The past decade the Australian Defence Science and Technology Organisation (DSTO) have been actively pursuing the use of the Belief, Desires, and Intention (BDI) agent model in operations analysis and computer generated forces. It has been shown that the BDI approach to agent programming brings the particular advantage of using terms and concepts that are familiar to all. For instance, in Air Operations studies, the tactical behaviour of a simulated pilot can he reviewed and verified directly by a real pilot, through inspection of the simulated tactical reasoning processes. At present the challenge is to bring this a step forward with appropriate tools and a control environment that enable pilots to operate an uninhabited airbome vehicle (UAV) during mission by means of tactical directives. Our experience has demonstrated that the BDI model provides a suitable framework for capturing tactical behaviours in the air operation domain. We have also demonstrated that the representation of these behaviours in terms of BDI plans is6 readily understandable by domain experts. However the current development process requires an experienced BDI developer to work with a domain expert in order to develop an appropriate set of agent behaviours. We would like to remove the BDI developer fiom the loop ~ this paper represents a first step in the achievement of that objective. In order to remove the BDI developer, we need to answer the following questions: 1. Can we provide the domain expert with a suitable (nonprogramming) environment for specification of agent behaviours? Can agent behaviour in this domain he partitioned into a declarative component (involving goals and plans) and a domain specific kemel? The focus of this paper is the fKst question, and we describe recent work on such a specification environment. The idea is that for controlling a UAV as an 'autonomous wingman', the pilot instructs the UAV by forming and commanding tactical plans at a suitable level of abstraction. The autonomous wingman would process these plans in the 2.context of common-sense background behaviour. Technically , this control environment is a simplified programming environment, where the pilot is presented with a collection of programming 'primitives' of high-level tactical behaviours and world-events based on the platform's sensor system. By combining primitives into plans. the pilot can build up a sensible instruction sequence for the UAV to accomplish particular tasks in ways appropriate for the situation at hand during mission.
With the increased use of unmanned aerial systems (UAS) for civil and commercial applications, there is a strong demand for new regulations and technology that will eventually permit for the integration of UAS in unsegregated airspace. This requires new technology to ensure sufficient safety and a smooth integration process. The absence of a pilot on board a vehicle introduces new problems that do not arise in manned flight. One challenging and safety-critical issue is flight in known icing conditions. Whereas in manned flight, dealing with icing is left to the pilot and his appraisal of the situation at hand; in unmanned flight, this is no longer an option and new solutions are required. To address this, an icing-related decision-making system (IRDMS) is proposed. The system quantifies in-flight icing based on changes in aircraft performance and measurements of environmental properties, and evaluates what the effects on the aircraft are. Based on this, it determines whether the aircraft can proceed, and whether and which available icing protection systems should be activated. In this way, advice on an appropriate response is given to the operator on the ground, to ensure safe continuation of the flight and avoid possible accidents.
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