| P. Maes, "Modeling adaptive autonomous agents," Artificial Life Journal, vol. 1, no. 1 & 2, pp. 135--162, 1994. |
....and may be applied (i.e. executed) in some domain. The application of a neural network represents the recall phase of a network. In many cases, a trained neural network is used to implement systems that simply classify tuples of input data. Such systems are often referred to as classifier systems [7, 10, 12]. In a classifier system, the parameters of a network remain constant which prevent it from continuing to learn during execution. Neural networks may also be used, however, to implement systems that continue to learn, adapt, and strengthen their classification capabilities during execution by ....
P. Maes. Modeling adaptive autonomous agents. In C. G. Langton, editor, Artificial Life, An Overview, Cambridge, Massachussets, 1995. MIT Press.
....with the ability to perform long term strategic reasoning. While behaviour based architectures have been advocated as an approach suitable to satisfying both requirements [2] the problem of managing behaviour interactions so as to engineer long term strategic behaviour can be extremely difficult [7]. We have addressed the problem by designing a hierarchical behaviour based architecture, where higher level behaviours control the strategic behaviour of James Westendorp and Paul Scerri are both currently full time students. the agent. These strategy level behaviours activate only a ....
....perceptual inputs, on each individual behaviour and weight these inputs in such a way that leads to the appropriate low level behaviour being selected. This becomes increasingly complex for agents which may pursure multiple goals simultaneously and is a recognised problem for such architectures [1, 7]. By allowing a strategy level behaviour to instantiate multiple behaviours at the action level, the ability to react at the lowest level is not compromised: for example, the agent can switch between action level behaviours without needing to rethink its strategy if it needs to in order to react ....
P. Maes. Modeling adaptive autonomous agents. Artificial Life Journal, 1, 1994.
....agent. An agent tries to act towards a specific goal. Agents are autonomous in view of their cooperating counterparts. They decide autonomously which actions are performed best in order to achieve a goal. The interests of an agent can change depending on what it perceives from the environment [10]. Interactions among agents considered in this paper are inter organizational and, thus, purely competitive, involving self interest and utility maximization. As agents are autonomous, the factors which influence their behavior are private and not available to their opponents. Thus, agents do not ....
P. Maes. Modeling adaptive autonomous agents. Artificial Life Journal, 1(1), 1994.
....grid world and real world task allocation experiments, applied to the emergency handling problem domain. We compare the grid world and real world results. 2 Problem Statement In the context of multi robot coordination, dynamic task allocation can be viewed as the selection of appropriate actions [10] for each robot at each point in time so as to achieve the completion of the global task by the team as a whole. From a global perspective, in multi robot coordination, action selection is based on the mapping from the combined robot state space to the combined robot action space. For homogeneous ....
P. Maes. Modeling adaptive autonomous agents. Artificial Life, I, (1&2)(9), 1994.
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P. Maes, "Modeling adaptive autonomous agents," Artificial Life Journal, vol. 1, no. 1 & 2, pp. 135--162, 1994.
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P. Maes, "Modeling Adaptive Autonomous Agents", Artificial Life Journal, 1(1-2), MIT, 1994.
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P. Maes. Modeling adaptive autonomous agents. Artificial Life Journal, 1(1-2), 1994.
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P. Maes, Modeling Adaptive Autonomous Agents. Artificial Life Journal, vol 1(1-2), 1994.
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P. Maes, Modeling Adaptive Autonomous Agents. Artificial Life Journal 1(1-2), MIT Press, Cambridge, MA (1994), 135--162.
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Maes, P.: Modeling Adaptive Autonomous Agents. Artificial Life Journal, 1 (1-2): 135-162, MIT Press, Cambridge, MA, 1994.
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P. Maes. Modeling adaptive autonomous agents. Artificial Life Journal, 1(1-2), 1994.
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Maes, P. Modeling Adaptive Autonomous Agents, MIT Media-Laboratory, MIT,
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Maes, P. Modeling Adaptive Autonomous Agents, Journal of Artificial Life, MIT Press, 1 (1-2): 135-162, 1994.
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Pattie Maes, Modeling adaptive autonomous agents, Arti cial life 1: 135-162, 1994.
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P. Maes, "Modeling Adaptive Autonomous Agents", Artificial Life Journal 1 (1994) 1 & 2, pp. 135-162.
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Maes, P.: Modeling Adaptive Autonomous Agents. Artificial Life Journal, 1 (1-2): 135-162, MIT Press, Cambridge, MA, 1994.
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Maes, P. (1994). Modeling adaptive autonomous agents. Artificial Life Journal, 1(1--2):135--162.
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P. Maes, Modeling Adaptive Autonomous Agents, in Arti cial Life Journal, 1 (1-2) pp. 135-162, MIT Press, Cambridge, MA, 1994.
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Pattie Maes, "Modeling Adaptive Autonomous Agents," Artificial Life Journal, edited by C. Langton, Vol. 1, No. 1 & 2, pp. 135-162, MIT Press, 1994.
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. Pattie Maes, "Modeling Adaptive Autonomous Agents", Artificial Life Journal, edited by C. Langton, Vol. 1, No. 1 & 2, pp. 135-162, MIT Press, 1994. 2 of 2 3/20/03 2:50 PM
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Pattie Maes. Modeling Adaptive Autonomous Agents. Artificial Life, I, (1&2)(9), 1994.
No context found.
P. Maes. Modeling adaptive autonomous agents. Artificial Life Journal, 1(1-2), 1994.
No context found.
Pattie Maes. Modeling adaptive autonomous agents. Artificial Life, 1(1):135--162, 1994.
No context found.
Maes, P. 1994: Modeling Adaptive Autonomous Agents. In: Artificial Life Journal, 1 (1--2), pp. 135-162, MIT Press, Cambridge, MA.
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Maes, P.: Modeling Adaptive Autonomous Agents, in: Langton, C. (ed.): Artificial Life Journal, Vol. 1, No. 182, MIT Press, pp. 135-162, 1994.
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