Software Agents for Automated Transaction Negotiations: Implementation and Evaluation

Software agents have the potential to serve as effective surrogates of humans in conducting business transactions in an electronic commerce environment. The reported proceeding research aims to evaluate the performance of software agents in automated transaction negotiations. As part of this research, agents are being built using IBM aglets, and their performance evaluation within various experimental settings is currently underway. Research Overview and Motivation The proliferation of newly emerging Internet and WWW technologies has created innovative and exciting multidisciplinary research opportunities in information systems/technology. Software agent is one research area that has received fast-growing interests and attention from researchers and practitioners alike. In an era characterized by new business models established and prevalent in electronic marketspace, examination of software agents for automated transaction negotiations is essential and timely. Software agents are software entities that function continuously and autonomously in a particular environment, often inhabited by other agents and processes (Shoham 1997). In spite of conceivable differences in role and sophistication, software agents by and large share several common characteristics, including reactivity, autonomy, collaborative behavior, embedded knowledge and inferential capability, mobility, and adaptability (Etzioni and Weld 1995, Franklin and Graesser 1996). Software agents can be programmed to incorporate task domain-specific knowledge, problemsolving capabilities, and human preferences and reasoning (Shoham 1997). When properly designed and deployed, these agents may alleviate or even remove such common human processing constraints as information overload, search-space limitation and bounded rationality (Maes 1994). Because of the discussed and other desired characteristics, software agents exhibit promising potentials in serving as effective surrogates of their human principals in automated transaction negotiations within common business contexts (Maes P., R. H. Guttman, and A. G. Moukas, 1999). Negotiation is an essential aspect of business activities. In many business transactions, negotiation proceeds in an iterative manner and usually is a tedious and a timeconsuming process through which market participants may or may not reach an agreement necessary for completing a transaction. Automating such negotiations in an EC environment is highly desirable. A system called INSPIRE is a non-agent based system that supports negotiations on the world wide web (Kersten G. E., and S. J. Noronha, 1999). Investigations of agent-based negotiations by previous research are limited. Kraus (1996) has presented a theoretical model for negotiations requiring multiple encounters, each of which takes place with incomplete information. Each agent maximizes his or her utility at each phase of the negotiation process, leading to equilibrium reached at each phase. Sandholm and Lesser have modeled different coalition formations among bounded rational agents (Sandholm and Lesser 1995). Chavez and Maes (1996) have taken a simplistic approach to develop a system (Kasbah) for automated selling and buying by software agents over the Internet. In their system, software agents deterministically exchange offer parameter (i.e., price) without learning from or adapting to the negotiation process, based on some embedded programmed price function defined over a finite time window. In the Bazaar project, Zeng and Sycara consider negotiations as a sequential decision process (Zeng and Sycara 1996). Software agents in Bazaar use Bayesian probability updates to learn from negotiations. Oliver (1996) has proposed a genetic algorithms (GA) approach where market participants play strategies against each other, thereby resulting in improved strategies in subsequent generations. Expanding the work by Oliver, Dworman, Kimbrough and Liang developed appropriate GAs to discover high-quality negotiation strategies in situations where multiple coalitions are formed (Dworman, Kimbrough and Liang 1996). Collectively, the literature suggests that acceptance of software agents by human principals, at a minimum, requires their encompassing the preference and reasoning capabilities of human users and demonstrating desired utilities for achieving a performance level comparable or superior to that commonly accomplished by human users. In response to the limited previous research on agentbased automated transaction negotiation support and evaluation, we have designed and developed a multi-agent system, whose performance evaluation is currently underway. Specifically, we use controlled experiments to evaluate the system in such areas as market efficiency and comparative performance of individual agents (with