Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Optimality issues of universal greedy agents with static priors
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Self-modification and mortality in artificial 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
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Space-Time embedded intelligence
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
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Theoretical models of artificial general intelligence, such as AIXI [3], typically consider an intelligent agent to have unlimited computational resources, allowing it to keep a perfect memory of its entire interaction history with its environment. In the real world, an agent's memory is part of the environment, which means that the latter can modify the former. This paper develops a theoretical framework for examining the implications of such real-world memory on universal intelligent agents. Within this framework we are able to show, for example, that in certain environments optimality can be achieved only with truly stochastic behaviors, and that guarantees about the trustworthiness of memories are difficult to obtain even with infinite computational power. To describe the probability of an agent's memory state, we propose an adaptation of the universal prior for the passive and the active case.