Sciweavers

AI
2002
Springer
13 years 7 months ago
The size distribution for Markov equivalence classes of acyclic digraph models
Bayesian networks, equivalently graphical Markov models determined by acyclic digraphs or ADGs (also called directed acyclic graphs or dags), have proved to be both effective and ...
Steven B. Gillispie, Michael D. Perlman
AI
2002
Springer
13 years 7 months ago
Using similarity criteria to make issue trade-offs in automated negotiations
Automated negotiation is a key form of interaction in systems that are composed of multiple autonomous agents. The aim of such interactions is to reach agreements through an itera...
Peyman Faratin, Carles Sierra, Nicholas R. Jenning...
AI
2002
Springer
13 years 7 months ago
Coherence in finite argument systems
Systems provide a rich abstraction within which divers concepts of reasoning, acceptability and defeasibility of arguments, etc., may be studied using a unified framework. Two imp...
Paul E. Dunne, Trevor J. M. Bench-Capon
AI
2002
Springer
13 years 7 months ago
Explanations, belief revision and defeasible reasoning
We present different constructions for non-prioritized belief revision, that is, belief changes in which the input sentences are not always accepted. First, we present the concept...
Marcelo A. Falappa, Gabriele Kern-Isberner, Guille...
AI
2002
Springer
13 years 7 months ago
On the computational complexity of assumption-based argumentation for default reasoning
ko et al. have recently proposed an abstract framework for default reasoning. Besides capturing most existing formalisms and proving that their standard semantics all coincide, th...
Yannis Dimopoulos, Bernhard Nebel, Francesca Toni
AI
2002
Springer
13 years 7 months ago
Emergence of social conventions in complex networks
The emergence of social conventions in multi-agent systems has been analyzed mainly in settings where every agent may interact either with every other agent or with nearest neighb...
Jordi Delgado
AI
2002
Springer
13 years 7 months ago
Learning Bayesian networks from data: An information-theory based approach
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
AI
2002
Springer
13 years 7 months ago
Improving heuristic mini-max search by supervised learning
This article surveys three techniques for enhancing heuristic game-tree search pioneered in the author's Othello program Logistello, which dominated the computer Othello scen...
Michael Buro
AI
2002
Springer
13 years 7 months ago
Multiagent learning using a variable learning rate
Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on ...
Michael H. Bowling, Manuela M. Veloso