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Modeling the Process of Collaboration and Negotiation with Incomplete Information



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Modeling the Process of Collaboration and Negotiation with Incomplete Information Katia Sycara Praveen Paruchuri Nilanjan Chakraborty Collaborators Roie Zivan Laurie Weingart Geoff Gordon Miro Dudik Virtual Humans USC Computational Models Implementation CMU USC CMU validation Identify Cultural Factors CUNY Georgetown CMU Theory Formation validation Surveys Interviews Data Analysis CUNY Georgetown U Pitt CMU Common task Subgroup task CUNY CMU U Mich Georgetown Cross Cultural Interactions U Pitt CMU MURI 14 Program Review September 10 2009 validation RESEARCH PRODUCTS Validated Theories Models Modeling Tools Briefing Materials Scenarios Training Simulations 2 Problem Computational model of reasoning in Cooperation and Negotiation C N Capture the rich process of C N Not just outcome Not just offer counteroffer but additional communications Account for cultural social factors Rewards of other agents not known Uncertain and dynamic environment MURI 14 Program Review September 10 2009 3 Contributions Created an initial model from real human data The model Applicable in a uniform way to both collaboration and negotiation Derives sequences of actions for an agent from real transcripts as opposed to state of the art work where action selection is constructed heuristically Adapts its beliefs during the course of the interaction Learns elements of the negotiation e g other party type as the interaction proceeds Produces optimal activity sequences considering also the other agents Has only incomplete information about others MURI 14 Program Review September 10 2009 4 POMDP Partially Observable Markov Decision Process The TheWorld World Other Other agents agents Observation Agent Action Agent has initial beliefs Agent takes an action Gets an observation Interprets the observation Updates beliefs Decides on an action Repeats Agent takes optimal action considering world other agents Elements States Actions Transitions Rewards Observations MURI 14 Program Review September 10 2009 Why POMDP based modeling Decentralized algorithm Incorporated in an agent that interacts with others Can represent communication arguments offers preferences etc Many conversational turns Learns e g the model of the other player Adaptive best response Computationally efficient for realistic interactions Extendable to more the two agents Natural way to represent cultural and social factors in C and N MURI 14 Program Review September 10 2009 6 Output of POMDP The output is a policy matrix Policy Optimal action to take given current state observations and other s model At run time agent consults the matrix and takes appropriate action MURI 14 Program Review September 10 2009 7 Simplified Example Two agents negotiating Seller S POMDP Agent Buyer B Other player Single item negotiation Initially buyer at 0 price and seller at max 10 MURI 14 Program Review September 10 2009 8 Example State Space State composed of 2 parts Seller Type Buyer type Negotiation status current offers Agent types



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