My research uses partially observable Markov decision processes (POMDP's) as a basic framework for Multi-Agent planning. We distinguish three perspectives: first one is that of an omniscient agent that has access to the global state of the system, second one is the perspective of an individual agent that has access only to its local state, and the third one is the perspective of an agent that models the states of information of the other agents. We detail how the first perspective differs from the other two due to the partial observability.

POMDP's allow us to formally define the notion of optimal actions in each perspective, and to quantify the loss of performance due to partial observability, and possible gain in performance due to intelligent information exchange between the agents. As an example we consider the domain of agents in a distributed information network. There, agents have to decide how to route packets and how to share information with other agents. Though almost all routing protocols have been formulated based on detailed study of the functional parameters in the system, there has been no clear formal representation for optimality. We argue that the various routing protocols should fall out as different approximations to policies (optimal solutions) in such a framework. Our approach also proves critical and useful for the computation of error bounds due to approximations used in practical routing algorithms. Each routing protocol can be seen as a conditional plan that involves physical actions, which change the physical state of the system, and actions that explicitly exchange information.

• Bharaneedharan Rathnasabapathy and Piotr Gmytrasiewicz; "Formalizing Multi-Agent POMDP's in the context of network routing", [PS], to appear in the proceedings of the Hawaii International Conference on System Sciences, HICSS-36, January 2003.

• POMDP Solver by Tony Cassandra
  
  
• Incremental pruning algorithm which gives all the equivalent solutions for a POMDP problem -- send me an email.

• NIPS '03 (Vancouver and Whistler, British Columbia, Canada)
  Tutorials - December 8, 2003; Conference - December 9-11, 2003; Workshops - December 11-13, 2003
  Submission Deadlines:
  Papers: June 6, 2003; Workshops: August 1, 2003
  
  
• IICAI-03 (Hyderabad, India)
  Conference - December 18-20 2003
  Submission Deadlines:
  Draft papers: July 1st 2003
  
  
• AAMAS-04 (New York City, NY)
  Conference - July 18-23 2004
  Submission Deadlines:
  Abstract due: Jan 16, 2004
  Paper deadline: Jan 21, 2004
  
  
• AAAI-04 (San Jose, California)
  Conference - July 25-29 2004
  Submission Deadlines:
  December 1, 2003: January 20, 2004: Authors register on the AAAI web site.
  January 20, 2004: Electronic submission of title page, abstract and paper.
  January 23, 2004: Submission of hard copy of one formatted title page, including tracking number, title, authors, contact information, and keywords.
  March 12, 2004: Notification of acceptance or rejection.
  April 6, 2004: Camera-ready copy due at AAAI office.
  

• UIC AI Lab
• Multi-Agent Systems group
• POMDP information page
• Tony Cassandra's POMDP page
• Introduction to Hidden markov models
• Reinforcement learning : Survey
• NS Simulator
• AI JOBS