Jeffrey S. Rosenschein

Learn More
A diverse collection of trust-modeling algorithms for multi-agent systems has been developed in recent years, resulting in significant breadth-wise growth without unified direction or benchmarks. Based on enthusiastic response from the agent trust community, the Agent Reputation and Trust (ART) Testbed initiative has been launched, charged with the task of(More)
We demonstrate that winner selection in two prominent proportional representation voting systems is a computationally intractable problem—implying that these systems are impractical when the assembly is large. On a different note, in settings where the size of the assembly is constant, we show that the problem can be solved in polynomial time.
Previous studies have been suggestive of the fact that reputation ratings may be provided in a strategic manner for reasons of reciprocation and retaliation, and therefore may not properly reflect the trustworthiness of rated parties. It thus appears that supporting privacy of feedback providers could improve the quality of their ratings. We argue that(More)
A formal framework is presented that models communication and promises in multi-agent interactions. This framework generalizes previous work on cooperation without communication, and shows the ability of communication to resolve conflicts among agents having disparate goals. Using a deal-making mechanism, agents are able to coordinate and cooperate more(More)
Negotiation among multiple agents remains an important topic of research in Distributed Artiicial Intelligence (DAI). Most previous work on this subject, however, has focused on bilateral negotiation, deals that are reached between two agents. There has also been research on n-agent agreement which has considered \consensus mechanisms" (such as voting),(More)
Multi-agent decision problems, in which independent agents have to agree on a joint plan of action or allocation of resources, are central to AI. In such situations, agents’ individual preferences over available alternatives may vary, and they may try to reconcile these differences by voting. Based on the fact that agents may have incentives to vote(More)
Encouraging voters to truthfully reveal their preferences in an election has long been an important issue. Previous studies have shown that some voting protocols are hard to manipulate, but predictably used <i>NP</i>-hardness as the complexity measure. Such a <i>worst-case</i> analysis may be an insufficient guarantee of resistance to manipulation.Indeed,(More)
This work uses the language of game theory to analyze negotiation among automated agents in cooperative domains. However, while game theory generally deals with negotiation in continuous domains and among agents with full information, this research considers discrete domains and the case where agents have only partial information, assumptions of greater(More)
Michael N. Huhns, Munindar P. Singh, Mark Burstein, Keith Decker, Edmund Durfee,Tim Finin, Les Gasser, Hrishikesh Goradia, Nick Jennings, Kiran Lakkaraju, Hideyuki Nakashima, H.Van Dyke Parunak, Jeffrey S. Rosenschein, Alicia Ruvinsky, Gita Sukthankar, Samarth Swarup, Katia Sycara, Milind Tambe, Tom Wagner, and Laura Zavala MAS Research Roadmap Project(More)