Showing papers in "Artificial Intelligence in 2007"

TL;DR: A number of foundational contributions provided the basis for the formulation of argumentation models and their promotion in AI related settings and then a number of new themes that have emerged in recent years are considered, many of which provide the principal topics of the research presented in this volume.

TL;DR: In this paper, a hierarchical Markov model is proposed to infer a user's daily movements through an urban community using multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as user's destination and mode of transportation.

TL;DR: This paper defines two important rationality postulates that should be satisfied: the consistency and the closure of the results returned by a rule-based argumentation system and provides a relatively easy way in which these rationality postulate can be warranted for a particular rule- based argumentation System developed within a European project on argumentation.

TL;DR: The goal of this article is to start a discussion in the research community that will result in firmer foundations for the area of learning in multi-agent systems.

TL;DR: Two dialectic procedures for the sceptical ideal semantics for argumentation are presented, defined in terms of dispute trees, for abstract argumentation frameworks and a variant of the procedure of [P.A. Kowalski, F. Toni, Dialectic proof procedures for assumption-based, admissible argumentation, Artificial Intelligence 170 (2006) 114-159] is presented.

TL;DR: A formal, mathematical model of argument structure and evaluation is presented, taking seriously the procedural and dialogical aspects of argumentation, using premise types to capture the varying effect on the burden of proof of different kinds of questions.

TL;DR: A set of general criteria for semantics evaluation is introduced by proposing a formal counterpart to several intuitive notions related to the concepts of maximality, defense, directionality, and skepticism.

TL;DR: In this article, the authors introduce an AND/OR search space perspective for graphical models that include probabilistic networks (directed or undirected) and constraint networks, and compare memory intensive and/or graph search with inference methods, and place various existing algorithms within the search space.

TL;DR: In this paper, the authors study the ability of an election's chair to prevent a particular candidate from winning an election, through such mechanisms as voter/candidate addition/suppression/partition, ensuring that a candidate (equivalently, alternative) does not win.

TL;DR: This paper develops an algorithm called ARMS (action-relation modelling system) for automatically discovering action models from a set of successful observed plans, and lays the theoretical foundations of the learning problem and evaluates the effectiveness of ARMS empirically.

TL;DR: The contribution of the paper is to provide firm foundations for an approach to practical reasoning based on presumptive argument in terms of a well-known model for representing the effects of actions of a group of agents.

TL;DR: The recently proposed Argument Interchange Format (AIF) is extended to express arguments with a structure based on Walton's theory of argumentation schemes, and an implementation of this ontology using the RDF Schema Semantic Web-based ontology language is described.

TL;DR: A formal analysis shows that it is possible to generate forced satisfiable instances whose hardness is similar to unforced satisfiable ones.

TL;DR: A general framework for merging argumentation systems from Dung's theory of argumentation is presented and voting is used on the selected extensions of the resulting systems so as to characterize the acceptable arguments at the group level.

TL;DR: Results are presented indicating that decision problems known to be intractable in their most general form remain so even under quite severe graph-theoretic restrictions and the problem of deciding "subjective acceptability" continues to be NP-complete even when the underlying graph is a binary tree.

TL;DR: Overcoming the ''brittleness'' of present AI systems and reaching human-level AI requires programs that deal with the common sense informatic situation-in which the phenomena to be taken into account in achieving a goal are not fixed in advance.

TL;DR: In this paper, the authors present a formal analysis of the deficiency of the standard postulates alone, and show how to solve the problem by an additional postulate of independence, which is compatible with AGM and DP.

TL;DR: As I studied the RAND papers and memos, I found to my surprise that, far from replacing philosophy, the pioneers in CS and AI had learned a lot, directly and indirectly from the philosophers.

TL;DR: It is concluded that the central concerns of multiagent learning are rather narrow compared with the broad variety of work identified in [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365-377, this issue].

TL;DR: ABCN2, an argument-based extension of the CN2 rule learning algorithm is implemented, and its performance is analyzed to analyze its performance in comparison with the original CN2 algorithm.

TL;DR: A resolution-like calculus for Max-SAT is proposed and its soundness and completeness are proved, and the completeness of some refinements of this calculus is proved.

TL;DR: A framework for practical reasoning which accommodates three distinctive features of practical reasoning is presented, using the notion of argumentation frameworks to capture the first feature and addressing the third feature using a formal description of a dialogue from which preferences over values emerge.

TL;DR: Using a discriminative approach to contextual prediction and multi-modal integration, performance of head gesture detection was improved with context features even when the topic of the test set was significantly different than the training set.

TL;DR: By using this novel algorithm, each agent is assigned some part of the calculation such that the agents' shares are exhaustive and disjoint, the algorithm is decentralized, requires no communication between the agents, has minimal memory requirements, and can reflect variations in the computational speeds of the agents.

TL;DR: An extension of CASE representation of natural languages that allows a plausible means of interface between users and the computer is proposed.

TL;DR: An analysis of Ray Kurzweil's recent book The Singularity Is Near is given, along with Drew McDermott's recent critique, and an alternative is discussed, in which human-level AI arrives first via non-human-like AI's operating virtual worlds.

TL;DR: This paper develops a theoretical framework for the error bound in case-based reasoning, and proposes a novel case-base mining algorithm guided by the theoretical results that returns a high-quality case base from raw data efficiently.

TL;DR: This paper presents challenges motivated by engineering applications and discusses the potential appeal of multi-agent learning to meet these challenges and highlights various differences in the underlying assumptions and issues of concern that generally distinguish engineering applications from models that are typically considered in the economic game theory literature.

TL;DR: From the AI perspective, the term ''multiagent learning'' applies more broadly than can be usefully framed in game theoretic terms, and how (and perhaps whether) multiagent learning can beUsefully applied in complex domains is still a large open question.

TL;DR: Two extensions of a modal logic that, besides the next-time temporal operator, contains a belief operator and an information operator are proposed that provide an axiomatic characterization of the first six postulates of the AGM theory of belief revision.