The extended ramp model: A biomimetic model of behaviour arbitration for lightweight cognitive architectures |
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Institution: | 1. School of Computer and Information Technology, Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China;2. Department of Automation, University of Science and Technology of China, Hefei 230027, China;3. School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, NSW 2751, Australia;4. State Key Laboratory of Fire Science, Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China;5. Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China;1. Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool L69 3GJ, UK;2. Agency for Science, Engineering and Research, Institute for Infocomm Research, Singapore;3. Harbin Institute of Technology, Shenzhen, China |
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Abstract: | In this article, we present an idea for a more intuitive, low-cost, adjustable mechanism for behaviour control and management. One focus of current development in virtual agents, robotics and digital games is on increasingly complex and realistic systems that more accurately simulate intelligence found in nature. This development introduces a multitude of control parameters creating high computational costs. The resulting complexity limits the applicability of AI systems. One solution to this problem it to focus on smaller, more manageable, and flexible systems which can be simultaneously created, instantiated, and controlled. Here we introduce a biologically inspired systems-engineering approach for enriching behaviour arbitration with a low computational overhead. We focus on an easy way to control the maintenance, inhibition and alternation of high-level behaviours (goals) in cases where static priorities are undesirable. The models we consider here are biomimetic, based on neuro-cognitive research findings from dopaminic cells responsible for controlling goal switching and maintenance in the mammalian brain. The most promising model we find is applicable to selection problems with multiple conflicting goals. It utilizes a ramp function to control the execution and inhibition of behaviours more accurately than previous mechanisms, allowing an additional layer of control on existing behaviour prioritization systems. |
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Keywords: | Reactive planning Action selection BBAI POSH BOD Bio-inspiration Modelling Behaviour design Curiosity 00-01 99-00 |
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