Artificial Intelligence

Items from 1 to 11 out of 11 results

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Author Index—Volume 164 (2005)

Artificial Intelligence > 2005 > 164 > 1-2 > 283

article

Understanding planning with incomplete information and sensing

Marcelo Oglietti

Artificial Intelligence > 2005 > 164 > 1-2 > 171-208

PKS is the framework for planning with incomplete information and sensing recently introduced by Bacchus and Petrick [Proc. KR'98, pp. 432–443]. The fact that PKS generalizes STRIPS to domains with incomplete information and sensing opens up the possibility of proposing it as a reference for comparisons with other formalisms that approach the problem from different perspectives.To this end we first...

article

A formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks

Yannick Pencolé, Marie-Odile Cordier

Artificial Intelligence > 2005 > 164 > 1-2 > 121-170

We address the problem of diagnosing large discrete event systems. Given a flow of observations from the system, the goal is to explain these observations on-line by identifying and localising possible failures and their consequences across the system. Model-based diagnosis approaches deal with this problem but, apart very recent proposals, either they require the computation of a global model of...

article

On the logic of cooperation and propositional control

Wiebe van der Hoek, Michael Wooldridge

Artificial Intelligence > 2005 > 164 > 1-2 > 81-119

Cooperation logics have recently begun to attract attention within the multi-agent systems community. Using a cooperation logic, it is possible to represent and reason about the strategic powers of agents and coalitions of agents in game-like multi-agent systems. These powers are generally assumed to be implicitly defined within the structure of the environment, and their origin is rarely discussed...

article

A formal analysis of why heuristic functions work

B. John Oommen, Luis G. Rueda

Artificial Intelligence > 2005 > 164 > 1-2 > 1-22

Many optimization problems in computer science have been proven to be NP-hard, and it is unlikely that polynomial-time algorithms that solve these problems exist unless P=NP. Alternatively, they are solved using heuristics algorithms, which provide a sub-optimal solution that, hopefully, is arbitrarily close to the optimal. Such problems are found in a wide range of applications, including artificial...

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Knowledge updates: Semantics and complexity issues

Chitta Baral, Yan Zhang

Artificial Intelligence > 2005 > 164 > 1-2 > 209-243

We consider the problem of updating of an agent's knowledge. We propose a formal method of knowledge update on the basis of the semantics of modal logic S5. In our method, an update is specified according to the minimal change on both the agent's actual world and knowledge. We discuss general minimal change properties of knowledge update and show that our knowledge update operator satisfies all the...

article

MaxSolver: An efficient exact algorithm for (weighted) maximum satisfiability

Zhao Xing, Weixiong Zhang

Artificial Intelligence > 2005 > 164 > 1-2 > 47-80

Maximum Boolean satisfiability (max-SAT) is the optimization counterpart of Boolean satisfiability (SAT), in which a variable assignment is sought to satisfy the maximum number of clauses in a Boolean formula. A branch and bound algorithm based on the Davis–Putnam–Logemann–Loveland procedure (DPLL) is one of the most competitive exact algorithms for solving max-SAT. In this paper, we propose and investigate...

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Editorial Board

Artificial Intelligence > 2005 > 164 > 1-2 > CO2

article

Forthcoming Papers

Artificial Intelligence > 2005 > 164 > 1-2 > 281-282

article

The complexity of contract negotiation

Paul E. Dunne, Michael Wooldridge, Michael Laurence

Artificial Intelligence > 2005 > 164 > 1-2 > 23-46

The use of software agents for automatic contract negotiation in e-commerce and e-trading environments has been the subject of considerable recent interest. A widely studied abstract model considers the setting in which a set of agents have some collection of resources shared out between them and attempt to construct a mutually beneficial optimal reallocation of these by trading resources. The simplest...

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Qualitative decision under uncertainty: back to expected utility

Hélène Fargier, Régis Sabbadin

Artificial Intelligence > 2005 > 164 > 1-2 > 245-280

Different qualitative models have been proposed for decision under uncertainty in Artificial Intelligence, but they generally fail to satisfy the principle of strict Pareto dominance or principle of “efficiency”, in contrast to the classical numerical criterion—expected utility. Among the most prominent examples of qualitative models are the qualitative possibilistic utilities (QPU) and the order...

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164
Year:
2005

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1-2 (11)

Keywords

A* ALGORITHMS (1)
BELIEF REVISION AND UPDATE (1)
COMPLEXITY (1)
COMPUTATIONAL COMPLEXITY (1)
CONDITIONAL PLANNING (1)
COOPERATION LOGIC (1)
DECENTRALISED MODEL (1)
DECISION UNDER UNCERTAINTY (1)
DISCRETE EVENT SYSTEMS (1)
DISTRIBUTED ARTIFICIAL INTELLIGENCE (1)
DPLL (1)
EPISTEMIC PLANNING (1)
EXPECTED UTILITY (1)
FAULT PROPAGATION (1)
HEURISTIC ALGORITHMS (1)
KNOWLEDGE REPRESENTATION (1)
KNOWLEDGE UPDATE (1)
KRIPKE STRUCTURES (1)
LEXIMAX ORDERING (1)
LEXIMIN ORDERING (1)
LINEAR PROGRAMMING (1)
LOGICS FOR CONTROL (1)
MODEL-BASED DIAGNOSIS (1)
MULTI-AGENT SYSTEMS (1)
MULTIAGENT SYSTEMS (1)
NEGOTIATION (1)
NONLINEAR PROGRAMMING (1)
OPTIMIZATION (1)
PATTERN RECOGNITION (1)
PLAN QUALITY (1)
PLANNING (1)
PLANNING WITH INCOMPLETE INFORMATION AND SENSING (1)
POSSIBILITY THEORY (1)
QUALITY MEASURES (1)
S5 MODAL LOGIC (1)
TELECOMMUNICATION NETWORKS (1)
UNIT PROPAGATION (1)
VARIABLE ORDERING (1)
WEIGHTED MAXIMUM SATISFIABILITY (1)
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Artificial Intelligence

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