An important form of negotiation is argumentation. This is the ability to argue and to persuade the other party to accept a desired agreement, to acquire or give information, to coordinate goals and actions, and to find and verify evidence. This is a key capability in negotiating with humans.
While automated negotiations between software agents can often exchange offers and counteroffers, humans require persuasion. This challenges the design of agents arguing with people, with the objective that the outcome of the negotiation will meet the preferences of the arguer agent.
CAP’s objective is to enable automated agents to argue and persuade humans.
To achieve this, we intend to develop the following key components:
1) The extension of current game theory models of persuasion and bargaining to more realistic settings, 2) Algorithms and heuristics for generation and evaluation of arguments during negotiation with people, 3) Algorithms and heuristics for managing inconsistent views of the negotiation environment, and decision procedures for revelation, signalling, and requesting information, 4) The revision and update of the agent’s mental state and incorporation of social context, 5) Identifying strategies for expressing emotions in negotiations, 6) Technology for general opponent modelling from sparse and noisy data.
To demonstrate the developed methods, we will implement two training systems for people to improve their interviewing capabilities, and for training negotiators in inter-culture negotiations.
CAP will revolutionise the state of the art of automated systems negotiating with people. It will also create breakthroughs in the research of multi-agent systems in general, and will change paradigms by providing new directions for the way computers interact with people.
Fields of science
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