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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.

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Model of an agent.Adapted and modified from2 (see text).
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f1: Model of an agent.Adapted and modified from2 (see text).

Mentions: In the following, we shall discuss the concept of projective simulation in the framework of intelligent agents2. Realizations of intelligent agents could be robots, biological systems, or software packages (internet robots). An agent (see Figure 1) has sensors, through which it perceives its environment, and actuators, through which it acts upon the environment. Internally, one may imagine that it has access to some kind of computing device, on which the agent program is implemented. The function of the agent program is to process the perceptual input and output the result to the actuators.


Projective simulation for artificial intelligence.

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

Model of an agent.Adapted and modified from2 (see text).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: Model of an agent.Adapted and modified from2 (see text).
Mentions: In the following, we shall discuss the concept of projective simulation in the framework of intelligent agents2. Realizations of intelligent agents could be robots, biological systems, or software packages (internet robots). An agent (see Figure 1) has sensors, through which it perceives its environment, and actuators, through which it acts upon the environment. Internally, one may imagine that it has access to some kind of computing device, on which the agent program is implemented. The function of the agent program is to process the perceptual input and output the result to the actuators.

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