Towards Adaptive Role Selection for Behavior-Based Agents
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This paper presents a model for adaptive agents. The model describes the behavior of an agent as a graph of roles, in short a behavior graph. Links between roles provide conditions that determine whether the agent can switch roles. The behavior graph is assigned at design time, however adaptive role selection takes place at runtime. Adaptivity is achieved through factors in the links of the behavior graph. A factor models a property of the agent or its perceived environment. When an agent can switch roles via different links, the factors determine the role the agent will switch to. By analyzing the effects of its performed actions the agent is able to adjust the value of specific factors, adapting the selection of roles in line with the changing circumstances. Models for adaptive agents typically describe how an agent dynamically selects a behavior (or action) based on the calculation of a probability value as a function of the observed state for each individual behavior (or action). In contrast, the model we propose aims to dynamically adapt logical relations between different behaviors (called roles here) in order to dynamically form paths of behaviors (i.e. sequences of roles) that are suitable in the current state. To verify the model we applied it to the Packet-World. In the paper we discuss simulation results that show how the model enables the agents in the Packet-World to adapt their behavior to changes in the environment.
KeywordsMultiagent System Logical Relation Current Role Basic Agent Learning Factor
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- 1.Maes, P.: Modeling adaptive autonomous agents. Artificial Life, I 1–2 (1994)Google Scholar
- 2.Bryson, J.J.: Intelligence by design: Principles of modularity and coordination for engineering complex adaptive agents. In: PhD thesis, MIT, Department of EECS, Cambridge, MA. AI Technical Report 2001-003 (2001)Google Scholar
- 3.Bruemmer, D.: Adaptive robotics, behavior-based robotics, muti-agent control (2004), http://www.inel.gov/adaptiverobotics/behaviorbasedrobotics/multiagent.shtml
- 4.Maes, P., Brooks, R.: Learning to coordinate behaviors. Autonomous Mobile Robots: Control, Planning and Architecture, vol. 2. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
- 5.Drogoul, A.: De la simulation multi-agent a la résolution collective de problémes. Ph.D thesis, Université Paris 6, France (1993)Google Scholar
- 7.Schelfthout, K., Holvoet, T.: To do or not to do: The individual’s model for emergent task allocation. In: Proceedings of the AISB 2002 Symposium on Adaptive Agents and Multi-Agent Systems (2002)Google Scholar
- 8.De Wolf, T., Holvoet, T.: Adaptive behavior based on evolving thresholds with feedback. In: Kazakov, D., Alonso, D.K.E. (eds.) Proceedings of the AISB 2003 Symposium on Adaptive Agents and Multiagent Systems (2003)Google Scholar
- 9.Drogoul, A., Zucker, J.: Methodological issues for designing multi-agent systems with machine learning techniques: Capitalizing experiences from the robocup challenge. Technical Report 041, LIP6 (1998)Google Scholar
- 10.Kaebling, L., Litmann, L., Moore, A.: Reinforcement learning: a survey. Journal of Artificial Intelligence Research 4 (1996)Google Scholar
- 11.Huhns, M., Stephens, L.: Multi-agent systems and societies of agents. In: Weiss, G. (ed.) Multi-agent Systems, vol. 2. MIT Press, Cambridge (1999)Google Scholar
- 12.Weyns, D., Holvoet, T.: The packet–world as a case to investigate sociality in multi-agent systems. In: Demo presented at the 1st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2002, Bologna (2002)Google Scholar