Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
Optimality issues of universal greedy agents with static priors
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Delusion, survival, and intelligent agents
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Delusion, survival, and intelligent agents
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Universal knowledge-seeking agents
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Avoiding unintended AI behaviors
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Space-Time embedded intelligence
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Memory issues of intelligent agents
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
On Potential Cognitive Abilities in the Machine Kingdom
Minds and Machines
Universal knowledge-seeking agents
Theoretical Computer Science
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This paper considers the consequences of endowing an intelligent agent with the ability to modify its own code. The intelligent agent is patterned closely after AIXI [1], but the environment has read-only access to the agent's description. On the basis of some simple modifications to the utility and horizon functions, we are able to discuss and compare some very different kinds of agents, specifically: reinforcement-learning, goal-seeking, predictive, and knowledge-seeking agents. In particular, we introduce what we call the "Simpleton Gambit" which allows us to discuss whether these agents would choose to modify themselves toward their own detriment.