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Formal Theory of Creativity, Fun, and Intrinsic Motivation (19902010)
"... The simple but general formal theory of fun & intrinsic motivation & creativity (1990) is based on the concept of maximizing intrinsic reward for the active creation or discovery of novel, surprising patterns allowing for improved prediction or data compression. It generalizes the traditional fiel ..."
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Cited by 33 (14 self)
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The simple but general formal theory of fun & intrinsic motivation & creativity (1990) is based on the concept of maximizing intrinsic reward for the active creation or discovery of novel, surprising patterns allowing for improved prediction or data compression. It generalizes the traditional field of active learning, and is related to old but less formal ideas in aesthetics theory and developmental psychology. It has been argued that the theory explains many essential aspects of intelligence including autonomous development, science, art, music, humor. This overview first describes theoretically optimal (but not necessarily practical) ways of implementing the basic computational principles on exploratory, intrinsically motivated agents or robots, encouraging them to provoke event sequences exhibiting previously unknown but learnable algorithmic regularities. Emphasis is put on the importance of limited computational resources for online prediction and compression. Discrete and continuous time formulations are given. Previous practical but nonoptimal implementations (1991, 1995, 19972002) are reviewed, as well as several recent variants by others (2005). A simplified typology addresses current confusion concerning the precise nature of intrinsic motivation.
Gödel machines: Fully selfreferential optimal universal selfimprovers
 Goertzel and C. Pennachin, Artificial General Intelligence
, 2006
"... Summary. We present the first class of mathematically rigorous, general, fully selfreferential, selfimproving, optimally efficient problem solvers. Inspired by Kurt Gödel’s celebrated selfreferential formulas (1931), such a problem solver rewrites any part of its own code as soon as it has found ..."
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Cited by 25 (12 self)
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Summary. We present the first class of mathematically rigorous, general, fully selfreferential, selfimproving, optimally efficient problem solvers. Inspired by Kurt Gödel’s celebrated selfreferential formulas (1931), such a problem solver rewrites any part of its own code as soon as it has found a proof that the rewrite is useful, where the problemdependent utility function and the hardware and the entire initial code are described by axioms encoded in an initial proof searcher which is also part of the initial code. The searcher systematically and efficiently tests computable proof techniques (programs whose outputs are proofs) until it finds a provably useful, computable selfrewrite. We show that such a selfrewrite is globally optimal—no local maxima!—since the code first had to prove that it is not useful to continue the proof search for alternative selfrewrites. Unlike previous nonselfreferential methods based on hardwired proof searchers, ours not only boasts an optimal order of complexity but can optimally reduce any slowdowns hidden by the O()notation, provided the utility of such speedups is provable at all. 1
Gödel Machines: SelfReferential Universal Problem Solvers Making Provably Optimal SelfImprovements
, 2003
"... An old dream of computer scientists is to build an optimally ecient universal problem solver. We show how to solve arbitrary computational problems in an optimal fashion inspired by Kurt Gödel's celebrated selfreferential formulas (1931). Our Godel machine's initial software includes an axiomat ..."
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Cited by 16 (7 self)
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An old dream of computer scientists is to build an optimally ecient universal problem solver. We show how to solve arbitrary computational problems in an optimal fashion inspired by Kurt Gödel's celebrated selfreferential formulas (1931). Our Godel machine's initial software includes an axiomatic description of: the Godel machine's hardware, the problemspeci c utility function (such as the expected future reward of a robot), known aspects of the environment, costs of actions and computations, and the initial software itself (this is possible without introducing circularity). It also includes a typically suboptimal initial problemsolving policy and an asymptotically optimal proof searcher searching the space of computable proof techniquesthat is, programs whose outputs are proofs. Unlike previous approaches, the selfreferential Gödel machine will rewrite any part of its software, including axioms and proof searcher, as soon as it has found a proof that this will improve its future performance, given its typically limited computational resources. We show that selfrewrites are globally optimalno local minima!since provably none of all the alternative rewrites and proofs (those that could be found by continuing the proof search) are worth waiting for.
New millennium AI and the convergence of history
 Challenges to Computational Intelligence
, 2007
"... Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. At ..."
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Cited by 6 (4 self)
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Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. At the same time there has been rapid progress in practical methods for learning true sequenceprocessing programs, as opposed to traditional methods limited to stationary pattern association. Here we will briefly review some of the new results, and speculate about future developments, pointing out that the time intervals between the most notable events in over 40,000 years or 2 9 lifetimes of human history have sped up exponentially, apparently converging to zero within the next few decades. Or is this impression just a byproduct of the way humans allocate memory space to past events? 1
Algorithmic Information Theory [ a brief nontechnical guide to the field]
, 2007
"... This article is a brief guide to the field of algorithmic information theory (AIT), its underlying philosophy, and the most important concepts. AIT arises by mixing information theory and computation theory to obtain an objective and absolute notion of information in an individual object, and in so ..."
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Cited by 4 (4 self)
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This article is a brief guide to the field of algorithmic information theory (AIT), its underlying philosophy, and the most important concepts. AIT arises by mixing information theory and computation theory to obtain an objective and absolute notion of information in an individual object, and in so doing gives rise to an objective and robust notion of randomness of individual objects. This is in contrast to classical information theory that is based on random variables and communication, and has no bearing on information and randomness of individual objects. After a brief overview, the major subfields,
Mixing cognitive science concepts with computer science algorithms and data structures: An integrative approach to strong AI
 In AAAI Spring Symposium Series
, 2006
"... We posit that, given the current state of development of cognitive science, the greatest synergies between this field and artificial intelligence arise when one adopts a high level of abstraction. On the one hand, we suggest, cognitive science embodies some interesting, potentially general principle ..."
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Cited by 2 (1 self)
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We posit that, given the current state of development of cognitive science, the greatest synergies between this field and artificial intelligence arise when one adopts a high level of abstraction. On the one hand, we suggest, cognitive science embodies some interesting, potentially general principles regarding cognition under limited resources, and AI systems that violate these principles should be treated with skepticism. But on the other hand, attempts to precisely emulate human cognition in silicon are hampered by both their ineffectiveness at exploiting the power of digital computers, and the current paucity of algorithmlevel knowledge as to how human cognition takes place. We advocate a focus on artificial general intelligence design. This
The Evaluation of AGI Systems
"... The paper surveys the evaluation approaches used in AGI research, and argues that the proper way of evaluation is to combine empirical comparison with human intelligence and theoretical analysis of the assumptions and implications of the AGI models. Approaches of Evaluation In recent years, the prob ..."
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Cited by 1 (1 self)
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The paper surveys the evaluation approaches used in AGI research, and argues that the proper way of evaluation is to combine empirical comparison with human intelligence and theoretical analysis of the assumptions and implications of the AGI models. Approaches of Evaluation In recent years, the problem of evaluation is getting more and more attention in the field of Artificial General Intelligence, or AGI (GB09; LIIL09; LGW09; MAP + 09; Was09). Though the evaluation of research results is important in any field of scientific research, the problem has special difficulty in the current context of AGI, since the research activities belong to many different paradigms, and there seems to be no “neutral”
New Millennium AI and the Convergence of History: Update of 2012
"... millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. There also has been rapid progress in not quite universal but still rather general ..."
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millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. There also has been rapid progress in not quite universal but still rather general and practical artificial recurrent neural networks for learning sequenceprocessing programs, now yielding stateoftheart results in real world applications. And the computing power per Euro is still growing by a factor of 1001000 per decade, greatly increasing the feasibility of neural networks in general, which have started to yield humancompetitive results in challenging pattern recognition competitions. Finally, a recent formal theory of fun and creativity identifies basic principles of curious and creative machines, laying foundations for artificial scientists and artists. Here I will briefly review some of the new results of my lab at IDSIA, and speculate about future developments, pointing out that the time intervals between the most notable events in over 40,000 years or 2 9 lifetimes of human history have sped up exponentially, apparently converging to zero within the next few decades. Or is this impression just a byproduct of the way humans allocate memory space to past events?
1 The New AI is General & Mathematically Rigorous
"... Summary. Most traditional artificial intelligence (AI) systems of the past decades are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, ..."
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Summary. Most traditional artificial intelligence (AI) systems of the past decades are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam’s razor, problem solving, decision making, and reinforcement learning in environments of a very general type. Since inductive inference is at the heart of all inductive sciences, some of the results are relevant not only for AI and computer science but also for physics, provoking nontraditional predictions based on Zuse’s thesis of the computergenerated universe. We first briefly review the history of AI since Gödel’s 1931 paper, then discuss recent post2000 approaches that are currently transforming general AI research into a formal science.
The Fastest Way of Computing All Universes
"... Is there a short and fast program that can compute the precise history of our universe, including all seemingly random but possibly actually deterministic and pseudorandom quantum fluctuations? There is no physical evidence against this possibility. So let us start searching! We already know a shor ..."
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Is there a short and fast program that can compute the precise history of our universe, including all seemingly random but possibly actually deterministic and pseudorandom quantum fluctuations? There is no physical evidence against this possibility. So let us start searching! We already know a short program that computes all constructively computable universes in parallel, each in the asymptotically fastest way. Assuming ours is computed by this optimal method, we can predict that it is among the fastest compatible with our existence. This yields testable predictions. Note: This paper extends an overview of previous work 51–54,58,59 presented in a survey for the German edition of Scientific American. 61 1.