Software Agents

being used, and touted, for applications as diverse as personalised information management, electronic commerce, interface design, computer games, and management of complex commercial and industrial processes. Despite this proliferation, there is, as yet, no commonly agreed upon definition of exactly what an agent is — Smith et al. (1994) define it as “a persistent software entity dedicated to a specific purpose”; Selker (1994) takes agents to be “computer programs that simulate a human relationship by doing something that another person could do for you”; and Janca (1995) defines an agent as “a software entity to which tasks can be delegated”. To capture this variety, a relatively loose notion of an agent as a self-contained program capable of controlling its own decision making and acting, based on its perception of its environment, in pursuit of one or more objectives will be used here. Within the extant applications, three distinct classes of agent can be identified. At the simplest level, there are “gopher ” agents, which execute straightforward tasks based on pre-specified rules and assumptions (eg inform me when the share price deviates by 10 % from its mean position or tell me when I need to reorder stock items). The next level of sophistication involves “service performing” agents, which execute a well defined task at the request of a user (eg find me the cheapest flight to Paris or arrange a meeting with the managing director some day next week). Finally, there are “predictive ” agents, which volunteer information or services to a user, without being explicitly asked, whenever it is deemed appropriate (eg an agent may monitor newsgroups on the INTERNET and return discussions that it believes to be of interest to the user or a holiday agent may inform its user that a travel firm is offering large discounts on holidays to South Africa knowing that the user is interested in safaris). Common to all these classes are the following key hallmarks of agenthoo

An Embodied question answering system for use in the treatment of eating disorders

This paper presents work in progress on implementing an embodied question answering system, Dr. Cecilia, in the form of a virtual caregiver, for use in the treatment of eating disorders. The rationale for the system is grounded in one of the few effective treatments for anorexia and bulimia nervosa. The questions and answers database is encoded using natural language, and is easily updatable by human caregivers without any technical expertise. Matching of users' questions with database entries is performed using a weighted and normalized n-gram similarity function. In this paper we give a comprehensive background to and an overview of the system, with a focus on aspects pertaining to natural language processing and user interaction. The system is currently only implemented for Swedish

Machine Learning of User Profiles: Representational Issues

As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles that accurately capture user interest with minimum user interaction. The research described here focuses on the importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could be drawn from a thesaurus. Our experiments, conducted in the context of a content-based profiling system for on-line newspapers on the World Wide Web (the IDD News Browser), demonstrate the importance of a generalization hierarchy and the promise of combining natural language processing techniques with machine learning (ML) to address an information retrieval (IR) problem.Comment: 6 page

Agent-Based Team Aiding in a Time Critical Task

In this paper we evaluate the effectiveness of agent-based aiding in support of a time-critical team-planning task for teams of both humans and heterogeneous software agents. The team task consists of human subjects playing the role of military commanders and cooperatively planning to move their respective units to a common rendezvous point, given time and resource constraints. The objective of the experiment was to compare the effectiveness of agent-based aiding for individual and team tasks as opposed to the baseline condition of manual route planning. There were two experimental conditions: the Aided condition, where a Route Planning Agent (RPA) finds a least cost plan between the start and rendezvous points for a given composition of force units; and the Baseline condition, where the commanders determine initial routes manually, and receive basic feedback about the route. We demonstrate that the Aided condition provides significantly better assistance for individual route planning and team-based re-planning