We are witnessing an explosion of available data from the Web, government authorities, scientific databases, sensors and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling the vast amounts of data for these applications. In this paper, we consider nonmonotonic reasoning, which has traditionally focused on rich knowledge structures. In particular, we consider defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge data sets. Our experimental results demonstrate that defeasible reasoning with billions of data is performant, and has the potential to scale to trillions of facts.

We describe the application of assumption-based argumentation (ABA) to a domain of medical knowledge derived from clinical trials of drugs for breast cancer. We adapt an algorithm for calculating the admissible semantics for ABA frameworks to take account of preferences and describe a prototype implementation which uses variant-based parallel computation to improve the efficiency of query answering.

We consider the problem of synthesizing an agent pro- tocol satisfying LTL specifications for multiple, partially- observable environments. We present a sound and complete procedure for solving the synthesis problem in this setting and show it is computationally optimal from a theoretical com- plexity standpoint. While this produces perfect-recall, hence unbounded, strategies we show how to transform these into agent protocols with bounded number of states.

In this paper we encode some of the reasoning methods used in frame based knowledge representation languages in answer set programming (ASP). In particular, we show how cloning'' and unification'' in frame based systems can be encoded in ASP. We then show how some of the types of queries with respect to a biological knowledge base can be encoded using our methodology. We also provide insight on how the reasoning can be done more efficiently when dealing with a huge knowledge base.

In this paper we introduce an abstract theory of normative reasoning, whose central notion is the generation of obligations, permissions and institutional facts from conditional norms.

We explore the paradigm of artifact-centric systems from a knowledge-based perspective. We provide a semantics based on interpreted-systems to interpret a first-order temporal- epistemic language with identity in a multi-agent setting. We consider the model checking problem for this language and provide abstraction results. We isolate a natural subclass of artifact-systems for which the model checking problem is decidable. We give an upper bound on the complexity of the model checking problem.

Computing minimal models is an important task in Knowledge Representation and Reasoning that appears in formalisms such as circumscription, diagnosis and answer set programming. Even the most basic question of whether there exists a minimal model containing a given variable is known to be $\Sigma_2 P$-complete. In this work we study the problem of computing minimal models from the viewpoint of parameterized complexity theory. We perform an extensive complexity analysis of this problem with respect to eleven parameters. Tractable fragments based on combinations of these parameters are identified by giving several fixed-parameter algorithms. For the remaining combinations we show parameterized hardness results and thus prove that under usual complexity theoretic assumptions no further fixed-parameter algorithms exist for these parameters.

We analyze, along the lines of the knowledge compilation map, both the tractability and the succinctness of the propositional language URC of unit-refutation complete propositional formulae, as well as its disjunctive closure URC[V, ], and a superset of URC where variables can be existentially quantified and unit-refutation completeness concerns only consequences built up from free variables.

The stable model semantics treats a logic program as a mechanism for specifying its intensional predicates. In this paper we discuss a modification of that semantics in which functions, rather than predicates, are intensional. The idea of the new definition comes from nonmonotonic causal logic.

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