Read or Download Advances in Computers, Vol. 24 PDF

Similar artificial intelligence books

Reasoning about Uncertainty

Uncertainty is a primary and unavoidable function of lifestyle; so as to take care of uncertaintly intelligently, we have to be capable of symbolize it and cause approximately it. during this publication, Joseph Halpern examines formal methods of representing uncertainty and considers a number of logics for reasoning approximately it. whereas the guidelines offered are formalized by way of definitions and theorems, the emphasis is at the philosophy of representing and reasoning approximately uncertainty; the fabric is out there and correct to researchers and scholars in lots of fields, together with laptop technological know-how, synthetic intelligence, economics (particularly online game theory), arithmetic, philosophy, and statistics. Halpern starts by way of surveying attainable formal platforms for representing uncertainty, together with chance measures, threat measures, and plausibility measures. He considers the updating of ideals in accordance with altering info and the relation to Bayes' theorem; this results in a dialogue of qualitative, quantitative, and plausibilistic Bayesian networks. He considers not just the uncertainty of a unmarried agent but additionally uncertainty in a multi-agent framework. Halpern then considers the formal logical structures for reasoning approximately uncertainty. He discusses wisdom and trust; default reasoning and the semantics of default; reasoning approximately counterfactuals, and mixing chance and counterfactuals; trust revision; first-order modal common sense; and facts and ideology. He incorporates a sequence of workouts on the finish of every chapter.

Genetic Programming Theory and Practice XII (Genetic and Evolutionary Computation)

Those contributions, written via the major overseas researchers and practitioners of Genetic Programming (GP), discover the synergy among theoretical and empirical effects on real-world difficulties, generating a entire view of the state-of-the-art in GP. issues during this quantity contain: gene expression law, novel genetic versions for glaucoma, inheritable epigenetics, combinators in genetic programming, sequential symbolic regression, procedure dynamics, sliding window symbolic regression, huge function difficulties, alignment within the mistakes house, HUMIE winners, Boolean multiplexer functionality, and hugely disbursed genetic programming structures.

Temporal Logic of Programs

Advent to the temporal good judgment of - specifically paral- lel - courses. Divided into 3 major elements: - Presenta- tion of the natural temporal good judgment: language, semantics, and facts thought; - illustration of courses and their right- ties in the language of temporal common sense; - program of the logical gear to the verification of software right- ties together with a brand new embedding of Hoare's good judgment into the temporal framework.

Innovations and approaches for resilient and adaptive systems

"This ebook is a accomplished selection of wisdom on expanding the notions and types in adaptive and loyal platforms, bettering the notice of the function of adaptability and resilience in procedure environments"--Provided via writer. summary: "This e-book is a finished choice of wisdom on expanding the notions and versions in adaptive and loyal platforms, bettering the attention of the function of adaptability and resilience in approach environments"--Provided by means of writer

Additional info for Advances in Computers, Vol. 24

Example text

16). These observations show that there is a close relationship among belief functions, inner measures, and lower probabilities. 1 Given a belief function Bel defined on a space W , let PBel = {µ : µ(U ) ≥ Bel(U ) for all U ⊆ W }. Then Bel = (PBel )∗ and Plaus = (PBel )∗. 23. 1 shows that every belief function on W can be viewed as a lower probability of a set of probability measures on W . 1). 1 does not hold. 8), and thus there is a space W and a set P of probability measures on W such that no belief function Bel on W with Bel = P∗ exists.

7). Intuitively, µ∗(U ) is the best approximation to the actual probability of U from below and µ∗(U ) is the best approximation from above. If U ∈ F, then it is easy to see that µ∗(U ) = µ∗(U ) = µ(U ). If U ∈ F − F then, in general, µ∗(U ) < µ∗(U ). 7, since the largest measurable set contained in {blue} is the empty set, while the smallest measurable set containing blue is {blue, yellow}. 3. These are precisely the same numbers obtained using the lower and upper probabilities (P2)∗ and (P2)∗.

It follows from RAT1 that (U , 0) (U , 1). As observed earlier, (U , α) gets less attractive as α gets larger, and (U , 1 − α) gets more attractive as α gets larger. Since, by RAT1, (U , 0) (U , 1), it easily follows that there is there is some point α ∗ at which, roughly speaking, (U , α ∗) and (U , 1 − α ∗) are in balance. I take αU to be α ∗. I need a few more definitions to make this precise. Given a set X of real numbers, let sup X, the supremum (or just sup) of X, be the least upper bound of X—the smallest real number that is at least as large as all the elements in X.

Download PDF sample

Rated 4.53 of 5 – based on 30 votes