David C. Han

Agent-Oriented Design

Recent development in the field of Multi-Agent Systems (MAS) has attracted researchers from various fields with new techniques rapidly emerging. Due to its multi-disciplinary nature, it is not surprising that proposed theories and research results in the field are not coherent and hard to integrate. In this paper we propose a functional decomposition of problem solving activities to serve as a framework to assist MAS designers in their selection and integration of different techniques and existing research results according to their system requirements. The basic phases include agent organization construction, plan generation, task allocation, plan integration, and plan execution. An example usage of the proposed model for the domain of naval radar frequency management is also presented.

Sensible agents: an implemented multi-agent system and testbed

Sensible Agents have been engineered to solve distributed problems in complex, uncertain, and dynamic domains. Each Sensible Agent is composed of four modules: the Action Planner, Perspective Modeler, Conflict Resolution Advisor, and Autonomy Reasoner. These modules give Sensible Agents the abilities to plan, model, resolve individual conflicts, and change agent system organization. Two component suites provide a variety of user- oriented features: the Sensible Agent Run- time Environment (SARTE) and the Sensible Agent Testbed. The SARTE provides facilities for instantiating Sensible Agents, deploying a Sensible Agent system, and monitoring run- time operations. The Sensible Agents Testbed facilitates automated generation of parameter combinations for controlled experiments, deterministic and non-deterministic simulation, and configuration of Sensible Agents and data acquisition. Experimentation is a crucial step in gaining insight into the behavior of agents, as well as evidence toward or against hypotheses. Using a real- world example, this paper explains and demonstrates: (1) the functional capabilities of Sensible Agents, (2) the Sensible Agent Run- Time Environments facilities for monitoring and control of Sensible Agent systems and (3) the experimental set- up, monitoring, and analysis capabilities of the Sensible Agent Testbed.

Infrastructure for Design, Deployment and Experimentation of Distributed Agent-based Systems: The Requirements, The Technologies, and An Example

This paper discusses infrastructure for design, development, and experimentation of multi-agent systems. Multi-agent system design requires determining (1) how domain requirements drive the use of agents and AI techniques, (2) what competencies agents need in a MAS, and (3) which techniques implement those competencies. Deployment requirements include code reuse, parallel development through formal standardized object specifications, multi-language and multi-platform support, simulation and experimentation facilities, and user interfaces to view internal module, agent, and system operations. We discuss how standard infrastructure technologies such as OMG IDL, OMG CORBA, Java, and VRML support these services. Empirical evaluation of complex software systems requires iteration through combinations of experimental parameters and recording desired data. Infrastructure software can ease the setup, running, and analysis of large-scale computational experiments. The development of the Sensible Agent Testbed and architecture over the past six years provides a concrete example. The design rationale for the Sensible Agent architecture emphasizes domain-independent requirements and rapid deployment to new application domains. The Sensible Agent Testbed is a suite of tools providing or assisting in setting up, running, visually monitoring, and chronicling empirical testing and operation of complex, distributed multi-agent systems. A thorough look at the various Sensible Agents infrastructure pieces illustrates the engineering principles essential for multi-agent infrastructure, while documenting the software for users.

Agent technology for coordinating UAV target tracking

This paper illustrates three agent technologies deployed in the Unmanned Aerial Vehicle (UAV) target tracking domain. These capabilities enable: (1) coordination of the tracking of multiple targets among a set of UAVs, (2) identification of the best subset of assigned UAVs from which to collect location information, and (3) evaluation of location information accuracy. These capabilities aid the efficient and effective collection and verification of target location information.

Problem-solving frameworks for sensible agents in an electronic market

The need for responsive, flexible agents is pervasive in the electronic commerce environment due to its complex, dynamic nature. Two critical aspects of agent capabilities are the ability to (1) classify agent behaviors according to autonomy level, and (2) adapt problem-solving roles to various situations during system operation. Sensible Agents, capable of Dynamic Adaptive Autonomy, have been developed to address these issues. A Sensible Agent’s “autonomy level” constitutes a description of the agent’s problem-solving role with respect to a particular goal. Problem-solving roles are defined along a spectrum of autonomy ranging from command-driven, to consensus, to locally autonomous/master. Dynamic Adaptive Autonomy allows Sensible Agents to change autonomy levels during system operation to meet the needs of a particular problem-solving situation. This paper provides an overview of the Sensible Agent Testbed and provides examples showing how this testbed can be used to simulate agent-based problem solving in electronic-commerce environments.

Medical Imaging and Laboratory Analysis of Diagnostic Accuracy in 107 Consecutive Hospitalized Patients With Diabetic Foot Osteomyelitis and Partial Foot Amputations

The primary aim of our study was to compare the preoperative diagnostic accuracy of plain radiographic findings with the accuracy of magnetic resonance imaging (MRI) findings for diabetic foot osteomyelitis in hospitalized patients who underwent first-time partial foot amputations with confirmed histopathological specimens positive for osteomyelitis. Second, it was desired to determine whether certain variables within the initial clinical presentation and preoperative laboratory findings were associated with more accurate diagnosis of diabetic foot osteomyelitis in this study population. Finally, it was desired to determine the most common bacterial organisms found in bone and soft-tissue cultures taken intraoperatively and to determine how often the same organism was found in both. After applying the inclusion and exclusion criteria to the initial 329 patients identified through chart review, the final sample size for further analysis was n =107. In this study, after adjusting for the effects of covariates such as age, erythrocyte sedimentation rate (ESR) and C-reactive protein, plain radiographs seemed to have statistically more significant power than MRI in predicting and diagnosing diabetic foot osteomyelitis. In addition, higher ESR values were confirmed to predict a higher chance of positive diagnosis for diabetic foot osteomyelitis. Furthermore, the presence of positive bacterial identification from intraoperative bone cultures did not always indicate true osteomyelitis on histopathological examination. Levels of Evidence: Level II: Diagnostic study

Application of action selection, information gathering, and information evaluation technologies to UAV target tracking

This paper illustrates agent technologies applied to unmanned aerial vehicle (UAV) target tracking. The combination of the three technologies presented in this paper provide UAVs with functionality needed for coordinated autonomous operation, from building up accurate beliefs, efficiently gathering information, to acting rationally. In the UAV target tracking domain, communication among agents is necessary for building beliefs about target locations. Reliable information provisioning networks are constructed through selection of appropriate information sources and trust evaluations are applied to belief revision. Also, a macro-based action selection scheme is deployed for efficient coordination of the target tracking activity among agents.