Variable Neighborhood Model for Agent Control Introducing Accessibility Relations Between Agents with Linear Temporal Logic
Seiki Ubukata*1, Tetsuya Murai*2, Yasuo Kudo*3,
and Seiki Akama*4
*1Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka 560-8531, Japan
*2Graduate School of Information Science and Technologies, Hokkaido University, Kita 14, Nishi 9, Kita-Ku, Sapporo 060-0814, Japan
*3College of Information and Systems, Muroran Institute of Technology, 27-1 Mizumoto, Muroran 050-8585, Japan
*4C-Republic, 1-20-1, Higashi-Yurigaoka, Asao-ku, Kawasaki-shi 215-0012, Japan
In general, there are two types of agents, reflex and deliberative. The former does not have the ability for deep planning that produces higher-level actions to attain goals cooperatively, which is the ability of the latter. Can we cause reflex agents to act as though they could plan their actions? In this paper, we propose a variable neighborhood model for reflex agent control, that allows such agents to create plans in order to attain their goals. The model consists of three layers: (1) topological space, (2) agent space, and (3) linear temporal logic. Agents with their neighborhoods move in a topological space, such as a plane, and in a cellular space. Then, a binary relation between agents is generated each time from the agents’ position and neighborhood. We call the pair composed of a set of agents and binary relations the agent space. In order to cause reflex agents to have the ability to attain goals superficially, we consider the local properties of the binary relation between agents. For example, if two agents have a symmetrical relation at the current time, they can struggle to maintain symmetry or they could abandon symmetry at the next time, depending on the context. Then, low-level behavior, that is, the maintenance or abandonment of the local properties of binary relations, grant reflex agents a method for selecting neighborhoods for the next time. As a result, such a sequence of low-level behavior generates seemingly higher-level actions, as though reflex agents could attain a goal with such actions. This low-level behavior is shown through simulation to generate the achievement of a given goal, such as cooperation and target pursuing.
and Seiki Akama, “Variable Neighborhood Model for Agent Control Introducing Accessibility Relations Between Agents with Linear Temporal Logic,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.6, pp. 937-945, 2014.
-  S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach,” Prentice Hall, 1995.
-  R. A. Brooks, “Intelligence Without Representation,” Artificial Intelligence, Vol.47, pp. 139-159, 1991.
-  S. Ubukata, Y. Kudo, and T. Murai, “An Agent Control Method Based on Rough-set-based Granularity,” Soft Computing and Intelligent Systems and 9th Int. Symp. on Advanced Intelligent Systems (SCIS&ISIS 2008) CD-ROM, FR-G3-1, 2008.
-  S. Ubukata, Y. Kudo, and T. Murai, “An Agent Control Method Based on Variable Neighborhoods,” Knowledge-Based and Intelligent Information and Engineering Systems, LNAI 5712, pp. 356-363, Springer, Berlin, 2009.
-  S. Ubukata, Y. Kudo, and T. Murai, “Autonomous Agent Control Based on Variable Neighborhoods,” Int. J. of Reasoning-based Intelligent Systems (IJRIS), Vol.3, No.1, 2010.
-  W. Sierpiński, “General Topology,” Univ. of Toronto Press, 1956.
-  J. R. Munkres, “Topology,” 2nd edition, Prentice Hall, 2000.
-  R. Goldblatt, “Logics of Time and Computation,” CSLI, 1987.
-  B. F. Chellas, “Modal Logic: an Introduction,” Cambridge University Press, Cambridge, 1980.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.