Sequence optimization, where the items in a list are ordered to maximize some reward has many applications such as web advertisement placement, search, and control libraries in robotics. Previous work in sequence optimization produces a static ordering that does not take any features of the item or context of the problem into account. In this work, we propose a general approach to order the items within the sequence based on the context (e.g., perceptual information, environment description, and goals). We take a simple, efficient, reduction-based approach where the choice and order of the items is established by repeatedly learning simple classifiers or regressors for each "slot" in the sequence. Our approach leverages recent work on submodular function maximization to provide a formal regret reduction from submodular sequence optimization to simple cost-sensitive prediction. We apply our contextual sequence prediction algorithm to optimize control libraries and demonstrate results on two robotics problems: manipulator trajectory prediction and mobile robot path planning.

Data Mining techniques plays a vital role like extraction of required knowledge, finding unsuspected information to make strategic decision in a novel way which in term understandable by domain experts. A generalized frame work is proposed by considering non - domain experts during mining process for better understanding, making better decision and better finding new patters in case of selecting suitable data mining techniques based on the user profile by means of intelligent agents. KEYWORDS: Data Mining Techniques, Intelligent Agents, User Profile, Multidimensional Visualization, Knowledge Discovery.

We introduce the framework of qualitative optimization problems (or, simply, optimization problems) to represent preference theories. The formalism uses separate modules to describe the space of outcomes to be compared (the generator) and the preferences on outcomes (the selector). We consider two types of optimization problems. They differ in the way the generator, which we model by a propositional theory, is interpreted: by the standard propositional logic semantics, and by the equilibrium-model (answer-set) semantics. Under the latter interpretation of generators, optimization problems directly generalize answer-set optimization programs proposed previously. We study strong equivalence of optimization problems, which guarantees their interchangeability within any larger context. We characterize several versions of strong equivalence obtained by restricting the class of optimization problems that can be used as extensions and establish the complexity of associated reasoning tasks. Understanding strong equivalence is essential for modular representation of optimization problems and rewriting techniques to simplify them without changing their inherent properties.

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.

Many examples of epistemic reasoning in the literature exhibit a stratified structure: defaults are formulated on top of an incomplete knowledge base. These defaults derive extra information in case information is missing in the knowledge base. In autoepistemic logic, default logic and ASP this inherent stratification is not preserved as they may refer to their own knowledge or logical consequences. Defining the semantics of such logics requires a complex mathematical construction. As an alternative, this paper further develops ordered epistemic logic. This logic extends first order logic with a modal operator and stratification is maintained. This allows us to define an easy to understand semantics. Moreover, inference tasks have a lower complexity than in autoepistemic logic and the logic integrates seamlessly into classical logic and its extensions. In this paper we also propose a generalization of ordered epistemic logic, which we call distributed ordered epistemic logic. We argue that it can provide a semantic foundation for a number of distributed knowledge representation formalisms found in the literature.

Entity resolution (ER) is an important and common problem in data cleaning. It is about identifying and merging records in a database that represent the same real-world entity. Recently, matching dependencies (MDs) have been introduced and investigated as declarative rules that specify ER. An ER process induced by MDs over a dirty instance leads to multiple clean instances, in general. In this work, we present disjunctive answer set programs (with stable model semantics) that capture through their models the class of alternative clean instances obtained after an ER process based on MDs. With these programs, we can obtain clean answers to queries, i.e. those that are invariant under the clean instances, by skeptically reasoning from the program. We investigate the ER programs in terms of expressive power for the ER task at hand. As an important special and practical case of ER, we provide a declarative reconstruction of the so-called union-case ER methodology, as presented through a generic approach to ER (the so-called Swoosh approach).

Complex mission or task specification languages play a fundamentally important role in human/robotic interaction. In realistic scenarios such as emergency response, specifying temporal, resource and other constraints on a mission is an essential component due to the dynamic and contingent nature of the operational environments. It is also desirable that in addition to having a formal semantics, the language should be sufficiently expressive, pragmatic and abstract. The main goal of this paper is to propose a mission specification language that meets these requirements. It is based on extending both the syntax and semantics of a well-established formalism for reasoning about action and change, Temporal Action Logic (TAL), in order to represent temporal composite actions with constraints. Fixpoints are required to specify loops and recursion in the extended language. The results include a sound and complete proof theory for this extension. To ensure that the composite language constructs are adequately grounded in the pragmatic operation of robotic systems, Task Specification Trees (TSTs) and their mapping to these constructs are proposed. The expressive and pragmatic adequacy of this approach is demonstrated using an emergency response scenario.

Abstract argumentation frameworks (AFs) provide the basis for various reasoning problems in the areas of Knowledge Representation and Artificial Intelligence. Efficient evaluation of AFs has thus been identified as an important research challenge. So far, implemented systems for evaluating AFs have either followed a straight-forward reduction-based approach or been limited to certain tractable classes of AFs. In this work, we present a generic approach for reasoning over AFs, based on the novel concept of complexity-sensitivity. Establishing the theoretical foundations of this approach, we derive several new complexity results for preferred, semistable and stage semantics which complement the current complexity landscape for abstract argumentation, providing further understanding on the sources of intractability of AF reasoning problems. The introduced generic framework exploits decision procedures for problems of lower complexity whenever possible. This allows, in particular, instantiations of the generic framework via harnessing in an iterative way current sophisticated Boolean satisfiability (SAT) solver technology for solving the considered AF reasoning problems. First experimental results show that the SAT-based instantiation of our novel approach outperforms existing systems.

Automatically deriving properties of new games is one of the fundamental challenges for general game-playing systems, whose task is to learn to play any previously unknown game solely by being given the rules of that game. A recently developed method uses Answer Set Programming for verifying finitely-bounded temporal invariance properties against a given game description by structural induction. Addressing the new challenge posed by the recent extension of the general Game Description Language to include games with imperfect information and randomness, we extend this method to epistemic properties about games. We formally prove this extension to be correct, and we report on experiments that show its practical applicability.

Only-knowing was originally introduced by Levesque to capture the beliefs of an agent in the sense that its knowledge base is all the agent knows. When a knowledge base contains defaults Levesque also showed an exact correspondence between only-knowing and autoepistemic logic. Later these results were extended by Lakemeyer and Levesque to also capture a variant of autoepistemic logic proposed by Konolige and Reiter's default logic. One of the benefits of such an approach is that various nonmonotonic formalisms can be compared within a single monotonic logic leading, among other things, to the first axiom system for default logic. In this paper, we will bring another large class of nonmonotonic systems, which were first studied by McDermott and Doyle, into the only-knowing fold. Among other things, we will provide the first possible-world semantics for such systems, providing a new perspective on the nature of modal approaches to nonmonotonic reasoning.