Learning Fast and Slow: Levels of Learning in General Autonomous Intelligent Agents

  title={Learning Fast and Slow: Levels of Learning in General Autonomous Intelligent Agents},
  author={John E. Laird and Shiwali Mohan},
We propose two distinct levels of learning for general autonomous intelligent agents. Level 1 consists of fixed architectural learning mechanisms that are innate and automatic. Level 2 consists of deliberate learning strategies that are controlled by the agent's knowledge. We describe these levels and provide an example of their use in a task-learning agent. We also explore other potential levels and discuss the implications of this view of learning for the design of autonomous agents.  
Integrating Declarative Long-Term Memory Retrievals into Reinforcement Learning
A framework in which agents that follow the common model of cognition can learn to retrieve from LTM based only on task rewards, and use the resulting knowledge to select actions, to speed up learning when new entities are encountered.
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Causal cognitive architecture 1: Integration of connectionist elements into a navigation-based framework
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Daniel Kahneman, recipient of the Nobel Prize in Economic Sciences for his seminal work in psychology challenging the rational model of judgment and decision making, is one of the world's most
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A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics
A key foundational hypothesis in artificial intelligence is that minds are computational entities of a special sort — that is, cognitive systems — that can be implemented through a diversity of physical devices, whether natural brains, traditional generalpurpose computers, or other sufficiently functional forms of hardware or wetware.
Make It Stick: The science of successful learning
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