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Reinforcement Learning/Griffiths: Reinforcement learning is a standard method for training intelligent machines. By associating particular outcomes with rewards, a machine-learning system
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can be trained to follow strategies that produce those outcomes.
Def Inverse reinforcement learning: turns this approach around: By observing the actions of an intelligent agent that has already learned effective strategies, we can infer the rewards that led to the development of those strategies.
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Rationality is the standard assumption in inverse-reinforcement-learning models that try to make inferences from human behavior - perhaps with the concession that humans are not perfectly rational agents and sometimes randomly choose to act in ways unaligned with or even opposed to their best interests.
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(…) heuristic is a reasonable strategy for avoiding complex probabilistic computations, but also results in errors. For instance, relying on the ease of generating an event from memory as a guide to its probability leads us to overestimate the chances of extreme (hence extremely memorable) events such as terrorist attacks. Heuristics provide a more accurate model of human cognition but one that is not easily generalizable. How do we know which heuristic people might use in a particular situation? Are there other heuristics they use that we just haven’t discovered yet? >Decision Theory/Griffiths.
Griffiths, Tom, “The Artificial Use of Human Beings” in: Brockman, John (ed.) 2019. Twenty-Five Ways of Looking at AI. New York: Penguin Press._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. The note [Author1]Vs[Author2] or [Author]Vs[term] is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition.
Possible Minds: Twenty-Five Ways of Looking at AI New York 2019