A large number of interdependent issues in complex contract negotiation poses a significant challenge for current approaches, which becomes even more apparent when negotiation problems scale up. To address this challenge, we present a structured anytime search process with an agenda management mechanism using a hierarchical negotiation model, where agents search at various levels during the negotiation with the guidance of a mediator. This structured negotiation process increases computational efficiency, making negotiations scalable for large number of interdependent issues. To validate the contributions of our approach, 1) we developed our proposed negotiation model using a hierarchical problem structure and a constraint-based preference model for real-world applications; 2) we defined a scenario matrix to capture various characteristics of negotiation scenarios and developed a scenario generator that produces test cases according to this matrix; and 3) we performed an extensive set of experiments to study the performance of this structured negotiation protocol and the influence of different scenario parameters, and investigated the Pareto efficiency and social welfare optimality of the negotiation outcomes. The experimental result supports the hypothesis that this hierarchical negotiation approach greatly improves scalability with the complexity of the negotiation scenarios.

Hybrid group autonomy, organizations and teams composed of humans, machines and robots, are important to AI. Unlike the war in Iraq in 2002, the war in Afghanistan has hundreds of mobile robots aloft, on land, or under the sea. But when it comes to solving problems as part of a team, these agents are socially passive. Were the problem of aggregation and the autonomy of hybrids to be solved, robot teams could accompa- ny humans to address and solve problems together on Mars, under the sea, or in dan- gerous locations on earth (such as, fire-fighting, reactor meltdowns, and future wars). “Robot autonomy is required because one soldier cannot control several robots ... [and] because no computational system can discriminate between combatants and innocents in a close-contact encounter.” (Sharkey, 2008) Yet, today, one of the fundamental unsolved problems in the social sciences is the aggregation of individual data (such as preferences) into group (team) data (Giles, 2011) The original motivation behind game theory was to study the effect that multi- ple agents have on each other (Von Neumann and Morgenstern, 1953), known as interdependence or mutual dependence. Essentially, the challenge addresses the ques- tion: why is a group different from the collection of individuals who comprise the group? That the problem remains unsolved almost 70 years later is a remarkable com- ment on the state of the social sciences today, including game theory and economics. But solving this challenge is essential for the science and engineering of multiagent, multirobot and hybrid environments (that is, humans, machines and robots working together).

Thirteenth International Distributed AI Workshop

This article discusses the Thirteenth International Distributed AI Workshop. An overview of the workshop is given as well as concerns and goals for the technology. This article discusses the Thirteenth International Distributed AI Workshop. An overview of the workshop is given as well as concerns and goals for the technology.

The goal of this workshop was which was held in June 1995 in San istributed artificial intelligence the cooperative solution of "making connections," trying to better Francisco. The DAI Workshop problems in multiagent intelligent understand the connections received financial support from the systems with both computational between DAI and related fields (for American Association for Artificial and human agents. The central problem example, computer-supported cooperative Intelligence as well as the Boeing in DAI is how to achieve coordinated work, group decision support Company. Registration materials for the Thirteenth National Conference on Artificial Intelligence (AAAI-96), the Eighth Innovative Applications of Artificial Intelligence Conference (IAAI-96), and the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) are now available from the AAAI office at ncai@aaai.org Copies of the AAAI-96 registration brochure are being mailed to all AAAI members.