On Wong

An evolutionary learning approach for adaptive negotiation agents

Developing effective and efficient negotiation mechanisms for real‐world applications such as e‐business is challenging because negotiations in such a context are characterized by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This article illustrates our adaptive negotiation agents, which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA‐based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism that guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real‐world applications.

Handheld devices for applications using dynamic multimedia data

Growing demand for ubiquitous and pervasive computing has triggered a sharp rise in handheld device usage. At the same time, dynamic multimedia data has become accepted as core material which many important applications depend on, despite intensive costs in computation and resources. This paper investigates the suitability and constraints of using handheld devices for such applications. We firstly analyse the capabilities and limitations of current models of handheld devices and advanced features offered by next generation models. We then categorise these applications and discuss the typical requirements of each class. Important issues to be considered include data organisation and management, communication, and input and user interfaces. Finally, we briefly discuss future outlook and identify remaining areas for research.

Towards genetically optimised responsive negotiation agents

Real-world negotiations are characterised by combinatorially complex negotiation spaces, tough deadlines, bounded agent rationality, very limited information about the opponents, and volatile negotiator preferences. Classical negotiation models fail to address most of these issues. This work illustrates our practical negotiation agents which are empowered by an effective and efficient genetic algorithm to deal with complex, incomplete, and dynamic negotiation spaces arising in real-world applications. Initial experiment demonstrates that our genetically optimised adaptive negotiation agents outperform a theoretically optimal negotiation model when time pressure exists. Our research work opens the door to the development of responsive and adaptive negotiation agents for real-world applications.

Bio-workflows with BizTalk: using a commercial workflow engine for eScience

Workflow is an important enabling technology for eScience. Research into workflow systems for eScience has yielded several specialized workflow engines. In this paper we investigate the nature of scientific workflow and describe how an existing business workflow engine, Microsofts BizTalk, can be adapted to support scientific workflow, and some advantages of doing this. We demonstrate how BizTalk is able to support scientific workflow through the implementation of two real bioinformatics workflows. These workflows are enacted through a novel Web based interface making them widely accessible

Mining Trading Partners' Preferences for Efficient Multi-Issue Bargaining in E-Business

Classical negotiation models are weak in supporting real-world business negotiations because these models often assume that the preference information of each negotiator is made public. Although parametric learning methods have been proposed for acquiring the preference information of negotiation opponents, these methods suffer from the strong assumptions about the specific utility function and negotiation mechanism employed by the opponents. Consequently, it is difficult to apply these learning methods to the heterogeneous negotiation agents participating in e-marketplaces. This paper illustrates the design, development, and evaluation of a nonparametric negotiation knowledge discovery method which is underpinned by the well-known Bayesian learning paradigm. According to our empirical testing, the novel knowledge discovery method can speed up the negotiation processes while maintaining negotiation effectiveness. To the best of our knowledge, this is the first nonparametric negotiation knowledge discovery method developed and evaluated in the context of multi-issue bargaining over e-marketplaces.

GPFlow: An Intuitive Environment for Web Based Scientific Workflow

Increasingly scientists are using collections of software tools in their research. These tools are typically used in concert, often necessitating laborious and error prone manual data reformatting and transfer. We present an intuitive workflow environment to support scientists with their research. The workflow, GPFlow, wraps legacy tools, presenting a high level, interactive Web based frontend to scientists. The workflow backend is realized by a commercial grade workflow engine (BizTalk). The workflow model is inspired by spreadsheets and is novel in its support for an intuitive method of interaction as required by many scientists, for example, bioinformaticians. We apply GPFlow to two bioinformatics experiments and demonstrate its flexibility and simplicity

Mining Negotiation Knowledge for Adaptive Negotiation Agents in e-Marketplaces

By increasing the degree and sophistication of automation, e-marketplaces will become much more efficient and transparent, and hence more widely adopted by organizations. Negotiation is one of main activities conducted in e-marketplaces, and adaptive negotiation agents can be applied to improve the effectiveness of B2B e-marketplaces. Classical negotiation models have limited use in modern e-marketplaces because these models often assume that complete information about the negotiation spaces is available. This paper illustrates the design and development of adaptive negotiation agents for e-marketplaces. These agents are empowered by the Bayesian learning mechanisms so that they can gradually acquire negotiation knowledge based on their previous encounters with the opponents. Our preliminary experiment shows that the proposed probabilistic negotiation decision making mechanism and the associated data mining approach is effective and efficient in simulated e-marketplaces