Multi-objective variable elimination for collaborative graphical games
نویسندگان
چکیده
In this paper we propose multi-objective variable elimination (MOVE), an efficient solution method for multi-objective collaborative graphical games (MO-CoGGs), that exploits loose couplings. MOVE computes the convex coverage set, which can be much smaller than the Pareto front. In an empirical study, we show that MOVE can tackle multi-objective problems much faster than methods that do not exploit loose couplings.
منابع مشابه
Convex Coverage Set Methods for Multi-Objective Collaborative Decision Making (Doctoral Consortium)
My research is aimed at finding efficient coordination methods for multi-objective collaborative multi-agent decision theoretic planning. Key to coordinating efficiently in these settings is exploiting loose couplings between agents. We proposed two algorithms for the case in which the agents need to make a single collective decision: convex multiobjective variable elimination (CMOVE) and varia...
متن کاملComputing Convex Coverage Sets for Multi-objective Coordination Graphs
Many real-world decision problems require making trade-offs between multiple objectives. However, in some cases, the relative importance of the objectives is not known when the problem is solved, precluding the use of singleobjective methods. Instead, multi-objective methods, which compute the set of all potentially useful solutions, are required. This paper proposes new multiobjective algorith...
متن کاملConvex coverage set methods for multi-objective collaborative decision making
My research is aimed at finding efficient coordination methods for multi-objective collaborative multi-agent decision theoretic planning. Key to coordinating efficiently in these settings is exploiting loose couplings between agents. We proposed two algorithms for the case in which the agents need to make a single collective decision: convex multiobjective variable elimination (CMOVE) and varia...
متن کاملExploiting Agent and Type Independence in Collaborative Graphical Bayesian Games
Efficient collaborative decision making is an important challenge for multiagent systems. Finding optimal joint actions is especially challenging when each agent has only imperfect information about the state of its environment. Such problems can be modeled as collaborative Bayesian games in which each agent receives private information in the form of its type. However, representing and solving...
متن کاملStructured models for multi-agent interactions*
The traditional representations of games using the extensive form or the strategic (normal) form obscure much of the structure that is present in real-world games. In this paper, we propose a new representation language for general multi-player noncooperative games multi-agent influence diagrams (MAIDs). This representation extends graphical models for probability distributions to a multi-agent...
متن کامل