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Projective simulation for artificial intelligence.

Briegel HJ, De las Cuevas G - Sci Rep (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institut für Theoretische Physik, Universität Innsbruck, Technikerstrasse 25, A-6020 Innsbruck, Austria. hans.briegel@uibk.ac.at

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.

Show MeSH
Game invasion.Defender agent D, whose task is to block the passage against invasion by the attacker A, tries to guess A’s next move from a symbol shown.
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f3: Game invasion.Defender agent D, whose task is to block the passage against invasion by the attacker A, tries to guess A’s next move from a symbol shown.

Mentions: To illustrate some of these concepts, let us consider the following simple game, which we call invasion (see Figure 3). It has two parties, an attacker (A) and a defender (D) (the robot/agent). The task of D is to defend a certain region against invasion by A. The attacker A can enter the region through doors in a wall, which are placed at equal distances. The defender D can block a door and thereby prevent A from invasion.


Projective simulation for artificial intelligence.

Briegel HJ, De las Cuevas G - Sci Rep (2012)

Game invasion.Defender agent D, whose task is to block the passage against invasion by the attacker A, tries to guess A’s next move from a symbol shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351754&req=5

f3: Game invasion.Defender agent D, whose task is to block the passage against invasion by the attacker A, tries to guess A’s next move from a symbol shown.
Mentions: To illustrate some of these concepts, let us consider the following simple game, which we call invasion (see Figure 3). It has two parties, an attacker (A) and a defender (D) (the robot/agent). The task of D is to defend a certain region against invasion by A. The attacker A can enter the region through doors in a wall, which are placed at equal distances. The defender D can block a door and thereby prevent A from invasion.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institut für Theoretische Physik, Universität Innsbruck, Technikerstrasse 25, A-6020 Innsbruck, Austria. hans.briegel@uibk.ac.at

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.

Show MeSH