Pierre Urlings

Future Directions: Building a Decision Making Framework Using Agent Teams

1 School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Australia 2 Air Operations Division, Defence Science and Technology Organisation, Edinburgh SA 5111, Australia 3 The Sellinger School of Business and Management, Loyola College in Maryland, Baltimore, MD 21210 USA 4 School of Electrical and Information Engineering, University of South Australia, Adelaide, South Australia SA 5095, Australia

A future framework for interfacing BDI agents in a real-time teaming environment

Research and applications in human-machine teaming continue to evolve the role of the human from immediate (manual) operator into supervisory and televisory controller. In the supervisory control role, the human operator will be functionally removed from the system under control and in the televisory role, the human operator will be physically removed. Although unmanned systems and vehicles have become a technical reality that drives this change, they will not eliminate the importance of the human operator as the commanding and controlling element in-the-loop. This paper will argue that existing automation concepts remain equally valid with an even greater emphasis on the need for a human-centered automation approach. Intelligent agent technology has become mature and attractive enough to implement the automated components of the human-machine team. Agents that implement the Beliefs-Desire-Intention syntax will be discussed as being of particular interest for human-machine teaming applications. This paper proposes a theoretical framework for teaming human and intelligent agents. The teaming framework will be demonstrated in a real-time simulation environment using the commercial game called Unreal Tournament and its existing GameBot extension. The intelligent agents will be implemented based on the Belief-Desire-Intention (BDI) syntax and using JACK, a commercial BDI Agent development language. The requirements for follow-on research, such as human-agent teaming, human-agent coordination and agent learning will be highlighted.

Intelligent Agents and Situation Awareness

Advances in automation and artificial intelligence, and especially in the area of intelligent (machine) agents, have enabled the formation of rather unique teams with human and machine members. This paper describes initial research into intelligent agents using a Beliefs-Desires-Intentions (BDI) architecture in a human-machine teaming environment. The potential for teaming applications of intelligent agent technologies based on cognitive principles will be examined. Intelligent agents using the BDI-reasoning model can be used to provide a situation awareness capability to a human-machine team dealing with a military hostile environment. The implementation described in this paper is using JACK agents and “Unreal Tournament” (UT). JACK is an intelligent agent platform while UT is a fast paced interactive game within a 3D-graphical environment. Each game is scenario based and displays the actions of a number of opponents engaged in adversarial roles. Opponents can be humans or agents interconnected via the UT games server. To support research goals, JACK extends the Bot-class of UT and its agents apply the BDI architecture to build situational awareness. This paper provides the background for the use of intelligent agents and their cognitive potential. The research is described in terms of the operational environment and the corresponding implementation that is suitable for intelligent agents to exhibit BDI behaviour. The JACK application will be described and specific requirements will be addressed to implement learning in intelligent agents. Other cognitive agent behaviour such as communication and teaming are aims of this research.

Intelligent Decision Support System in Defense Maintenance Methodologies

Integrated logistics support (ILS) systems are becoming more sophisticated as the defense operations demand increasing levels of service from contractors and maintainers. Intelligent decision support system (IDSS) are required to provide adaptive automated responses for provisioning and maintenance of an increasing number of defense platforms that are now being supported either by defense organizations or by long term maintenance contracts from within the private sector. Traditional methods of repair are being upgraded to include data mining and intelligent agents to help stimulate intelligent decision support systems in creating a more efficient and reliable maintenance environment for defense. A concept demonstration, called the automated test equipment multi-agent system (ATEMAS), is being developed under a collaborative program between Defense Science and Technology Organization (DSTO), the University of South Australia and Raytheon Australia. It is envisaged that when developed, this system will be able to provide cognitive intelligence to support better reliability predictions and thus making possible the much needed proactive obsolescence management for avionics parts

Teaming Humans and Agents in a Simulated World

Previous research on human-machine teaming[1] argued that a formal specification in human-agent systems could prove beneficial in a hostile environment, and proposed an initial demonstration which has been successfully implemented[2] using a test-bed based on JACK[3] and Unreal Tournament (UT) [4]. This paper describes how to harvest these results and proposes a team for situations where human-agent collaboration is crucial to its success. A specific game type within Unreal Tournament is utilised, called Capture The Flag[5]. The proposed team is designed with the Prometheus agent design methodology[6] as a guide and is comprised of humans and agents each having a specific role.

Intelligent decision support feedback using Multi-Agent System in a defence maintenance environment

Safety and Airworthiness of airborne platforms rest heavily on the maintainability and reliability to maximise the availability and reduce logistics down time. Most of the test and maintenance data currently produced is either paper-based or discarded and generally fails to provide preventive analysis. Improvements could be made by creating an expert system using intelligent agents. Data Mining techniques and intelligent agents could be employed to create an expert system within the Integrated Logistics Support (ILS), thereby creating a feedback mechanism. This concept would develop into an Intelligent Decision Support System (IDSS) that extrapolates forecasts and warnings. This paper presents the design and development of that agent-based Intelligent Decision Support System (IDSS).

Building a decision making framework using agent teams

This article describes preliminary work performed to gain an understanding of how to implement Decision support system that collaborate between intelligent agents in a Multi-Agent System MAS when human interaction is involved. A condensed description of previous research shows how developments in the agent software frameworks can be implemented using reasoning and learning models. Cooperation is a type of relationship that is evident within structured teams where collaboration involves the creation of temporary relationships between those agents and/or humans to achieve their respective goals. Due to the inherent physical separation between humans and agents, the concept of collaboration has been identified as the means of realizing human-agent teams to assist with decision making. An example application is also provided to demonstrate this research.

Teaming Human and Machine: A Conceptual Framework

Advances in automation and especially artificial intelligence have enabled the formation of rather unique teams with human and (electronic) machine members. This paper proposes a conceptual framework for teaming human and machine. The basis of this framework will be the introduction of the machine into the traditional situation where the human is solely responsible for managing, control and execution of all activities. Focus will be on the identification and classification of activities to be allocated to the machine. Task management and coordination between human and machine will be identified as a specific area of research and design concern.

Teaming Humans and Intelligent Agents

This chapter describes initial research into intelligent agents using the Beliefs-Desires-Intentions (BDI) architecture in a human-machine teaming environment. The potential for teaming applications of intelligent agent technologies based on cognitive principles will be examined

Agent Transportation Layer Adaptation System

Heuristic computing has consolidated into two streams of research. One that personifies software to exhibit human behaviour and an oher that provides innovative software or smart products [1]. The Turing test [2] was pivotal in providing researchers with a generally accepted method of classifying the work that now defines the major problems pursued within Artificial Intelligence (AI). Cognitive Science is one of these fields and Research in Multi-Agent System (MAS) has revealed that Agents must enter into a voluntarily trust relationship in order to collaborate, otherwise the imposed goal(s) may be aborted or fail completely [3, 4]. Current agent architectures present a finite limit to functionality when supporting one or more of these paradigms.