J. H. Conway, On Numbers and Games, Academic Press, New In this article, a number of concepts that are of importance York, 1976. in research on game-playing programs have been J. H. Conway, "All Games Bright and Beautiful," Am.

Step 6 is a goal-assertion the input, another algorithm might result. Thus one could resolution that functions similarly to the goal-goal resolution break a into a[1],..., a [length(a)/2] and a [length(a)/ above. The final synthesized program is: 2 1],..., a[length(a)] and find an algorithm that recursively calls f on both the first and second halves of its f(x) if x NIL then 0 else car(x) f(cdr(x)).

This chapter explores a number of issues involving representation and use of what we term meta-level knowledge, or knowledge about knowledge.1 It begins by defining the term, then exploring a few of its varieties and considering the range of capabilities it makes possible. Four specific examples of meta-level knowledge are described, and a demonstration given of their application to a number of problems, including interactive transfer of expertise and the "intelligent" use of knowledge. Finally, we consider the long-term implications of the concept and its likely impact on the design of large programs. The context of this work is the TEIRESIAS program discussed in Chapter 9. In the earlier chapter we focused on the use of TEIRESIAS for knowledge acquisition. Here we focus on the classification and types of knowledge used by TEIRESIAS. In the most general terms, meta-level knowledge is knowledge about knowledge. Its primary use here is to enable a program to "know what it knows," and to make multiple uses of its knowledge. As mentioned in Chapter 9, the program is not only able to use its knowledge directly, but may also be able to examine it, abstract it, reason about it, or direct its application. This chapter discusses examples of meta-level knowledge classified along two dimensions: (i) specificity character (representation-specific vs. domain-specific), and (ii) source (user-supplied vs. derived). Representation-specific meta-level knowledge involves supplying a program with a store of knowledge dealing with the form of its representations, in particular, their design and organization. Traditionally, this design and organization infor-This chapter is an expanded and edited version of a paper originally appearing in Proceedings of the Fifth IJCAL 1977, pp. Used by permission of International Joint Conferences on Artificial Intelligence, Inc.; copies of the Proceedings are available from William Kaufmann, Inc., 95 First Street, Los Altos, CA 94022. IFollowing standard usage, knowledge about objects and relations in a particular domain will be referred to as object-level knowledge. Type declarations are a small step toward more explicit specification of this information, especially as they are used in extended data types and record structures. As we discuss below, this sort of information, along with a range of other facts about representation design, can be employed quite usefully if it is made explicit and made available to the system.

IDijkstra 19591 Dijkstra, E., NA Note on Two Problems in Connection with Graphs," Numeri.scht'

It is no pure coincidence that an organic chemist rather than a computer specialist is writing this foreword.

We are motivated by problems that arise in a number of applications such as Online Marketing and Explosives detection, where the observations are usually modeled using Poisson statistics. We model each observation as a Poisson random variable whose mean is a sparse linear superposition of known patterns. Unlike many conventional problems observations here are not identically distributed since they are associated with different sensing modalities. We analyze the performance of a Maximum Likelihood (ML) decoder, which for our Poisson setting involves a non-linear optimization but yet is computationally tractable. We derive fundamental sample complexity bounds for sparse recovery when the measurements are contaminated with Poisson noise. In contrast to the least-squares linear regression setting with Gaussian noise, we observe that in addition to sparsity, the scale of the parameters also fundamentally impacts $\ell_2$ error in the Poisson setting. We show tightness of our upper bounds both theoretically and experimentally. In particular, we derive a minimax matching lower bound on the mean-squared error and show that our constrained ML decoder is minimax optimal for this regime.

While in classical planning the objective is to achieve one of the equally attractive goal states at as low total action cost as possible, the objective in deterministic oversubscription planning (OSP) is to achieve an as valuable as possible subset of goals within a fixed allowance of the total action cost. Although numerous applications in various fields share the latter objective, no substantial algorithmic advances have been made in deterministic OSP. Tracing the key sources of progress in classical planning, we identify a severe lack of effective domain-independent approximations for OSP. With our focus here on optimal planning, our goal is to bridge this gap. Two classes of approximation techniques have been found especially useful in the context of optimal classical planning: those based on state-space abstractions and those based on logical landmarks for goal reachability. The question we study here is whether some similar-in-spirit, yet possibly mathematically different, approximation techniques can be developed for OSP. In the context of abstractions, we define the notion of additive abstractions for OSP, study the complexity of deriving effective abstractions from a rich space of hypotheses, and reveal some substantial, empirically relevant islands of tractability. In the context of landmarks, we show how standard goal-reachability landmarks of certain classical planning tasks can be compiled into the OSP task of interest, resulting in an equivalent OSP task with a lower cost allowance, and thus with a smaller search space. Our empirical evaluation confirms the effectiveness of the proposed techniques, and opens a wide gate for further developments in oversubscription planning.

We consider using an ensemble of binary classifiers for transductive prediction, when unlabeled test data are known in advance. We derive minimax optimal rules for confidence-rated prediction in this setting. By using PAC-Bayes analysis on these rules, we obtain data-dependent performance guarantees without distributional assumptions on the data. Our analysis techniques are readily extended to a setting in which the predictor is allowed to abstain.

The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design. In this paper, we propose a new protein design algorithm based on the AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the design process. Tests on real protein data show that our new protein design algorithm is able to solve many prob- lems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.

This article is an overview of the "SP theory of intelligence". The theory aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It is conceived as a brain-like system that receives 'New' information and stores some or all of it in compressed form as 'Old' information. It is realised in the form of a computer model -- a first version of the SP machine. The concept of "multiple alignment" is a powerful central idea. Using heuristic techniques, the system builds multiple alignments that are 'good' in terms of information compression. For each multiple alignment, probabilities may be calculated. These provide the basis for calculating the probabilities of inferences. The system learns new structures from partial matches between patterns. Using heuristic techniques, the system searches for sets of structures that are 'good' in terms of information compression. These are normally ones that people judge to be 'natural', in accordance with the 'DONSVIC' principle -- the discovery of natural structures via information compression. The SP theory may be applied in several areas including 'computing', aspects of mathematics and logic, representation of knowledge, natural language processing, pattern recognition, several kinds of reasoning, information storage and retrieval, planning and problem solving, information compression, neuroscience, and human perception and cognition. Examples include the parsing and production of language including discontinuous dependencies in syntax, pattern recognition at multiple levels of abstraction and its integration with part-whole relations, nonmonotonic reasoning and reasoning with default values, reasoning in Bayesian networks including 'explaining away', causal diagnosis, and the solving of a geometric analogy problem.