Abstract.This paper presents a polynomial algorithm for solving the problem of finding the shortest path in an environment with states, with an emotional agent. The algorithm originates from an algorithm which in exponential time solves the same problem with the same agent architecture. By implementing emotional learning using dynamic programming, the polynomial algorithm is obtained.

It can be concluded that the choice of the function which evaluates the emotional state of the agent has decisive role in solving the problem efficiently. That function should carry the key information for solving the problem, i.e. to answer in every state what the solution of that problem in that state is, in fact, this function should express the maximal awareness of the agent for consequences of its actions in every state, and so to give as detailed information as possible about the consequences of its actions. In this way, this function implements the properties of human emotions.

Received: May 19, 2006

AMS Subject Classification: 68T05, 68Q25

Key Words and Phrases: emotional agent, complexity, polynomial time, consequence programming, CAA-neural network, dynamic programming

Source: International Journal of Pure and Applied Mathematics
ISSN: 1311-8080
Year: 2006
Volume: 29
Issue: 4