Search

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.

Thesis (PhDComputerSystemsEng)--University of South Australia, 2006. Intelligent Agents that operate in dynamic, real-time domains are required to embody complex but controlled behaviours, some of which may not be easily implementable. This thesis investigates the difficulties presented with implementing Intelligent Agents for such environments and makes contributions in the fields of Agent Reasoning, Agent Learning and Agent-Oriented Design in order to overcome some of these difficulties. The thesis explores the need for incorporating learning into agents. This is done through a comprehensive review of complex application domains where current agent development techniques are insufficient to provide a system of acceptable standard. The theoretical foundations of agent reasoning and learning are reviewed and a critique of reasoning techniques illustrates how humans make decisions. Furthermore, a number of learning and adaptation methods are introduced. The concepts behind Intelligent Agents and the reasons why researchers have recently turned to this technology for implementing complex systems are then reviewed. Overviews of different agent-oriented development paradigms are explored, which include relevant development platforms available for each one. Previous research on modeling how humans make decisions is investigated, in particular three models are described in detail. A new cognitive, hybrid reasoning model is presented that fuses the three models together to offset the demerits of one model by the merits of another. Due to the additional elements available in the new model, it becomes possible to define how learning can be integrated into the reasoning process. In addition, an abstract framework that implements the reasoning and learning model is defined. This framework hides the complexity of learning and allows for designing agents based on the new reasoning model. Finally, the thesis contributes the design of an application where learning agents are faced with a rich, real-time environment and are required to work as a teamto achieve a common goal. Detailed algorithmic descriptions of the agent's behaviours as well as a subset of the source code are included in the thesis. The empirical results obtained validate all contributions within the domain of Unreal Tournament. Ultimately, this dissertation demonstrates that if agent reasoning is implemented using a cognitive reasoning model with defined learning goals, an agent can operate effectively in a complex, real-time, collaborative and adversarial environment.

2005

2004

2004

2004