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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 episodic memory as a network of clips.
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f2: Model of episodic memory as a network of clips.

Mentions: Formally, episodic memory will be described as a probabilistic network of clips as illustrated in Figure 2. An excited clip calls, with certain probabilities, another, neighboring clip. The neighborhood of clips is defined by the network structure, and the jump probabilities will be functions of the percept history. In the simplest version, only the jump probabilities (weights) change with time, while the network structure (graph topology) and the clip content is static. In a refined model, new clips (nodes in the graph) may be added, and the content of the clip may be modified (internal dimension of the nodes). A call of the episodic memory triggers a random walk through this memory space (network). In this sense, the agent jumps through the space of clips, invoking patchwork-like sequences of virtual experience. Action is induced by screening the clips for specific features. When a certain feature (or combination of features) is present and above a certain intensity level, it will trigger motor action.


Projective simulation for artificial intelligence.

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

Model of episodic memory as a network of clips.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Model of episodic memory as a network of clips.
Mentions: Formally, episodic memory will be described as a probabilistic network of clips as illustrated in Figure 2. An excited clip calls, with certain probabilities, another, neighboring clip. The neighborhood of clips is defined by the network structure, and the jump probabilities will be functions of the percept history. In the simplest version, only the jump probabilities (weights) change with time, while the network structure (graph topology) and the clip content is static. In a refined model, new clips (nodes in the graph) may be added, and the content of the clip may be modified (internal dimension of the nodes). A call of the episodic memory triggers a random walk through this memory space (network). In this sense, the agent jumps through the space of clips, invoking patchwork-like sequences of virtual experience. Action is induced by screening the clips for specific features. When a certain feature (or combination of features) is present and above a certain intensity level, it will trigger motor action.

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