Implementing an Agent Trade Server

An experimental server for stock trading autonomous agents is presented and made available, together with an agent shell for swift development. The server, written in Java, was implemented as proof-of-concept for an agent trade server for a real financial exchange.Comment: 14 pages, 7 figures, intended for B/W printin

Reasonable Goals

Assume that a number of autonomous agents are going to act in such a way that their respective goal states constitute a global plan. A main question that arises in this situation is whether there is such a plan at all, i.e. whether a solvable conflict prevails. In some sense. this means that the set of common goals is non-empty. Furthermore, if the agents are allowed to act in accordance with the result of some decision process, a situation may occur where subsets of their possible goal sets are consistent, but in actual fact the individual agents may nevertheless always terminate in states that are in conflict. We present a formal framework for the analysis of conflicts in sets of autonomous agents restricted in the sense that they can be described in a (first-order) language and by a transaction mechanism. This is also enriched by processes for evaluating decision situations given imprecise background information. The agent specifications are analysed with respect to a concept of consistency that requires the formulae of one specification together with a set of correspondence assertions to not restrict the models of another specification. i.e. the agent system does not essentially restrict the individual agents. The main emphasis is on the specifications being compatible with respect to reasonable probable states. i.e. states for which it is reasonable to assume that they eventually will be reached

Evaluating Decision Trees under Different Criteria

Based on our earlier results in decision theory, we demonstrate how decision trees can be integrated into a general framework for analysing decision situations with respect to different criteria, and suggest an evaluation rule taking into account all strategies, criteria, probabilities and utilities involved in the situations under consideration. A significant property of the framework is that it admits the representation of imprecise information at all stages. This information is modelled in sets of measures constrained by interval estimates. The strategies are then evaluated relative to different decision rules, e.g., a set of generalisations of the principle of admissibility. Decision situations are evaluated using fast algorithms developed particularly for solving these kinds of problems. The presented framework has been developed and used within a large-scale evaluation project at the Swedish National Rail Administration

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

The expected value of perfect information in unrepeatable decision-making

This paper reflects on the concept of the 'Expected Value of Perfect Information' (EVPI) and the procedure used to determine it. It is widely accepted that this value is the difference between the expected value when we have perfect information and the best expected value provided by alternatives. However, this difference often results in values that no rational decision-maker would accept. Here, we overcome this difficulty by defining the 'Value of Perfect Information for the Problem' (VPIP) where we consider not only the price of perfect information (EVPI) but also two additional parameters: the 'Loss to be Avoided' and 'The Most Favourable Payoff in the Worst Scenario'. In this way, we are able to obtain a more accurate value of the amount a decision-maker might be willing to pay for perfect information. We also seek to show that the indiscriminate employment of probability theory, based by definition on the repetition of the experiment, can be misleading in the case of decisions which, owing to the very nature of the problem, are unrepeatable

Imposing Security Constraints on Agent-Based Decision Support

The principle of maximising the expected utility has had a large influence on agent-based decision support. Even though this principle is often useful when evaluating a decision situation, it is not always the most rational decision rule and other candidates are worth considering. A decision making agent may want, for example, to exclude particular strategies which, in some sense, are too risky with respect to certain thresholds. A theory is presented for situations where a decision making agent, human or machine, has to choose between a finite set of strategies having access to a finite set of autonomous agents reporting their opinions on the strategies. The approach considers a decision problem with respect to the contents and the credibilities of the reports, and the main emphasis is on how to perform analyses in decision situations where the available information is vague or numerically imprecise. Keywords: Decision analysis, multi-agent systems, utility theory, uncertain reasoning..