Intelligent Software Agents

Brenner, Walter; Zarnekow, Rüdiger; Wittig, Hartmut

doi:10.1007/978-3-642-80484-7

Cited by 150 publications

(43 citation statements)

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“…Nwana [13] divided agents into collaborative agents, learning agents, interface agents, and smart agents. Brenner [5] Snadholm and Lesser [17] found that cooperative agents often exist and perform tasks inside an enterprise such as production planning and meeting scheduling. A competitive agent will not give in unless it can receive comparable compensation during negotiation because it cares only its own benefit and is least concerned with joint benefit.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Money Left on Table for Construction Procurement Negotiation

Dzeng

2004

Proceedings of the 21st International Symposium on Automation and Robotics in Construction

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Most contractors negotiate with suppliers for their construction procurement according to negotiators' experiences instead of extensive exploration of negotiable options and negotiators' preferences. Consequently, negotiators often reach suboptimal agreements, and leave money on the table. This research developed an agentbased computer system that find optimal agreements using the genetic algorithm, compared the results with the actual contractual agreements, and determine the "money" left on the table in the traditional construction negotiation.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring the Money Left on Table for Construction Procurement Negotiation

Dzeng

2004

Proceedings of the 21st International Symposium on Automation and Robotics in Construction

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“…The intelligent agents and multi-agent systems (MASs) technologies (Brenner et al, 1998;Wooldridge and Jennings, 1994) have provided new solutions to deal with complex problems in several domains such as data mining, e-commerce, medicine, stock market, intelligent manufacturing control, simulation of complex societies, etc. The MAS techniques provide a natural way to design and implement decentralized and distributed systems where each element, or agent, can take its own control decisions and modify its behaviour using the environment information or its previous experience.…”

Section: Introductionmentioning

confidence: 99%

Roboskeleton: An architecture for coordinating robot soccer agents

Camacho

Fernández

Rodelgo

2006

Engineering Applications of Artificial Intelligence

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SkeletonAgent is an agent framework whose main feature is to integrate different artificial intelligent skills, like planning or learning, to obtain new behaviours in a multi agent environment. This framework has been previously instantiated in a deliberative domain (electronic tourism), where planning was used to integrate Web information in a tourist plan. RoboSkeleton results from the instantiation of the same framework, SkeletonAgent, in a very different domain, the robot soccer. This paper shows how this architecture is used to obtain collaborative behaviours in a reactive domain. The paper describes how the different modules of the architecture for the robot soccer agents are designed, directly showing the flexibility of our framework. r

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“…4 Every MAS uses the agent concept, which is extensively described in several publications. 5 The main characteristics of a MAS can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%