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Jiaying Shen

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7 papers
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7

IJCAI Conference 2016 Conference Paper

Resolving Over-Constrained Conditional Temporal Problems Using Semantically Similar Alternatives

  • Peng Yu
  • Jiaying Shen
  • Peter Z. Yeh
  • Brian Williams

In recent literature, several approaches have been developed to solve over-constrained travel planning problems, which are often framed as conditional temporal problems with discrete choices. These approaches are able to explain the causes of failure and recommend alternative solutions by suspending or weakening temporal constraints. While helpful, they may not be practical in many situations, as we often cannot compromise on time. In this paper, we present an approach for solving such over-constrained problems, by also relaxing non-temporal variable domains through the consideration of additional options that are semantically similar. Our solution, called Conflict-Directed Semantic Relaxation (CDSR), integrates a knowledge base and a semantic similarity calculator, and is able to simultaneously enumerate both temporal and domain relaxations in best-first order. When evaluated empirically on a range of urban trip planning scenarios, CDSR demonstrates a substantial improvement in flexibility compared to temporal relaxation only approaches.

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AAAI Conference 2005 Conference Paper

OAR: A Formal Framework for Multi-Agent Negotiation

  • Jiaying Shen

In Multi-Agent systems, agents often need to make decisions about how to interact with each other when negotiating over task allocation. In this paper, we present OAR, a formal framework to address the question of how the agents should interact in an evolving environment in order to achieve their different goals. The traditional categorization of selfinterested and cooperative agents is unified by adopting a utility view. We illustrate mathematically that the degree of cooperativeness of an agent and the degree of its self-directness are not directly related. We also show how OAR can be used to evaluate different negotiation strategies and to develop distributed mechanisms that optimize the performance dynamically. This research demonstrates that sophisticated probabilistic modeling can be used to understand the behaviors of a system with complex agent interactions.

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