Intelligent agents for lawyers

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Proactive computing in process monitoring:Information agents for operator support

While automation systems can track thousands of measurements it is still up to human process operators to determine the operational situation of the controlled process, particularly in abnormal situations. To fully exploit the computing power of embedded processors and to release humans from simple data harvesting activities, the concept of proactive computing tries to exploit the strengths of both man and machine. Proactive features can be implemented using intelligent agent technology, enabling humans to move from simple interaction with computers into supervisory tasks. Autonomous information agents can handle massive amounts of heterogeneous data. They perform tedious tasks of information retrieving, combining and monitoring on the behalf of their users. This paper presents a multi-agent-based architecture for process automation, which aims to support process operators in their monitoring activities. The approach is tested with a scenario inspired by a real-world industrial challenge. (24 refs.

Designing Self-Modifying Agents

Agents need to be able to adapt to changes in their environment. One way to achieve this, is to provide agents with the ability of self-modification. Self-modification requires reflection and strategies with which new knowledge can be acquired, a necessary condition for creativity. This paper describes a knowledge-level model for the design of self-modifying agents and explores the feasibility of automatically designing self-modifying agents

Heterogeneous Active Agents

Over the years, many different agent programming languages have been proposed. In this paper, we propose a concept called Agent Programs using which, the way an agent should act in various situations can be declaratively specified by the creator of that agent. Agent Programs may be built on top of arbitrary pieces of software code and may be used to specify what an agent is obliged to do, what an agent may do, and what an agent may not do. In this paper, we define several successively more sophisticated and epistemically satisfying declarative semantics for agent programs, and study the computation price to be paid (in terms of complexity) for such epistemic desiderata. We further show that agent programs cleanly extend well understood semantics for logic programs, and thus are clearly linked to existing results on logic programming and nonmonotonic reasoning. Last, but not least, we have built a simulation of a Supply Chain application in terms of our theory, building on top of commercial software systems such as Microsoft Access and ESRI's Map Object. (Also cross-referenced as UMIACS-TR-98-15

Re-design of compositional systems

Treur, J. [Promotor]Brazier, F.M.T. [Copromotor

Multi-agent integration of information gathering and decision support

We are investigating techniques for developing distributed and adaptive collections of information agents that coordinate to retrieve, filter and fuse information relevant to the user, task and situation. In our system of agents, information gathering is seamlessly integrated with decision support. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for structuring agents. The system has three types of agents: Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user’s tasks. Task agents help users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneous collection of information sources. We have implemented this system framework and are developing collaborating agents in diverse complex real world tasks, such as organizational decision making, and financial portfolio management.