This paper presents an approach to using a large team of UAVs to find radio frequency (RF) emitting targets in a large area. Small, inexpensive UAVs that can collectively and rapidly determine the approximate location of intermittently broadcasting and mobile RF emitters have a range of applications in both military, e.g., for finding SAM batteries, and civilian, e.g., for finding lost hikers, domains. Received Signal Strength Indicator (RSSI) sensors on board the UAVs measure the strength of RF signals across a range of frequencies. The signals, although noisy and ambiguous due to structural noise, e.g., multipath effects, overlapping signals and sensor noise, allow estimates to be made of emitter locations. Generating a probability distribution over emitter locations requires integrating multiple signals from different UAVs into a Bayesian filter, hence requiring cooperation between the UAVs. Once likely target locations are identified, EO-camera equipped UAVs must be tasked to provide a video stream of the area to allow a user to identify the emitter.
Many applications require that a group of agents share a coherent distributed picture of the world given commu nication constraints. This paper describes an analysis and design methodology for coordination algorithms for extremely large groups of agents maintaining a distributed belief. This design methodology creates a probability distribution which relates global properties of the system to agent interaction dynamics using the tools of statistical mechanics. Using this probability distribution we show that this system undergoes a rapid phase transition between low divergence and high divergence in the distributed belief at a critical value of system temperature. We also show empirically that at the critical system temperature the number of messages passed and belief divergence between agents is optimal. Finally, we use this fact to develop an algorithm using system temperature as a local decision parameter for an agent.
Agent based simulations often model humans and increasingly it is necessary to do this at an appropriate level of complexity. It has been suggested that the Belief Desire Intention (BDI) paradigm is suitable for modeling the cognitive processes of agents representing (some of) the humans in an agent based modeling simulation. This approach models agents as having goals, and reacting to events, with high level plans, or plan types, that are gradually refined as situations unfold. This is an intuitive approach for modeling human cognitive processes. However, it is important that users can understand, verify and even contribute to the model being used. We describe a tool that can be used to explore, understand and modify, the BDI model of an agent's cognitive processes within a simulation. The tool is interactive and allows users to explore options available (and not available) at a particular agent decision point. INTRODUCTIONDeveloping an Agent Based Model for social simulation requires the modeler to capture people's behaviors. In Padgham et al. (2011) it was argued that simple rules, typical of many agent based modeling platforms such as Repast (North et al. 2006) are inadequate, or at least inconvenient, for modeling the decision making of humans, who often operate using abstract plans over multiple time steps. This work described the integration of the JACK Belief Desire Intention (BDI) agent platform (Winikoff 2005) with Repast, to allow for easier modeling of human decision making processes. Also, it is becoming widely accepted that to make social simulations effective, with respect to their particular intended use, then end users, stakeholders and/or domain experts, need to be involved in the model specification, design, testing and use (see e.g. Ramanath and Gilbert (2004)). Who should be involved and how depends on the intended use of the simulation, which may range from education to social research exploration to decision support. This paper examines how -in the context of using the BDI paradigm to specify agents within an ABM simulation -to give users the possibility to understand the cognitive processes of an agent in the simulation, interact with these processes during a simulation, and also to help specify what those cognitive processes might be.Agent based simulations can be used for a range of different purposes around gaining greater understanding of complex situations. We have done some work (and developed a prototype simulation) around evacuation in response to a bushfire (i.e., forest fire or wildfire). In exploring with stakeholders how the tool we have developed may be further refined and used, one aspect which stands out is the need to explore and potentially interact with a representation of the cognitive processes (plans, goals and decisions) of the agent. If the simulation was to be used in community awareness building, we have been told it will be necessary for an individual to identify "their" representative agent in the simulation visualization, and also
La modélisation et la simulation ont longtemps été dominées par les approches basées sur les équations, jusqu'à l'avènement récent des approches orientées agents. Pour freiner l'augmentation de complexité des modèles que peut entraîner l'utilisation de cette nouvelle approche, la tendance est à la sursimplification des modèles. Des modèles plus descriptifs ont cependant été développés pour une variété de phénomènes, mais la cognition des agents est encore trop souvent négligée alors qu'elle a une grande importance dans certains domaines, en particulier en sciences humaines et sociales. La solution que nous proposons dans cet article est d'utiliser des agents BDI. Nous montrons qu'il s'agit d'un paradigme expressif, réaliste et simple qui apporte de nombreux bénéfices à la simulation à base d'agents. ABSTRACT. Modeling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the increasing complexity of models resulting from this new approach, the trend has been to oversimplify the models. Some more descriptive models have still been developed for various phenomenons, but the cognition of agents is too often neglected, despite its great importance in some fields, such as Social and Human Sciences. The solution that we put forward in this paper is to use BDI agents. We will show that this is an expressive, realistic yet simple paradigm that thus offers numerous benefits to agent-based simulation.
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