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Thomas Haynes

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Thomas Haynes

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Personal Homepage:
blogs.sun.com/roller/page/tdh/
Current place of employment:
Sun Microsystems, Inc.

Thomas Haynes got his PhD in Computer Science from the University of Tulsa in 1998. His research field was in genetic programming.

Drafts of his work can be found at loghyr.com.

Tom taught at the Wichita State University before starting back in the systems industry. He worked for 6 years at Network Appliance and recently joined the NFS group at Sun Microsystems, Inc.

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Publications by Thomas Haynes (bibliography)

» 1998 «

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Haynes, Thomas and Sen, Sandip (1998): Learning Cases to Resolve Conflicts and Improve Group Behavior. In International Journal of Human-Computer Studies, 48 (1) pp. 31-49

“Groups of agents following fixed behavioral rules can be limited in performance and efficiency. Adaptability and flexibility are key components of intelligent behavior which allow agent groups to improve performance in a given domain using prior problem-solving experience. We motivate the utility of individual learning by group members in the context of overall group behavior. In particular, we propose a framework in which individual group members learn cases from problem-solving experiences to improve their model of other group members. We use a testbed problem from the Distributed Artificial Intelligence literature to show that simultaneous learning by group members can lead to significant improvement in group performance and efficiency over agent groups following static behavioral rules.”

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» 1997 «

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Sen, Sandip, Haynes, Thomas and Arora, Neeraj (1997): Satisfying User Preferences while Negotiating Meetings. In International Journal of Human-Computer Studies, 47 (3) pp. 407-427

“Our research agenda focuses on building software agents that can facilitate and streamline group problem solving in organizations. We are particularly interested in developing intelligent agents that can partially automate routine information processing tasks by representing and reasoning with the preferences and biases of associated users. The distributed meeting scheduler is a collection of agents, responsible for scheduling meetings for their respective users. Users have preferences on when they like to meet, e.g. time of day, day of week, status of other invitees, topic of the meeting, etc. The agent must balance such concerns, proposing and accepting meeting times that satisfy as many of these criteria as possible. For example, a user might prefer not to meet at lunchtime unless the president of the company is hosting the meeting. We apply techniques from voting theory to arrive at consensus choices for meeting times while balancing different preferences.”

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