The binary Constraint Satisfaction Problem (CSP) is to decide whether there exists an assignment to a set of variables which satisfies specified constraints between pairs of variables. A CSP instance can be presented as a labelled graph (called the microstructure) encoding both the forms of the constraints and where they are imposed. We consider subproblems defined by restricting the allowed form of the microstructure. One form of restriction that has previously been considered is to forbid certain specified substructures (patterns). This captures some tractable classes of the CSP, but does not capture the well-known property of acyclicity. In this paper we introduce the notion of a topological minor of a binary CSP instance. By forbidding certain patterns as topological minors we obtain a compact mechanism for expressing several novel tractable classes, including new generalisations of the class of acyclic instances.

We develop a methodology to model and verify Regarding the second limitation, proposals have been put open multi-agent systems (OMAS), where agents forward to consider a set of objects that vary at design time; may join in or leave at run time. Further, we specify the set of agents is normally considered to be finite in each properties of interest on OMAS in a variant of firstorder system run. This is a sensible assumption in many scenarios, temporal-epistemic logic, whose characterising but there are applications of MAS (e.g., e-commerce, smart features include epistemic modalities indexed grids) where an unbounded number of agents may freely enter to individual terms, interpreted on agents appearing and leave the system at run time. There is, therefore, at a given state. This formalism notably allows a need to account for the unbounded and possibly infinite to express group knowledge dynamically. We study agents joining in or leaving an open MAS. In this setting it the verification problem of these systems and show is still of interest to reason about their evolution and what that, under specific conditions, finite bisimilar abstractions they know individually and collectively. For example, in an can be obtained.

Description Logic (DL) based ontologies and non-monotonic rules provide complementary features whose combination is crucial in many applications. In hybrid knowledge bases (KBs), which combine both formalisms, for large real-world applications, often integrating knowledge originating from different sources, inconsistencies can easily occur. These commonly trivialize standard reasoning and prevent us from drawing any meaningful conclusions. When restoring consistency by changing the KB is not possible, paraconsistent reasoning offers an alternative by allowing us to obtain meaningful conclusions from its consistent part. In this paper, we address the problem of efficiently obtaining meaningful conclusions from (possibly inconsistent) hybrid KBs. To this end, we define two paraconsistent semantics for hybrid KBs which, beyond their differentiating properties, are faithful to well-known paraconsistent semantics as well as the non-paraconsistent logic they extend, and tractable if reasoning in the DL component is.

It is now readily accepted that automated algorithm configuration is a necessity for ensuring optimized performance of solvers on a particular problem domain. Even the best developers who have carefully designed their solver are not always able to manually find the best parameter settings for it. Yet, the opportunity for improving performance has been repeatedly demonstrated by configuration tools like ParamILS, SMAC, and GGA. However, all these techniques currently assume a static environment, where demonstrative instances are procured beforehand, potentially unlimited time is provided to adequately search the parameter space, and the solver would never need to be retrained. This is not always the case in practice. The ReACT system, proposed in 2014, demonstrated that a solver could be configured during runtime as new instances arrive in a steady stream. This paper further develops that approach and shows how a ranking scheme, like TrueSkill, can further improve the configurator's performance, making it able to quickly find good parameterizations without adding any overhead on the time needed to solve any new instance, and then continuously improve as new instances are evaluated. The enhancements to ReACT that we present enable us to even outperform existing static configurators like SMAC in a non-dynamic setting.

We propose a graph-based method for extractive single-document summarization which considers importance, non-redundancy and local coherence simultaneously. We represent input documents by means of a bipartite graph consisting of sentence and entity nodes. We rank sentences on the basis of importance by applying a graph-based ranking algorithm to this graph and ensure non-redundancy and local coherence of the summary by means of an optimization step. Our graph based method is applied to scientific articles from the journal PLOS Medicine. We use human judgements to evaluate the coherence of our summaries. We compare ROUGE scores and human judgements for coherence of different systems on scientific articles. Our method performs considerably better than other systems on this data. Also, our graph-based summarization technique achieves state-of-the-art results on DUC 2002 data. Incorporating our local coherence measure always achieves the best results.

Interactive dynamic influence diagrams(I-DIDs) are a well recognized decision model that explicitly considers how multiagent interaction affects individual decision making. To predict behavior of other agents, I-DIDs require models of the other agents to be known ahead of time and manually encoded. This becomes a barrier to I-DID applications in a human-agent interaction setting, such as development of intelligent non-player characters(NPCs) in real-time strategy(RTS) games, where models of other agents or human players are often inaccessible to domain experts. In this paper, we use automatic techniques for learning behavior of other agents from replay data in RTS games. We propose a learning algorithm with improvement over existing work by building a full profile of agent behavior. This is the first time that data-driven learning techniques are embedded into the I-DID decision making framework. We evaluate the performance of our approach on two test cases.

Schema.org is an initiative by the major search engine providers Bing, Google, Yahoo, and Yandex that provides a collection of ontologies which webmasters can use to mark up their pages. Schema.org comes without a formal language definition and without a clear semantics. We formalize the language of Schema.org as a Description Logic (DL) and study the complexity of querying data using (unions of) conjunctive queries in the presence of ontologies formulated in this DL (from several perspectives). While querying is intractable in general, we identify various cases in which it is tractable and where queries are even rewritable into FO queries or datalog programs.

In optimal planning as heuristic search, admissible pruning techniques are paramount. One idea is dominance pruning, identifying states "better than" other states. Prior approaches are limited to simple dominance notions, like "more STRIPS facts true" or "higher resource supply". We apply simulation, well-known in model checking, to compute much more general dominance relations based on comparing transition behavior across states. We do so effectively by expressing state-space simulations through the composition of simulations on orthogonal projections. We show how simulation can be made more powerful by intertwining it with a notion of label dominance. Our experiments show substantial improvements across several IPC benchmark domains.

Formal Concept Analysis (FCA) is a prominent field of applied mathematics using object-attribute relationships to define formal concepts — groups of objects with common attributes — which can be ordered into conceptual hierarchies, so-called concept lattices. We consider the problem of satisfiability of membership constraints, i.e., to determine if a formal concept exists whose object and attribute set include certain elements and exclude others. We analyze the computational complexity of this problem in general and for restricted forms of membership constraints. We perform the same analysis for generalizations of FCA to incidence structures of arity three (objects, attributes and conditions) and higher. We present a generic answer set programming (ASP) encoding of the membership constraint satisfaction problem, which allows for deploying available highly optimized ASP tools for its solution. Finally, we discuss the importance of membership constraints in the context of navigational approaches to data analysis.

We propose a new type of algorithm for computing first-order (FO) rewritings of concept queries under ELHdr-TBoxes. The algorithm is tailored towards efficient implementation, yet complete. It outputs a succinct non-recursive datalog rewriting if the input is FO-rewritable and otherwise reports non-FO-rewritability. We carry out experiments with real-world ontologies which demonstrate excellent performance in practice and show that TBoxes originating from applications admit FO-rewritings of reasonable size in almost all cases, even when in theory such rewritings are not guaranteed to exist.