title: Projective simulation for artificial intelligence.
abstract: We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.
authors.affiliation: Institut für Theoretische Physik, Universität Innsbruck, Technikerstrasse 25, A-6020 Innsbruck, Austria. hans.briegel@uibk.ac.at
authors.forename: Hans J
authors.initials: HJ
authors.lastname: Briegel
authors.forename: Gemma
authors.initials: G
authors.lastname: De las Cuevas
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology; D008954:Models, Biological; D016571:Neural Networks, Computer; D012054:Reinforcement, Psychology; D015203:Reproducibility of Results