Dates: July 1 2011- June 30 2016

Funding: Advanced ERC

Description:

The goal of the CAP project is to develop models and algorithms that enable automated systems to argue proficiently when negotiating with people. The arguer agent would like the outcome of the negotiation to unfold according to its own preferences. There are several ways to argue with and persuade people, and we considered several approaches and developed and tested several algorithms for various aspects of negotiations. Our goal is to enable the agents to negotiate in more natural ways (e.g., using natural language rather than menu driven) and reach better agreements according to its specifications.

Notable agents that we developed so far include NegoChat agent, the first negotiation agent that successfully negotiates with people in natural language; the Personality Adaptive Learning (PAL) agent that negotiates with people from different cultures; SAP, a Social agent for Advice Provision that generates advice according to a social model that we developed; equilibrium agents that follow strategies that are in equilibrium and Sigmoid Acceptance Learning Agent (SIGAL), that uses a decision-theoretic approach to negotiate in revelation games, which is based on a model of how humans make decisions in the game.

Research and technological achievements

People:

Prof. Sarit Kraus

Amos Azaria, Moshe Bitan, Dr. Avi Rosenfeld, Dr. Noam Hazon

Programmers:

Ariel Roth, Amir Gottlieb, Osnat Drein