Coalition formation with dynamically changing externalities. (May 2020)

• Record Type: Journal Article
• Authors: Sklab, Youcef; Aknine, Samir; Shehory, Onn; Tari, Abdelkamel
• Is Part Of: Engineering applications of artificial intelligence. Volume 91(2020)
• Publication Date: 2020-05
• Subjects: Coalition formation -- Coalitions externalities -- Dynamically changing externalities -- Task dependencies -- Multi-agent negotiation; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103577
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: We consider multiple self-interested bounded-rational agents each of which has a goal it needs to achieve. Goals are achievable by executing a set of interdependent tasks. Some tasks exhibit time dependencies and may require sequential execution. For each agent, there may be several alternative sets of tasks that can achieve the goal. Execution of alternatives, may be more beneficial when done by a group of agents and not by a single agent. To jointly achieve goals, agents may form interdependent coalitions. Such coalition formation is computationally intractable. We nevertheless seek a practical solution that is not necessarily optimal yet acceptable by the agents. A solution where agents examine only coalitions in which they are members is inapplicable, as externalities are a major factor given task interdependencies. In this paper we study this coalition formation problem. We describe the problem and introduce a novel Multi-lateral Negotiation Protocol ( MNP ) that solves it by forming interdependent coalitions. We allow agents to heuristically make gradual concessions, revise their proposals and converge on specific alternatives, and nevertheless increase their expected gains.