Keith Decker

Agent-Oriented Software Engineering

The ATAL workshops focus on the links between the theory and practice of intelligent agents. One aspect of this, which is steadily growing in importance, is the idea of agent technology as a software engineering paradigm. Previous ATAL workshops have had special tracks on programming languages for agent-oriented development, and methodologies for agent system development. ATAL-99 aims to build on this experience by focussing on the wider issues of agents as a software engineering paradigm.

Distributed intelligent agents

In Retsina, the authors have developed a distributed collection of software agents that cooperate asynchronously to perform goal-directed information retrieval and integration for supporting a variety of decision-making tasks. Examples for everyday organizational decision making and financial portfolio management demonstrate its effectiveness.

Selecting uncertainty calculi and granularity: an experiment in trading-off precision and complexity

The management of uncertainty in expert systems has usually been left to ad hoc representations and rules of combinations lacking either a sound theory or clear semantics. The objective of this paper is to establish a theoretical basis for defining the syntax and semantics of a small subset of calculi of uncertainty operating on a given term set of linguistic statements of likelihood. Each calculus will be defined by specifying a negation, a conjunction and a disjunction operator. Families of Triangular norms and conorms will provide the most general representations of conjunction and disjunction operators. These families provide us with a formalism for defining an infinite number of different calculi of uncertainty. The term set will define the uncertainty granularity, i.e. the finest level of distinction among different quantifications of uncertainty. This granularity will limit the ability to differentiate between two similar operators. Therefore, only a small finite subset of the infinite number of calculi will produce notably different results. This result is illustrated by an experiment where nine different calculi of uncertainty are used with three term sets containing five, nine, and thirteen elements, respectively.

Distributed problem-solving techniques: A survey

Distributed problem-solving is defined as a subfield of artificial intelligence that deals with the interaction of groups of intelligent agents attempting to cooperate to solve problems. A taxonomy of distributed artificial intelligence systems is presented, based on the communication and control methodologies used by their constituent agents, along with the theoretical foundations which underly them. Control in distributed problem-solvers is characterized by cooperation, organization, and dynamics. Communications are specified through paradigms, content, and protocols. Several prototypical systems in areas such as natural language processing and medical diagnosis are briefly discussed, along with more mature systems in applications such as air-traffic control, vehicle monitoring, and manufacturing systems.

Designing behaviors for information agents

To facilitate the rapid development and open system interoperability of autonomous agents we need to carefully specify and effectively implement various classes of agent behaviors. Our current focus is on the behaviors and underlying architecture of WWW-based autonomous software agents that collect and supply information to humans and other computational agents. This paper discusses a set of architectural building blocks that support the specification of behaviors for these information agents in a way that allows periodic actions, interleaving of planning and execution, and the concurrent activation of multiple behaviors with asynchronous components. We present an initial set of information agent behaviors, including responding to repetitive queries, monitoring information sources, advertising capabilities, and self cloning. We have implemented and tested these behaviors on the WWW in the context of WARREN, an open multi-agent organization for financial portfolio management.

Intelligent Adaptive Information Agents

Adaptation in open, multi-agent information gathering systems isimportant for several reasons. These reasons include the inability toaccurately predict future problem-solving workloads, future changes inexisting information requests, future failures and additions of agents anddata supply resources, and other future task environment characteristicchanges that require system reorganization. We have developed a multi-agentdistributed system infrastructure, RETSINA (REusable Task Structure-based Intelligent Network Agents) that handles adaptation in an open Internetenvironment. Adaptation occurs both at the individual agent level as well asat the overall agent organization level. The RETSINA system has three typesof agents. Interface agents interact with the userreceiving user specifications and delivering results. They acquire, model,and utilize user preferences to guide system coordination in support of theuser‘s tasks. Task agents help users perform tasks byformulating problem solving plans and carrying out these plans throughquerying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneouscollection of information sources. In this paper, we concentrate on theadaptive architecture of the information agents. We use as the domain ofapplication WARREN, a multi-agent financial portfolio management system thatwe have implemented within the RETSINA framework.

Coordinated hospital patient scheduling

Hospital Patient Scheduling is an inherently distributed problem because of the way real hospitals are organized. As medical procedures have become more complex, and their associated tests and treatments have become interrelated, the current ad hoc patient scheduling solutions have been observed to break down. We propose a multi-agent solution using the Generalized Partial Global Planning (GPGP) approach that preserves the existing human organization and authority structures, while providing better system-level performance (increased hospital unit throughput and decreased patient slay time). To do this, we extend GPGP with a new coordination mechanism to handle mutually exclusive resource relationships. Like the other GPGP mechanisms, the new mechanism can be applied to any problem with the appropriate resource relationship. We evaluate the this new mechanism in the context of the hospital patient scheduling problem, and examine the effect of increasing interrelations between tasks performed by different hospital units.

GoFigure: Automated Gene Ontology ™ annotation

SUMMARY We have developed a web tool to predict Gene Ontology (GO) terms. The tool accepts an input DNA or protein sequence, and uses BLAST to identify homologous sequences in GO annotated databases. A graph is returned to the user via email. AVAILABILITY The tool is freely available at: http://udgenome.ags.udel.edu/frm_go.html/

Research directions for service-oriented multiagent systems

Todays service-oriented systems realize many ideas from the research conducted a decade or so ago in multiagent systems. Because these two fields are so deeply connected, further advances in multiagent systems could feed into tomorrows successful service-oriented computing approaches. This article describes a 15-year roadmap for service-oriented multiagent system research.

Coordinating Mutually Exclusive Resources using GPGP

Hospital Patient Scheduling is an inherently distributed problem because of the way real hospitals are organized. As medical procedures have become more complex, and their associated tests and treatments have become interrelated, the current ad hoc patient scheduling solutions have been observed to break down. We propose a multi-agent solution using the Generalized Partial Global Planning (GPGP) approach that preserves the existing human organization and authority structures, while providing better system-level performance (increased hospital unit throughput and decreased patient stay time). To do this, we extend GPGP with a new coordination mechanism to handle mutually exclusive resource relationships. Like the other GPGP mechanisms, the new mechanism can be applied to any problem with the appropriate resource relationship. We evaluate this new mechanism in the context of the hospital patient scheduling problem, and examine the effect of increasing interrelations between tasks performed by different hospital units.