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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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Communicating Neural Network Knowledge between Agents in a .. - Quirolgico, Canfield   (1 citation)  (Correct)

....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.


Strategic Behaviour-Based Reasoning with Dynamic Partial .. - Westendorp, Scerri.. (1998)   (1 citation)  (Correct)

....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.


Scheduling Non-Enforceable Contracts Among Autonomous Agents - Tesch, Aberer (1998)   (Correct)

....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.


Multi-robot Task Allocation in the Light of Uncertainty - Østergaard, Mataric, Sukhatme (2002)   (Correct)

....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.


Binary Agents for the Control of Autonomous - Vehicles Andrew Wallace   (Correct)

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P. Maes, "Modeling adaptive autonomous agents," Artificial Life Journal, vol. 1, no. 1 & 2, pp. 135--162, 1994.


Towards Commitments for Situated Agents - Weyns, Steegmans, Holvoet (2004)   (Correct)

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P. Maes, "Modeling Adaptive Autonomous Agents", Artificial Life Journal, 1(1-2), MIT, 1994.


Protocol-Based Communication for Situated Multi-Agent Systems - Weyns, Steegmans, Holvoet (2004)   (Correct)

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P. Maes. Modeling adaptive autonomous agents. Artificial Life Journal, 1(1-2), 1994.


Designing Roles for Situated Agents - Steegmans, Weyns, Holvoet, Berbers (2004)   (Correct)

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P. Maes, Modeling Adaptive Autonomous Agents. Artificial Life Journal, vol 1(1-2), 1994.


The Packet-World: a Test Bed for Investigating Situated .. - Weyns, Helleboogh..   (Correct)

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P. Maes, Modeling Adaptive Autonomous Agents. Artificial Life Journal 1(1-2), MIT Press, Cambridge, MA (1994), 135--162.


A Formal Model for Situated Multi-Agent Systems - Weyns, Holvoet (2004)   (Correct)

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Maes, P.: Modeling Adaptive Autonomous Agents. Artificial Life Journal, 1 (1-2): 135-162, MIT Press, Cambridge, MA, 1994.


Protocol-Based Communication for Situated Multi-Agent Systems - Weyns, Steegmans, Holvoet (2004)   (Correct)

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P. Maes. Modeling adaptive autonomous agents. Artificial Life Journal, 1(1-2), 1994.


Using Autonomous Agents In Internet . . . - Persson, al.   (Correct)

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Maes, P. Modeling Adaptive Autonomous Agents, MIT Media-Laboratory, MIT,


Interaction Modeling with Artificial Life Agents - German-Soto, Sheremetov..   (Correct)

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Maes, P. Modeling Adaptive Autonomous Agents, Journal of Artificial Life, MIT Press, 1 (1-2): 135-162, 1994.


On the Behavior-based Architectures of Autonomous Agency - Mali   (Correct)

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Pattie Maes, Modeling adaptive autonomous agents, Arti cial life 1: 135-162, 1994.


Agent-Mediated Off-Exchange Trading - Weinhardt, Gomber   (Correct)

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P. Maes, "Modeling Adaptive Autonomous Agents", Artificial Life Journal 1 (1994) 1 & 2, pp. 135-162.


A Formal Model for Situated Multi-Agent Systems - Weyns, Holvoet (2001)   (Correct)

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Maes, P.: Modeling Adaptive Autonomous Agents. Artificial Life Journal, 1 (1-2): 135-162, MIT Press, Cambridge, MA, 1994.


Behaviour-Based Learning - Evolution Inspired Development of.. - Dahl (2002)   (Correct)

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Maes, P. (1994). Modeling adaptive autonomous agents. Artificial Life Journal, 1(1--2):135--162.


A Model for Active Perception in Situated Multi-agent Systems - Weyns, Steegmans, Holvoet (2003)   (Correct)

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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.


Adaptive Agents for Information Gathering from Multiple, .. - Information Sources.. (1999)   (Correct)

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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.


Presented at: Proc. of WebNet '97, Nov., 1997. - An Adaptive Multi-Agent (1997)   (Correct)

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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


An Annotated Bibliography of Mobile Agents in Networks - Sriraman (2002)   (Correct)

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Pattie Maes. Modeling Adaptive Autonomous Agents. Artificial Life, I, (1&2)(9), 1994.


Combining Adaptive Behavior and Role Modeling with.. - Weyns, Steegmans, Holvoet (2004)   (Correct)

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P. Maes. Modeling adaptive autonomous agents. Artificial Life Journal, 1(1-2), 1994.


AgeS - An Agent System - James Kramer University   (Correct)

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Pattie Maes. Modeling adaptive autonomous agents. Artificial Life, 1(1):135--162, 1994.


Model for Situated Multi-Agent Systems with Regional.. - Weyns, Holvoet (2003)   (1 citation)  (Correct)

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Maes, P. 1994: Modeling Adaptive Autonomous Agents. In: Artificial Life Journal, 1 (1--2), pp. 135-162, MIT Press, Cambridge, MA.


A Scalable Agent Infrastructure - Michael Berger Bernhard   (Correct)

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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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