We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our "Generalized Integrated Learning Architecture" (GILA) is a set of integrated learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During the performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.
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.
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