The aim of this work is to propose a logical framework for the specification of cognitive emotions that are based on counterfactual reasoning about agents’ choices. An example of this kind of emotions is regret. In order to meet this objective, we exploit the well-known STIT logic [Belnap et al., 2001; Horty, 2001]. STIT logic has been proposed in the domain of formal philosophy in the nineties and, more recently, it has been imported into the field of theoretical computer science where its formal relationships with other logics for multiagent systems such as ATL and Coalition Logic (CL) have been studied. STIT is a very suitable formalism to reason about choices and capabilities of agents and groups of agents. Unfortunately, the version of STIT with agents and groups has been recently proved to be undecidable. In this work we study a decidable fragment of STIT with agents and groups which is sufficiently expressive for our purpose of formalizing counterfactual emotions.

In this paper we present an approach to the task of generating and resolving referring expressions (REs) for conversational mobile robots. It is based on a spatial knowledge base encompassing both robot-and human-centric representations. Existing algorithms for the generation of referring expressions (GRE) try to find a description that uniquely identifies the referent with respect to other entities that are in the current context. Mobile robots, however, act in large-scale space, that is environments that are larger than what can be perceived at a glance, e.g. an office building with different floors, each containing several rooms and objects. One challenge when referring to elsewhere is thus to include enough information so that the interlocutors can extend their context appropriately. We address Figure 1: Situated dialogue with a campus service robot this challenge with a method for context construction 2. "the area" that can be used for both generating and resolving 3. "Peter's office at the end of the corridor on the third floor REs - two previously disjoint aspects. Our approach of the Acme Corp. building 7 in the Acme Corp. complex, is embedded in a bidirectional framework 47 Evergreen Terrace, Calisota, Earth, (...)" for natural language processing for robots. Clearly, these REs are valid descriptions of the respective entities in the robot's world representation.

Systematic search and local search paradigms for combinatorial problems are generally believed to have complementary strengths. Nevertheless, attempts to combine the power of the two paradigms have had limited success, due in part to the expensive information communication overhead involved. We propose a hybrid strategy based on shared memory, ideally suited for multi-core processor architectures. This method enables continuous information exchange between two solvers without slowing down either of the two. Such a hybrid search strategy is surprisingly effective, leading to substantially better quality solutions to many challenging Maximum Satisfiability (MaxSAT) instances than what the current best exact or heuristic methods yield, and it often achieves this within seconds. This hybrid approach is naturally best suited to MaxSAT instances for which proving unsatisfiability is already hard; otherwise the method falls back to pure local search.

Recent work on Alternating-Time Temporal Logic and Coalition Logic has allowed the expression of many interesting properties of coalitions and strategies. However there is no natural way of expressing resource requirements in these logics. This paper presents a Resource-Bounded Coalition Logic (RBCL) which has explicit representation of resource bounds in the language, and gives a complete and sound axiomatisation of RBCL.

Least Common Subsumers (LCS) have been proposed in Description Logics (DL) to capture the commonalities between two or more concepts. Since its introduction in 1992,  LCS have been successfully employed  as a logical tool for a variety of applications, spanning from inductive learning, to bottom-up construction of knowledge bases, information retrieval,  to name a few. The best known algorithm for computing LCS uses structural comparison on normal forms, and the most expressive DL it is applied to is ALEN. We provide a general tableau-based calculus for computing LCS, via substitutions on concept terms containing concept variables. We show the applicability of our method to an expressive DL (but without disjunction and full negation), discuss complexity issues, and show the generality of our proposal.

Optimization in graphical models is an important problem which has been studied in many AI frameworks such as weighted CSP, maximum satisfiability or probabilistic networks. By identifying conditionally independent subproblems, which are solved independently and whose optimum is cached, recent Branch and Bound algorithms offer better asymptotic time complexity. But the locality of bounds induced by decomposition often hampers the practical effects of this result because subproblems are often uselessly solved to optimality. Following the Russian Doll Search (RDS) algorithm, a possible approach to overcome this weakness is to (inductively) solve a relaxation of each subproblem to strengthen bounds. The algorithm obtained generalizes both RDS and tree-decomposition based algorithms such as BTD or AND-OR Branch and Bound. We study its efficiency on different problems, closing a very hard frequency assignment instance which has been open for more than 10 years.

Almost all approaches to model-based diagnosis presume that the system being diagnosed behaves non-intermittently and analyze behavior over a small number (often only one) of time instants.  In this paper we show how existing approaches to model-based diagnosis can be extended to diagnose intermittent failures as they manifest themselves over time. In addition, we show where to insert probe points to best distinguish among the intermittent faults those that best explain the symptoms and isolate the fault in minimum expected cost.

P. This compilation, however, is linear in the number of possible initial states that is exponential in the number of fluents. The problem of planning in the presence of sensing We show nonetheless that even in such cases, a sound, has been addressed in recent years as a nondeterministic complete, and polynomial translation X(P) is possible, provided search problem in belief space. In this that the problem P has bounded contingent width, and work, we use ideas advanced recently for compiling show that the contingent width of almost all existing benchmarks conformant problems into classical ones for introducing is 1; a result that parallels the one reported by Palacios a different approach where contingent problems and Geffner for conformant planning. We then show how the P are mapped into non-deterministic problems non-deterministic but fully observable problem X(P) can be X(P) in state space.

We determine the implicit assumptions and the structure of the Single and Double Cross Calculi within Euclidean geometry, and use these results to guide the construction of analogous calculi in mereogeometry.  The systems thus obtained have strong semantic and deductive similarities with the Euclidean-based Cross Calculi although they rely on a different geometry. This fact suggests that putting too much emphasis on usual classification of qualitative spaces may hide important commonalities among spaces living in different classes.

Conflict driven clause learning, one of the most important component of modern SAT solvers, is also recognized as very important in parallel SAT solving. Indeed, it allows clause sharing between multiple processing units working on related (sub-)problems. However, without limitation, sharing clauses might lead to an exponential blow up in communication or to the sharing of irrelevant clauses. This paper, proposes two innovative policies to dynamically adjust the size of shared clauses between any pair of processing units. The first approach controls the overall number of exchanged clauses whereas the second additionally exploits the relevance quality of shared clauses. Experimental results show important improvements of the state-of the-art parallel SAT solver.