This paper presents an action selection method using fuzzy logic. The objective is to solve behaviour conflict in behaviour-based architectures for virtual agent navigation in unknown virtual environments. Two main problems have been identified: how to decide which behaviour should be activated at each instant; and how to combine the results from different behaviours into one action. The method uses fuzzy α-levels to compute behaviour weight for each behaviour and the final action is selected using the Huwicz criterion. The results clearly demonstrate the mapping of inputs to output with a near-optimum path in every navigation task.

This paper presents a multi-agent based evolutionary artificial neural network (ANN) for general navigation. While vision is a single input channel to the ANN, only the information about the availability of places in the current visual field is considered so that navigation is executed without restriction to any specific environment or object. Through constant interaction with the environment, multiple agents co-decide and compete with each other for the move decisions. These agents are subject to evolution via evolutionary strategies which are believed to be capable of solving the epistasis problem. Evolutionary learning continues during the lifetime of the ANN and is triggered whenever a correct move decision is absent. These basic ideas are illustrated by a virtual creature exploring unknown environments as far as possible with obstacle avoidance. Experimental results have shown that the virtual creature has incrementally improved its navigation ability and explored the environments efficiently. Meanwhile, some natural and robust behaviors have emerged, which are not programmed in advance.

This paper presents a solution for the behavioural animation of autonomous virtual agent navigation in virtual environments. We focus on developing a fuzzy controller to control and coordinate navigation behaviour. Two behaviours are considered: goal seeking and obstacle avoidance. Two main problems have been identified. These are: how to decide which behaviour should be activated at each instant; and how to combine the results from different behaviours into one command to be sent to the virtual agent. In summary, the proposed solution is simple and is expected to be applied to more complex problems with ease.

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