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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
Associative learning through projective simulation.After first training the agent with symbols of one color (red), at time step n = 200 the attacker starts to use a different color (blue). In comparison with Figure 6, now the agent learns faster. This situation resembles a form of “associative learning”, when the agent “recognizes” a similarity between the percepts of different colors, but identical shapes. The effect can be much enhanced if one allows for reflection times R > 1. The memory that gives rise to these learning curves is depicted in Figure 12. Ensemble average over 10000 agents.
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f11: Associative learning through projective simulation.After first training the agent with symbols of one color (red), at time step n = 200 the attacker starts to use a different color (blue). In comparison with Figure 6, now the agent learns faster. This situation resembles a form of “associative learning”, when the agent “recognizes” a similarity between the percepts of different colors, but identical shapes. The effect can be much enhanced if one allows for reflection times R > 1. The memory that gives rise to these learning curves is depicted in Figure 12. Ensemble average over 10000 agents.

Mentions: In contrast, in Figure 11, we see the learning curves for the same game but with a slightly modified memory architecture. After having trained the agent with symbols of one color (red), at time step n = 200 the attacker starts using a different color (blue). In comparison with Figure 6, now the agent learns faster, and the speed of learning increases with the strength of the parameter K. This situation resembles a form of “associative learning”, where the agent “recognizes” a similarity between the percepts of different colors (but identical shapes).


Projective simulation for artificial intelligence.

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

Associative learning through projective simulation.After first training the agent with symbols of one color (red), at time step n = 200 the attacker starts to use a different color (blue). In comparison with Figure 6, now the agent learns faster. This situation resembles a form of “associative learning”, when the agent “recognizes” a similarity between the percepts of different colors, but identical shapes. The effect can be much enhanced if one allows for reflection times R > 1. The memory that gives rise to these learning curves is depicted in Figure 12. Ensemble average over 10000 agents.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f11: Associative learning through projective simulation.After first training the agent with symbols of one color (red), at time step n = 200 the attacker starts to use a different color (blue). In comparison with Figure 6, now the agent learns faster. This situation resembles a form of “associative learning”, when the agent “recognizes” a similarity between the percepts of different colors, but identical shapes. The effect can be much enhanced if one allows for reflection times R > 1. The memory that gives rise to these learning curves is depicted in Figure 12. Ensemble average over 10000 agents.
Mentions: In contrast, in Figure 11, we see the learning curves for the same game but with a slightly modified memory architecture. After having trained the agent with symbols of one color (red), at time step n = 200 the attacker starts using a different color (blue). In comparison with Figure 6, now the agent learns faster, and the speed of learning increases with the strength of the parameter K. This situation resembles a form of “associative learning”, where the agent “recognizes” a similarity between the percepts of different colors (but identical shapes).

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