In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent's trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate.

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This research aims to develop a model of trust and reputation that will ensure good interactions amongst software agents in large scale open systems in particular. The following are key drivers for our model: (1) agents may be self-interested and may provide false accounts of experiences with other agents if it is beneficial for them to do so; (2) agents will need to interact with other agents with which they have no past experience. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent's trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents. When there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate.

The ability to create reliable, scalable virtual organisations (VOs) on demand in a dynamic, open and competitive environment is one of the challenges that underlie Grid computing. In response, in the CONOISE-G project, we are developing an infrastructure to support robust and resilient virtual organisation formation and operation. Specifically, CONOISE-G provides mechanisms to assure effective operation of agent-based VOs in the face of disruptive and potentially malicious entities in dynamic, open and competitive environments. In this paper, we describe the CONOISE-G system, outline its use in VO formation and perturbation, and review current work on dealing with unreliable information sources.

In many applications, Unmanned Aerial Vehicles (UAVs) provide an indispensable platform for gathering information about the situation on the ground. However, to maximise information gained about the environment, such platforms require increased autonomy to coordinate the actions of multiple UAVs. This has led to the development of flight planning and coordination algorithms designed to maximise information gain during sensing missions. However, these have so far neglected the need to maintain wireless network connectivity.In this paper, we address this limitation by enhancing an existing multi-UAV planning algorithm with two new features that together make a significant contribution to the state-of-theart: (1) we incorporate an on-line learning procedure that enables UAVs to adapt to the radio propagation characteristics of their environment, and (2) we integrate flight path and network routing decisions, so that modelling uncertainty and the affect of UAV position on network performance is taken into account. I. INTRODUCTIONIn many civilian applications, an aerial view is invaluable for gaining information about the situation on the ground [1]. Such applications include wilderness search and rescue, environmental monitoring, and situation awareness in natural disasters. In manned flight, such scenarios place a heavy burden on pilots, requiring long hours of monotonous flight at high-levels of concentration. Increasingly, however, advances in airframe design and control technology mean that using Unmanned Aerial Vehicles (UAVs) for such tasks is becoming a viable option. Small, inexpensive aircraft are now commercially available, and are typically equipped with an array of on-board sensors, such as GPS receivers and gyroscopes for navigation; and visible or infrared cameras to provide real-time information about the environment below. Moreover, with the development of sophisticated flight control algorithms, many craft can now take-off, land and fly automatically. As such, the human operator is no longer required to take low-level control of the UAV, but can instead concentrate on high-level decisions, and navigate the vehicle via GPS way-points.Despite these developments, existing applications still require a user on the ground to make complex real-time decisions about how to utilise the UAVs while they are in the air. Although such ground-based control has several advantages over manned flight, the complexity of some tasks mean that it is impossible for a human operator to take maximum advantage of UAV resources without increased autonomy or decision support mechanisms. For example, with multiple UAVs, the information gained about the environment is not just the sum of observations made by all UAVs. Rather, the actions of each UAV must be coordinated to reduce redundancy of