Knowledge discovery for adaptive negotiation agents in e-marketplaces

Intelligent software agents are promising in improving the effectiveness of e-marketplaces for e-commerce. Although a large amount of research has been conducted to develop negotiation protocols and mechanisms for e-marketplaces, existing negotiation mechanisms are weak in dealing with complex and dynamic negotiation spaces often found in e-commerce. This paper illustrates a novel knowledge discovery method and a probabilistic negotiation decision making mechanism to improve the performance of negotiation agents. Our preliminary experiments show that the probabilistic negotiation agents empowered by knowledge discovery mechanisms are more effective and efficient than the Pareto optimal negotiation agents in simulated e-marketplaces.

Air Gondwana : Using ICT to create an authentic learning environment to teach basic negotiation skills

In recent years greater emphasis has been placed by many Law Schools on teaching not only the substantive content of the law but also the skills needed for the practice of the law. Negotiation is one such skill. However, effective teaching of negotiation may be problematic in the context of large numbers of students studying in a variety of modes and often juggling other time commitments. This paper examines the Air Gondwana program, a blended learning environment designed to address these challenges. The program demonstrates that ICT can be used to create an authentic learning experience which engages and stimulates students.

Governance and negotiation : context revisited

Context is acknowledged as a significant feature of a negotiation. Background information about the relationship between the parties, available resources and organisational data are readily identifiable as key components of the contextual make-up of negotiations. However, information deriving from the broader setting of the negotiation may be less well-utilised or simply taken-for-granted in a negotiation. This paper suggests that this broader setting, discussed under the rubric of governance, is a critical facet of the context of negotiations. The paper explores the notion of governance and traces its relationship with negotiation. It then offers a framework that sets out the different governance approaches and allows for identifying and assessing potential negotiation strategies according to the dominant governance mode. It concludes that while a mix of governance approaches may be present in negotiations, identifying ‘ideal types’ or dominant governance modes assists in choosing appropriate strategies for successfully undertaking negotiations.

Optimal track access rights allocation for agent negotiation in an open railway market

In open railway access markets, a train service provider (TSP) negotiates with an infrastructure provider (IP) for track access rights. This negotiation has been modeled by a multi-agent system (MAS) in which the IP and TSP are represented by separate software agents. One task of the IP agent is to generate feasible (and preferably optimal) track access rights, subject to the constraints submitted by the TSP agent. This paper formulates an IP-TSP transaction and proposes a branch-and-bound algorithm for the IP agent to identify the optimal track access rights. Empirical simulation results show that the model is able to emulate rational agent behaviors. The simulation results also show good consistency between timetables attained from the proposed methods and those derived by the scheduling principles adopted in practice.

Intelligent negotiation behaviour model for an open railway access market

In an open railway access market, the provisions of railway infrastructures and train services are separated and independent. Negotiations between the track owner and train service providers are thus required for the allocation of the track capacity and the formulation of the services timetables, in which each party, i.e. a stakeholder, exhibits intelligence from the previous negotiation experience to obtain the favourable terms and conditions for the track access. In order to analyse the realistic interacting behaviour among the stakeholders in the open railway access market schedule negotiations, intelligent learning capability should be included in the behaviour modelling. This paper presents a reinforcement learning approach on modelling the intelligent negotiation behaviour. The effectiveness of incorporating learning capability in the stakeholder negotiation behaviour is then demonstrated through simulation.

Conflict resolution through negotiation in a railway open access market : a multi-agent system approach

Open access reforms to railway regulations allow multiple train operators to provide rail services on a common infrastructure. As railway operations are now independently managed by different stakeholders, conflicts in operations may arise, and there have been attempts to derive an effective access charge regime so that these conflicts may be resolved. One approach is by direct negotiation between the infrastructure manager and the train service providers. Despite the substantial literature on the topic, few consider the benefits of employing computer simulation as an evaluation tool for railway operational activities such as access pricing. This article proposes a multi-agent system (MAS) framework for the railway open market and demonstrates its feasibility by modelling the negotiation between an infrastructure provider and a train service operator. Empirical results show that the model is capable of resolving operational conflicts according to market demand.