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Coalitions Among Computationally Bounded Agents
 Artificial Intelligence
, 1997
"... This paper analyzes coalitions among selfinterested agents that need to solve combinatorial optimization problems to operate e ciently in the world. By colluding (coordinating their actions by solving a joint optimization problem) the agents can sometimes save costs compared to operating individua ..."
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Cited by 167 (24 self)
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This paper analyzes coalitions among selfinterested agents that need to solve combinatorial optimization problems to operate e ciently in the world. By colluding (coordinating their actions by solving a joint optimization problem) the agents can sometimes save costs compared to operating individually. A model of bounded rationality is adopted where computation resources are costly. It is not worthwhile solving the problems optimally: solution quality is decisiontheoretically traded o against computation cost. A normative, application and protocolindependent theory of coalitions among boundedrational agents is devised. The optimal coalition structure and its stability are signi cantly a ected by the agents ' algorithms ' performance pro les and the cost of computation. This relationship is rst analyzed theoretically. Then a domain classi cation including rational and boundedrational agents is introduced. Experimental results are presented in vehicle routing with real data from ve dispatch centers. This problem is NPcomplete and the instances are so large thatwith current technologyany agent's rationality is bounded by computational complexity. 1
Coalition Formation Processes with Belief Revision among BoundedRational SelfInterested Agents
 Journal of Logic and Computation
, 1999
"... This paper studies coalition formation among selfinterested agents that cannot make sidepayments. We show that alphacore stability reduces to analyzing whether some utility profile is maximal for all agents. We also show that strategy profiles that lead to the alphacore are a subset of Strong Nas ..."
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Cited by 31 (5 self)
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This paper studies coalition formation among selfinterested agents that cannot make sidepayments. We show that alphacore stability reduces to analyzing whether some utility profile is maximal for all agents. We also show that strategy profiles that lead to the alphacore are a subset of Strong Nash equilibria. This fact carries our alphacorebased stability results directly over to two other strategic solution concepts: Nash equilibrium and CoalitionProof Nash equilibrium.
Selecting Partners
 Proceedings of the Fourth International Conference on Autonomous Agents
, 2000
"... The goal of a rational agent is to maximize utility. We consider situations where a rational agent has to choose one of several contenders to enter into a partnership. We assume that the agent has a model of the likelihood of different outcomes and corresponding utilities for each such partnership. ..."
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Cited by 8 (0 self)
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The goal of a rational agent is to maximize utility. We consider situations where a rational agent has to choose one of several contenders to enter into a partnership. We assume that the agent has a model of the likelihood of different outcomes and corresponding utilities for each such partnership. Given a fixed, finite number of interactions, the problem is to choose a particular partner to interact with where the goal is to maximize the sum of utilities received from all the interactions. We develop a multinomial distribution based mechanism for partner selection and contrast its performance with other wellknown approaches which provide exact solution to this problem for infinite interactions.
Dynamic Properties of Multiagents based on a Mechanism of Loose Coalition
, 2000
"... . In this paper, we propose a method of coalition formation for assigning tasks to appropriate agents to improve the efficiency of multiagentsystems. To form a coalition, weintroduce subjective information to agents, which are the internal information of the agents. The subjective information re ..."
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Cited by 2 (0 self)
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. In this paper, we propose a method of coalition formation for assigning tasks to appropriate agents to improve the efficiency of multiagentsystems. To form a coalition, weintroduce subjective information to agents, which are the internal information of the agents. The subjective information reflect the agents' cooperativebehavior of the past. Next, we introduce loose coalition, a concept of a coalition of agents based on the subjective information. Using the agents' sense of values defined by their subjective information, eachagent can give priority to the loose coalitions to ask for the working status or to assign tasks. Thus loose coalitions with higher priority will be better cooperating candidates. Furthermore, loose coalitions enable agents to collect information (e.g. busyness of loose coalitions) for task assignment efficiently. Therefore, the agents on the system can decide its behavior properly, depending on the current status of the system, and thus the efficie...
E71  CS 516A Multiagent Systems Homework set 3: Auctions, Coalition Formation, and Internet Pricing
, 1996
"... (t CPU ) = $240 \Delta e \Gammat CPU + $400 c f2;3g (t CPU ) = $175 \Delta e \Gammat CPU + $419 c f1;3g (t CPU ) = $190 \Delta e \Gammat CPU + $400 c f1;2;3g (t CPU ) = $500 \Delta e \Gammat CPU + $595 What is the social welfare maximizing coalition structure? Is it stable according ..."
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(t CPU ) = $240 \Delta e \Gammat CPU + $400 c f2;3g (t CPU ) = $175 \Delta e \Gammat CPU + $419 c f1;3g (t CPU ) = $190 \Delta e \Gammat CPU + $400 c f1;2;3g (t CPU ) = $500 \Delta e \Gammat CPU + $595 What is the social welfare maximizing coalition structure? Is it stable according to the core solution concept (justify your answer)? How would these answers change if computation were free? 1 By the way, William Vickrey got the 1996 Nobel Prize in Economics. 4. Pricing the Internet under provider cooperation and collusion. Let there b