A Survey on Neural Networks in Automated Negotiations

2011 ◽  
pp. 1524-1529 ◽  
Author(s):  
Ioannis Papaioannou ◽  
Ioanna Roussaki ◽  
Miltiades Anagnostou
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
Research Field ◽  
A Priori Knowledge ◽  
Automated Negotiation ◽  
Multilateral Negotiation ◽  
The Third ◽  
Automated Negotiations ◽  
Single Instance ◽  
Or Modelling

Automated negotiation is a very challenging research field that is gaining momentum in the e-business domain. There are three main categories of automated negotiations, classified according to the participating agent cardinality and the nature of their interaction (Jennings, Faratin, Lomuscio, Parsons, Sierra, & Wooldridge, 2001): the bilateral, where each agent negotiates with a single opponent, the multi-lateral which involves many providers and clients in an auction-like framework and the argumentation/persuasion-based models where the involving parties use more sophisticated arguments to establish an agreement. In all these automated negotiation domains, several research efforts have focused on predicting the behaviour of negotiating agents. This work can be classified in two main categories. The first is based on techniques that require strong a-priori knowledge concerning the behaviour of the opponent agent in previous negotiation threads. The second uses mechanisms that perform well in single-instance negotiations, where no historical data about the past negotiating behaviour of the opponent agent is available. One quite popular tool that can support the latter case is Neural Networks (NNs) (Haykin, 1999). NNs are often used in various real world applications where the estimation or modelling of a function or system is required. In the automated negotiations domain, their usage aims mainly to enhance the performance of negotiating agents in predicting their opponents’ behaviour and thus, achieve better overall results on their behalf. This paper provides a survey of the most popular automated negotiation approaches that are using NNs to estimate elements of the opponent’s behaviour. The rest of this paper is structure as follows. The second section elaborates on the state of the art bilateral negotiation frameworks that are based on NNs. The third section briefly presents the multilateral negotiation solutions that exploit NNs. Finally, in the last section a brief discussion on the survey is provided.

A Survey on Neural Networks in Automated Negotiations

2009 ◽  
pp. 2360-2366
Author(s):  
Ioannis Papaioannou ◽  
Ioanna Roussaki ◽  
Miltiades Anagnostou
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
Research Field ◽  
A Priori Knowledge ◽  
Automated Negotiation ◽  
Multilateral Negotiation ◽  
The Third ◽  
Automated Negotiations ◽  
Single Instance ◽  
Or Modelling

Automated negotiation is a very challenging research field that is gaining momentum in the ebusiness domain. There are three main categories of automated negotiations, classified according to the participating agent cardinality and the nature of their interaction (Jennings, Faratin, Lomuscio, Parsons, Sierra, & Wooldridge, 2001): the bilateral, where each agent negotiates with a single opponent, the multi-lateral which involves many providers and clients in an auction-like framework and the argumentation/persuasion-based models where the involving parties use more sophisticated arguments to establish an agreement. In all these automated negotiation domains, several research efforts have focused on predicting the behaviour of negotiating agents. This work can be classi- fied in two main categories. The first is based on techniques that require strong a-priori knowledge concerning the behaviour of the opponent agent in previous negotiation threads. The second uses mechanisms that perform well in single-instance negotiations, where no historical data about the past negotiating behaviour of the opponent agent is available. One quite popular tool that can support the latter case is Neural Networks (NNs) (Haykin, 1999). NNs are often used in various real world applications where the estimation or modelling of a function or system is required. In the automated negotiations domain, their usage aims mainly to enhance the performance of negotiating agents in predicting their opponents’ behaviour and thus, achieve better overall results on their behalf. This paper provides a survey of the most popular automated negotiation approaches that are using NNs to estimate elements of the opponent’s behaviour. The rest of this paper is structure as follows. The second section elaborates on the state of the art bilateral negotiation frameworks that are based on NNs. The third section briefly presents the multilateral negotiation solutions that exploit NNs. Finally, in the last section a brief discussion on the survey is provided.

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