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Argumentation-based negotiation Rahwan, Ramchurn, Jennings, McBurney, Parsons and Sonenberg, 2004 Presented by Jean-Paul Calbimonte
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Negotiation? interactions Mutually acceptable agreement Scarce resources: time money services, anything Automate! Approaches Game-theoretic analysis Heuristic-based Argumentation-based
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Classical negotiation Agents exchange proposals Limitations Computational constraints (analytical game-theoretic) Sub-optimal approximate outcomes (heuristic-based) Require extensive empirical evaluation (heuristic) No additional info exchanged Agents preferences need to be fully characterized Lack information to evaluate/compare proposals Fixed agents preferences proposals
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Argumentation-based negotiation Exchange additional information Argument: piece of information Reasons for refusal Justification of a proposal Change object of negotiation Promise rewards Emit threats Components External elements Own elements
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Non-ABN Elements Proposal Database Locution Interpretation Opponent/ Environment Model & Mental Attitudes Proposal Evaluation Generation Locution Generation Outgoing Locutions Incoming Locutions query / update query Proposal content propose / accept / reject
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ABN elements Proposal Database Locution Interpretation Opponent/ Environment Model & Mental Attitudes Proposal Evaluation Generation Locution Generation Outgoing Locutions Incoming Locutions query / update query Proposal content propose / accept / reject Argument Interpretation Argument Generation Argument Selection Argument content
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External elements Communication language Interaction between agents Locutions, utterances Domain language Concepts & meta information about the world Negotiation protocol Conventions, rules of dialogue: who is allowed to say what? Information stores Keep track of utterances, behavior Internal, external repositories accept reject propose reject(b,a,Price=$200 and Item=palmI30)
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Communication & domain language Rich languages, clear semantics request(j,i, Do(i,α),Do(i,α) Do(j,β)) Express preferences Express plans, intentions Standardized domain languages Heterogeneous environments Semantic & Syntactic interoperability i j Do(i, α ) Do(i, α ) Do(j, β )
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Negotiation protocol Interaction, dialogue rules Finite state machines Dialogue games Enforce fairness, rule consistency Termination, avoid infinite interactions Enusre guaranteed success Endless interactions? Conformance checking Utterance acceptable? Admission to negotiation
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Information stores Keep track of past utterances Internal Centralized Commitment stores Promise to initiate, execute, maintain Defend claims Commitment rules Retract from commitments Allowed Forced
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Elements of ABN agents Argument and proposal evaluation Incoming arguments, update mental state Argument and proposal generation Generate set of candidates Argument selection Choosing a candidate
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Argument and proposal evaluation Objective considerations Argument acceptability, evaluation Subjective considerations Consider own preferences and motivations Combine objective + subjective Combine belief arguments and value arguments Provide unified framework for evaluation of goal, belief, plan, etc. Probabilistic evaluation
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Argument and proposal generation Generate candidate arguments Increase, maximize utilities Rule-based generation Characterization of possible arguments Take into account other factors: Protocol Authority Expectations Utility Honesty
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Argument selection Choose candidate argument ABN strategy Use strength order: common practice, appeal, self interest, promise,threat Use factors: trust-utility I need this but I don’t trust you Opponents information, Probabilistic models, uncertainty Learning techniques Consider behavior & mental model behind
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Conlcusions ABN: increasing importance Enables rational dialogue Richer models of negotiation How agents use objective reasoning to reach subjective goals? Challenges Communicative rationality Social influence Trust Mediated negotiation Complexity
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