Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability
E774591
Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability is a foundational monograph by Marcus Hutter that rigorously develops a formal, mathematical theory of general artificial intelligence based on algorithmic information theory and optimal sequential decision-making.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T9062799 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability Context triple: [Marcus Hutter, notableWork, Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability]
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A.
Universal Intelligence: A Definition of Machine Intelligence
"Universal Intelligence: A Definition of Machine Intelligence" is a foundational paper by Shane Legg (with Marcus Hutter) that formally defines and mathematically characterizes general machine intelligence using concepts from algorithmic information theory and reinforcement learning.
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B.
universal intelligence measure
The universal intelligence measure is a formal, mathematical framework proposed to quantify and compare the general intelligence of agents across all possible environments.
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C.
Cambrian intelligence: The early history of the new AI
Cambrian Intelligence: The Early History of the New AI is a book by roboticist Rodney Brooks that outlines his influential behavior-based approach to artificial intelligence and robotics in contrast to traditional symbolic AI.
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D.
Superintelligence: Paths, Dangers, Strategies
Superintelligence: Paths, Dangers, Strategies is a 2014 book by philosopher Nick Bostrom that analyzes the potential development of superhuman artificial intelligence and the existential risks and strategic challenges it could pose to humanity.
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E.
Bayesian Occam factor
The Bayesian Occam factor is a term in Bayesian model comparison that automatically penalizes overly complex models by integrating over their larger parameter spaces, thereby implementing Occam’s razor in probabilistic inference.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability Target entity description: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability is a foundational monograph by Marcus Hutter that rigorously develops a formal, mathematical theory of general artificial intelligence based on algorithmic information theory and optimal sequential decision-making.
-
A.
Universal Intelligence: A Definition of Machine Intelligence
"Universal Intelligence: A Definition of Machine Intelligence" is a foundational paper by Shane Legg (with Marcus Hutter) that formally defines and mathematically characterizes general machine intelligence using concepts from algorithmic information theory and reinforcement learning.
-
B.
universal intelligence measure
The universal intelligence measure is a formal, mathematical framework proposed to quantify and compare the general intelligence of agents across all possible environments.
-
C.
Cambrian intelligence: The early history of the new AI
Cambrian Intelligence: The Early History of the New AI is a book by roboticist Rodney Brooks that outlines his influential behavior-based approach to artificial intelligence and robotics in contrast to traditional symbolic AI.
-
D.
Superintelligence: Paths, Dangers, Strategies
Superintelligence: Paths, Dangers, Strategies is a 2014 book by philosopher Nick Bostrom that analyzes the potential development of superhuman artificial intelligence and the existential risks and strategic challenges it could pose to humanity.
-
E.
Bayesian Occam factor
The Bayesian Occam factor is a term in Bayesian model comparison that automatically penalizes overly complex models by integrating over their larger parameter spaces, thereby implementing Occam’s razor in probabilistic inference.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
book
ⓘ
monograph ⓘ |
| aim | provide a general mathematical theory of artificial intelligence ⓘ |
| author | Marcus Hutter NERFINISHED ⓘ |
| basedOn |
Solomonoff induction
NERFINISHED
ⓘ
algorithmic probability ⓘ expected reward maximization ⓘ sequential decision theory ⓘ |
| characteristic |
foundational
ⓘ
highly formal ⓘ mathematically rigorous ⓘ |
| contribution |
analysis of optimal sequential decisions
ⓘ
formal definition of universal intelligence ⓘ formalization of AIXI agent ⓘ integration of algorithmic information theory with decision theory ⓘ rigorous mathematical framework for general AI ⓘ |
| describes |
AIXI as a theoretical optimal agent
NERFINISHED
ⓘ
formal theory of general artificial intelligence ⓘ universal reinforcement learning agent ⓘ |
| field |
algorithmic information theory
ⓘ
artificial intelligence ⓘ machine learning ⓘ sequential decision theory ⓘ |
| influencedBy |
Andrey Kolmogorov
NERFINISHED
ⓘ
Claude Shannon NERFINISHED ⓘ Ray Solomonoff NERFINISHED ⓘ sequential decision theory ⓘ |
| intendedAudience |
mathematically oriented AI practitioners
ⓘ
researchers in artificial intelligence ⓘ theoretical computer scientists ⓘ |
| language | English ⓘ |
| proposes |
AIXI as a maximally intelligent agent in a computable environment class
ⓘ
universal intelligence measure ⓘ |
| publisher | Springer NERFINISHED ⓘ |
| topic |
AIXI model
NERFINISHED
ⓘ
Kolmogorov complexity NERFINISHED ⓘ Solomonoff induction NERFINISHED ⓘ algorithmic probability ⓘ computability ⓘ optimal decision theory ⓘ rational agents ⓘ reinforcement learning ⓘ sequential decision making ⓘ universal agents ⓘ universal artificial intelligence NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability Description of subject: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability is a foundational monograph by Marcus Hutter that rigorously develops a formal, mathematical theory of general artificial intelligence based on algorithmic information theory and optimal sequential decision-making.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
Marcus Hutter
→
notableWork
→
Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability
ⓘ
Marcus Hutter
→
authorOf
→
Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability
ⓘ