AAAI presented the AAAI-04 workshop program on July 25-26, 2004 in San Jose, California. This program included twelve workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were as follows: (1) Adaptive Text Extraction and Mining; (2) Agent Organizations: Theory and Practice; (3) Anchoring Symbols to Sensor Data; (4) Challenges in Game AI; (5) Fielding Applications of Artificial Intelligence; (6) Forming and Maintaining Coalitions in Adaptive Multiagent Systems; (7) Intelligent Agent Architectures: Combining the Strengths of Software Engineering and Cognitive Systems; (8) Learning and Planning in Markov Processes -- Advances and Challenges; (9) Semantic Web Personalization; (10) Sensor Networks; (11) Spatial and Temporal Reasoning; and (12) Supervisory Control of Learning and Adaptive Systems.

Caesarea Edmond Benjamin de Rothschild Email: patrick.hung@uoit.ca (MDAI 2005).

AAAI presented the AAAI-04 workshop program on July 25-26, 2004 in San Jose, California. This program included twelve workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were as follows: (1) Adaptive Text Extraction and Mining; (2) Agent Organizations: Theory and Practice; (3) Anchoring Symbols to Sensor Data; (4) Challenges in Game AI; (5) Fielding Applications of Artificial Intelligence; (6) Forming and Maintaining Coalitions in Adaptive Multiagent Systems; (7) Intelligent Agent Architectures: Combining the Strengths of Software Engineering and Cognitive Systems; (8) Learning and Planning in Markov Processes -- Advances and Challenges; (9) Semantic Web Personalization; (10) Sensor Networks; (11) Spatial and Temporal Reasoning; and (12) Supervisory Control of Learning and Adaptive Systems.

Posters, and the AAAI/ SIGART consist of two phases: a qualification Doctoral Consortium. Please visit the round and a runoff competition. A AAAI is pleased to announce the AAAI-05 web site periodically for upto-date $10,000 prize will be awarded to the launch of the First Annual Artificial information. We hope you will winning entrant. The competition is Intelligence for Interactive Digital join us in Pittsburgh!

AI'2004 was held at the University of AI'2004 had a large program committee were affiliated with AI'2004: The Third Canada (NRC) (who also hosted the hosted one of the longest-running and the First Causality and preconference workshops) cosponsored conference series in the AI field. The Causal Discovery workshop, organized the AI'2004 Graduate Symposium. CSCSI conference started in 1976 as a by Kamran Karimi (University of Regina). The Eighteenth Canadian Conference the conference became an annual forum, In addition, the AI'2004 graduate on Artificial Intelligence thus making AI'2004 the seventeenth symposium attracted many graduate (AI'2005) will be held in Victoria, conference in this series. These events turned the city British Columbia, from May 9 to 11, the years, the conference has attracted of London into a Mecca for AI and 2005.

The Sixth International Conference on Enterprise Information Systems (ICEIS) was held in Porto, Portugal; previous venues were in Spain, France, and the United Kingdom. Since its inception in 1999, ICEIS has grown steadily, and is now one of the largest international conferences in the area of information systems. In 2004, more than 600 papers were submitted to the conference and its ten satellite workshops. One of the interesting features of this conference is the high number of invited speakers. In 2004, eighteen keynote speakers were featured at ICEIS and its workshops.

Learning) are also available as AAAI be integrated and (2) architectures Technical Reports. There through Sunday, October 22-24 at an opportunity for new and junior researchers--as was consensus among participants the Hyatt Regency Crystal City in Arlington, well as students and that metrics in machine learning, Virginia, adjacent to Washington, postdoctoral fellows--to get an inside planning, and natural language processing DC. The symposium series was look at what funding agencies expect have driven advances in those preceded on Thursday, October 21 by in proposals from prospective subfields, but that those metrics have a one-day AI funding seminar, which grantees. Representatives and program also distracted attention from how to was open to all registered attendees. The topic is of increasing interest Domains for motivating, testing, large numbers of agents, more complex with the advent of peer-to-peer network and funding this research were agent behaviors, partially observable services and with ad-hoc wireless proposed (some during our joint session environments, and mutual adaptation.

We examine properties of a model of resource allocation in which several agents exchange resources in order to optimise their individual holdings. The schemes discussed relate to well-known negotiation protocols proposed in earlier work and we consider a number of alternative notions of ``rationality'' covering both quantitative measures, e.g. cooperative and individual rationality and more qualitative forms, e.g. Pigou-Dalton transfers. While it is known that imposing particular rationality and structural restrictions may result in some reallocations of the resource set becoming unrealisable, in this paper we address the issue of the number of restricted rational deals that may be required to implement a particular reallocation when it is possible to do so. We construct examples showing that this number may be exponential (in the number of resources m), even when all of the agent utility functions are monotonic. We further show that k agents may achieve in a single deal a reallocation requiring exponentially many rational deals if at most k-1 agents can participate, this same reallocation being unrealisable by any sequences of rational deals in which at most k-2 agents are involved.

Recent multi-agent extensions of Q-Learning require knowledge of other agents' payoffs and Q-functions, and assume game-theoretic play at all times by all other agents. This paper proposes a fundamentally different approach, dubbed "Hyper-Q" Learning, in which values of mixed strategies rather than base actions are learned, and in which other agents' strategies are estimated from observed actions via Bayesian inference. Hyper-Q may be effective against many different types of adaptive agents, even if they are persistently dynamic. Against certain broad categories of adaptation, it is argued that Hyper-Q may converge to exact optimal time-varying policies. In tests using Rock-Paper-Scissors, Hyper-Q learns to significantly exploit an Infinitesimal Gradient Ascent (IGA) player, as well as a Policy Hill Climber (PHC) player. Preliminary analysis of Hyper-Q against itself is also presented.

We study how to learn to play a Pareto-optimal strict Nash equilibrium when there exist multiple equilibria and agents may have different preferences among the equilibria. We focus on repeated coordination games of non-identical interest where agents do not know the game structure up front and receive noisy payoffs. We design efficient near-optimal algorithms for both the perfect monitoring and the imperfect monitoring setting(where the agents only observe their own payoffs and the joint actions).