Technical Note: \cal Q-Learning
Machine Learning
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Rigid Flexibility: The Logic of Intelligence (Applied Logic Series)
Rigid Flexibility: The Logic of Intelligence (Applied Logic Series)
A Collection of Definitions of Intelligence
Proceedings of the 2007 conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006
Extending the Soar Cognitive Architecture
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010)
IEEE Transactions on Autonomous Mental Development
On attention mechanisms for AGI architectures: a design proposal
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
2013 Special Issue: First experiments with PowerPlay
Neural Networks
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Resource-boundedness must be carefully considered when designing and implementing artificial general intelligence (AGI) algorithms and architectures that have to deal with the real world. But not only must resources be represented explicitly throughout its design, an agent must also take into account their usage and the associated costs during reasoning and acting. Moreover, the agent must be intrinsically motivated to become progressively better at utilizing resources. This drive then naturally leads to effectiveness, efficiency, and curiosity. We propose a practical operational framework that explicitly takes into account resource constraints: activities are organized to maximally utilize an agent's bounded resources as well as the availability of a teacher, and to drive the agent to become progressively better at utilizing its resources. We show how an existing AGI architecture called AERA can function inside this framework. In short, the capability of AERA to perform self-compilation can be used to motivate the system to not only accumulate knowledge and skills faster, but also to achieve goals using less resources, becoming progressively more effective and efficient.