Abstract
The paper considers the task of simultaneous learning and planning actions for moving a cognitive agent in two-dimensional space. Planning is carried out by an agent who uses an anthropic way of knowledge representation that allows him to build transparent and understood planes, which is especially important in case of human-machine interaction. Learning actions to manipulate objects is carried out through reinforcement learning and demonstrates the possibilities of replenishing the agent’s procedural knowledge. The presented approach was demonstrated in an experiment in the Gazebo simulation environment.
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Acknowledgments
The results concerning models of sign components and planning algorithms (Sects. 4.1 and 4.2) were obtained under the support of the Russian Science Foundation (project No. 16-11-00048), and the results on reinforcement learning for manipulator (Sects. 4.3 and 5) were obtained under the support of the Russian Foundation for Basic Research (project No. 17-29-07079).
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Aitygulov, E., Kiselev, G., Panov, A.I. (2018). Task and Spatial Planning by the Cognitive Agent with Human-Like Knowledge Representation. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2018. Lecture Notes in Computer Science(), vol 11097. Springer, Cham. https://doi.org/10.1007/978-3-319-99582-3_1
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