Eclipse-based prometheus design tool

The Prometheus Design Tool (PDT) is a graphical tool that is used to design a Multi-Agent System following the Prometheus Methodology. This paper describes the latest version of PDT which is now integrated into the Eclipse platform, enabling the users to accomplish the full development life-cycle of an agent-oriented application in one IDE and also inherit the rich set of product development features that Eclipse provides. This version of PDT also aims to support simpler integration with tools from other AOSE methodologies where appropriate

Health conversational system based on contextual matching of community-driven question-answer pairs

More and more people are turning to the World Wide Web for learning and sharing information about their health us- ing search engines, forums and question answering systems. In this demonstration, we look at a new way of deliver- ing health information to the end-users via coherent con- versations. The proposed conversational system allows the end-users to vaguely express and gradually refine their in- formation needs using only natural language questions or statements as input. We provide example scenarios in this demonstration to illustrate the inadequacies of current de- livery mechanisms and highlight the innovative aspects of the proposed conversational system

Automated unit testing intelligent agents in PDT

The Prometheus Design Tool (PDT) is an agent development tool that supports the Prometheus design methodology and includes features like automated code generation. We enhance this tool by adding a feature that allows the automated unit testing of agents that are built from within PDT

Simplifying the development of intelligent agents

Intelligent agents is a powerful Artificial Intelligence technology which shows considerable promise as a new paradigm for mainstream software development. However, despite their promise, intelligent agents are still scarce in the market place. A key reason for this is that developing intelligent agent software requires significant training and skill: a typical developer or undergraduate struggles to develop good agent systems using the Belief Desire Intention (BDI) model (or similar models). This paper identifies the concept set which we have found to be important in developing intelligent agent systems and the relationships between these concepts. This concept set was developed with the intention of being clearer, simpler, and easier to use than current approaches.We also describe briefly a (very simplified) example from one of the projects we have worked on (RoboRescue), illustrating the way in which these concepts are important in designing and developing intelligent software agents

Measuring plan coverage and overlap for agent reasoning

In Belief Desire Intention (BDI) agent systems it is usual for goals to have a number of plans that are possible ways of achieving the goal, applicable in di erent situations, usually captured by a context condition. In Agent Oriented Software Engineering it has been suggested that a designer should be conscious of whether a goal has complete coverage, that is, is there some plan that is applicable for every situation. Similarly a designer should be conscious of overlap, that is, for a given goal, are there situations where more than one plan could be applicable for achieving that goal. In this paper we further develop these notions in two ways, and then describe how they can be used both in agent reasoning and agent system development. Firstly we replace the boolean value for basic coverage and overlap with numerical measures, and explain how these may be calculated. Secondly we describe a measure that combines these basic measures, with the characteristics of the coverage/overlap in the goal-plan tree below a given goal. We then describe how these domain independent measures can be used for both plan selection and intention selection, as well as for guidance in agent system development

Contextual question answering for the health domain

Studies have shown that natural language interfaces such as question answering and conversational systems allow information to be accessed and understood more easily by users who are unfamiliar with the nuances of the delivery mechanisms (e.g., keyword-based search engines) or have limited literacy in certain domains (e.g., unable to comprehend health-related content due to terminology barrier). In particular, the increasing use of the web for health information prompts us to reexamine our existing delivery mechanisms. We present enquireMe, which is a contextual question answering system that provides lay users with the ability to obtain responses about a wide range of health topics by vaguely expressing at the start and gradually refining their information needs over the course of an interaction session using natural language. enquireMe allows the users to engage in 'conversations' about their health concerns, a process that can be therapeutic in itself. The system uses community-driven question-answer pairs from the web together with a decay model to deliver the top scoring answers as responses to the users' unrestricted inputs. We evaluated enquireMe using benchmark data from WebMD and TREC to assess the accuracy of system-generated answers. Despite the absence of complex knowledge acquisition and deep language processing, enquireMe is comparable to the state-of-the-art question answering systems such as START as well as those interactive systems from TREC

Experiences in teaching computing theory via aspects of problem-based learning

Computing Theory is a compulsory subject that many computer science students find difficult, and for which there is a wide range of abilities and backgrounds amongst students. In this paper we describe the evolution of this subject at our university over the past decade, which has resulted in a much more student-centred approach to learning. This has not only enhanced the learning experience of the students but also has simplified the development of teaching modes and resources involving on-line replacements for lectures

Creating adaptive and individual personalities in many characters without hand crafting behaviours

Believable characters significantly increase the immersion of users or players in interactive applications. A key component of believable characters is their personality, which has previously been implemented statically using the time consuming task of hand-crafting individuality for each character. Often personality has been modeled based on theories that assume behavior is the same regardless of situation and environment. This paper presents a simple affective and cognitive framework for interactive entertainment characters that allows adaptation of behavior based on the environment and emotions. Different personalities are reflected in behavior preferences which are generated based on individual experience. An initial version of the framework has been implemented in a simple scenario to explore which parameters have the greatest effect on agent diversity

Detecting and avoiding interference between goals in intelligent agents

Pro-active agents typically have multiple simultaneous goals. These may interact with each other both positively and negatively. In this paper we provide a mechanism allowing agents to detect and avoid a particular kind of negative interaction where the effects of one goal undo conditions that must be protected for successful pursuit of another goal. In order to detect such interactions we maintain summary information about the definite and potential conditional requirements and resulting effects of goals and their associated plans. We use these summaries to guard protected conditions by scheduling the execution of goals and plan steps. The algorithms and data structures developed allow agents to act rationally instead of blindly pursuing goals that will conflict

Automated testing for intelligent agent systems

This paper describes an approach to unit testing of plan based agent systems, with a focus on automated generation and execution of test cases. Design artefacts, supplemented with some additional data, provide the basis for specification of a comprehensive suite of test cases. Correctness of execution is evaluated against a design model, and a comprehensive report of errors and warnings is provided to the user. Given that it is impossible to design test suites which execute all possible traces of an agent program, it is extremely important to thoroughly test all units in as wide a variety of situations as possible to ensure acceptable behaviour. We provide details of the information required in design models or related data to enable the automated generation and execution of test cases. We also briefly describe the implemented tool which realises this approach