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Sabyasachi Saha

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers
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10

IJCAI Conference 2007 Conference Paper

  • Sabyasachi Saha
  • Sandip Sen

We study the problem of autonomous agents negotiating the allocation of multiple indivisible resources. It is difficult to reach optimal outcomes in bilateral or multi-lateral negotiations over multiple resources when the agents' preferences for the resources are not common knowledge. Self-interested agents often end up negotiating inefficient agreements in such situations. We present a protocol for negotiation over multiple indivisible resources which can be used by rational agents to reach efficient outcomes. Our proposed protocol enables the negotiating agents to identify efficient solutions using systematic distributed search that visits only a subspace of the whole solution space.

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AAMAS Conference 2007 Conference Paper

Reciprocal Negotiation Over Shared Resources in Agent Societies

  • Sabyasachi Saha
  • Sandip Sen

We are interested in domains where an agent repeatedly negotiates with other agents over shared resources where the demand or utility to the agent for the shared resources vary over time. We propose a protocol that will maximize social welfare if agents reveal their true preferences in every negotiation. The protocol, however, is not truth-revealing and selfish agents have the incentive to artificially inflate preferences. We use a probabilistic reciprocative behavior that discourages the reporting of false preferences. This reciprocative behavior promotes cooperation in repeated negotiations and improves both individual and group longterm payoff. We characterize environmental conditions under which agents can develop and sustain mutually beneficial relationships with similar agents and avoid exploitation by different types of selfish agents.

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AAAI Conference 2004 Short Paper

A Bayes Net Approach to Argumentation

  • Sabyasachi Saha
  • Sandip Sen

Argumentation-based negotiation approaches have been proposed to present realistic negotiation contexts. This paper presents a novel Bayesian network based argumentation and decision making framework that allows agents to utilize models of other agents. Our goal is to use Bayesian networks to capture the opponent model through an incremental learning process and use the model to generate more effective arguments to convince the opponent to accept favorable contracts.

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