A multi-agent architecture integrating learning and
fuzzy techniques for landmark-based robot navigation
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This paper extends a navigation system implemented as a multi-agent
system (MAS). The arbitration mechanism controlling the interactions
between the agents was based on manually-tuned bidding functions.
A difficulty with hand-tuning is that it is hard to handle situations
involving complex tradeoffs.  In this paper we explore the suitability 
of reinforcement learning for automatically tuning agents within a MAS
to optimize a complex tradeoff, namely the camera use.