Very ambitious project. , [review from amazon.com]
This book differs from most books on the theoretical formulations of artificial intelligence
in that it attempts to give a more rigorous accounting of machine learning and to rank machines according to their intelligence.
To accomplish this ranking, the author introduces a concept called `universal artificial intelligence,' which is constructed
in the context of algorithmic information theory. In fact, the book could be considered to be a formulation of artificial
intelligence from the standpoint of algorithmic information theory, and is strongly dependent on such notions as Kolmogorov
complexity, the Solomonoff universal prior, Martin-Lof random sequences and Occam's razor. These are all straightforward mathematical
concepts with which to work with, the only issue for researchers being their efficacy in giving a useful notion of machine
intelligence.
The author begins the book with a "short tour" of what will be discussed in the book, and this serves as helpful motivation for the reader. The reader is expected to have a background in algorithmic information theory, but the author does give a brief review of it in chapter two. In addition, a background in sequential decision theory and control theory would allow a deeper appreciation of the author's approach. In chapter four, he even gives a dictionary that maps concepts in artificial intelligence to those in control theory. For example, an `agent' in AI is a `controller' in control theory, a `belief state' in AI is an `information state' in control theory, and `temporal difference learning' in AI is `dynamic programming' or `value/policy iteration' in control theory. Most interestingly, this mapping illustrates the idea that notions of learning, exploration, adaptation, that one views as "intelligent" can be given interpretations that one does not normally view as intelligent. The re-interpretation of `intelligent' concepts as `unintelligent' ones is typical in the history of AI and is no doubt responsible for the belief that machine intelligence has not yet been achieved.
The author's formulations are very dependent on the notion of Occam's razor with its emphasis on simple explanations. The measurement of complexity that is used in algorithmic information theory is that of Kolmogorov complexity, which one can use to measure the a prior plausibility of a particular string of symbols. The author though wants to use the `Solomonoff universal prior', which is defined as the probability that the output of a universal Turing machine starts with the string when presented with fair coin tosses on the input tape. As the author points out, this quantity is however not a probability measure, but only a `semimeasure', since it is not normalized to 1, but he shows how to bound it by expressions involving the Kolmogorov complexity.
The author begins the book with a "short tour" of what will be discussed in the book, and this serves as helpful motivation for the reader. The reader is expected to have a background in algorithmic information theory, but the author does give a brief review of it in chapter two. In addition, a background in sequential decision theory and control theory would allow a deeper appreciation of the author's approach. In chapter four, he even gives a dictionary that maps concepts in artificial intelligence to those in control theory. For example, an `agent' in AI is a `controller' in control theory, a `belief state' in AI is an `information state' in control theory, and `temporal difference learning' in AI is `dynamic programming' or `value/policy iteration' in control theory. Most interestingly, this mapping illustrates the idea that notions of learning, exploration, adaptation, that one views as "intelligent" can be given interpretations that one does not normally view as intelligent. The re-interpretation of `intelligent' concepts as `unintelligent' ones is typical in the history of AI and is no doubt responsible for the belief that machine intelligence has not yet been achieved.
The author's formulations are very dependent on the notion of Occam's razor with its emphasis on simple explanations. The measurement of complexity that is used in algorithmic information theory is that of Kolmogorov complexity, which one can use to measure the a prior plausibility of a particular string of symbols. The author though wants to use the `Solomonoff universal prior', which is defined as the probability that the output of a universal Turing machine starts with the string when presented with fair coin tosses on the input tape. As the author points out, this quantity is however not a probability measure, but only a `semimeasure', since it is not normalized to 1, but he shows how to bound it by expressions involving the Kolmogorov complexity.