0,1]. However, in practice it may be difficult for an expert to provide exact degrees associated with formulas of a knowledge base. This paper proposes a flexible representation of uncertain information where the weights associated with formulas are in the form of intervals. We first study a framework for reasoning with interval-based possibilistic knowledge bases by extending main concepts of possibilistic logic such as the ones of necessity and possibility measures. We then provide a characterization of an interval-based possibilistic logic base by means of a concept of compatible standard possibilistic logic bases. We show that intervalbased possibilistic logic extends possibilistic logic in the case where all intervals are singletons. Lastly, we provide computational complexity results of deriving plausible conclusions from interval-based possibilistic bases and we show that the flexibility in representing uncertain information is handled without extra computational costs.

Merging multiple sources of information is a rising subject in artificial intelligence. Most of the proposals are model-based approaches with very high computational complexity, moreover few experimentations are available. This paper proposes a framework for performing Removed Sets Fusion (RSF) of belief bases consisting of prepositional formulae. It then describes the implementation of RSF which stems from Answer Set Programming (ASP) and can be performed with any ASP solver supporting the minimize statement. It finally presents an experimental study and a comparison.