If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
We argue that the time between mission conception and implementation can be radically reduced, that launch mass can be slashed, that totally autonomous robots can be more reliable than ground controlled robots, and that large numbers of robots can change the tradeoff between reliability of individual components and overall mission success. Lastly, we suggest that within a few years it will be possible at modest cost to invade a planet with millions of tiny robotsJournal of The British Interplanetary Society, Vol. 42, pp 478-485
Valiant’s learnability model is extended to learning classes of concepts defined by regions in Euclidean space E”. The methods in this paper lead to a unified treatment of some of Valiant’s results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufftcient conditions are provided for feasible learnability.JACM, 36 (4), 929-65
Artificial intelligence has progressed to the point where multiple cognitive capabilities are being integrated into computational architectures, such as SOAR, PRODIGY, THEO, and ICARUS. Learning in PRODIGY occurs at all decision points and integration in PRODIGY is at the knowledge level; the learning and reasoning modules produce mutually interpretable knowledge structures. Issues in architectural design are discussed, providing a context to examine the underlying tenets of the PRODIGY architecture.
This paper presents heuristic search algorithms which work within memory constraints. These algorithms, MA∗ (for ordinary graphs) and MAO∗ (for AND/OR graphs) guarantee admissible solutions within specified memory limitations (above the minimum required). The memory versus node expansions tradeoff is analyzed for the worst case. In the case of ordinary graphs, some experiments using the Fifteen Puzzle problem are carried out under various pruning conditions. These parameterized algorithms are found to encompass a wide class of best first search algorithms.
We address the question of when a network can be expected to generalize from m random training examples chosen from some arbitrary probability distribution, assuming that future test examples are drawn from the same distribution. Among our results are the following bounds on appropriate sample vs. network size. We show that if m O(W/ log N/) random examples can be loaded on a feedforward network of linear threshold functions with N nodes and W weights, so that at least a fraction 1 /2 of the examples are correctly classified, then one has confidence approaching certainty that the network will correctly classify a fraction 1 of future test examples drawn from the same distribution. Conversely, for fully-connected feedforward nets with one hidden layer, any learning algorithm using fewer than Ω(W/) random training examples will, for some distributions of examples consistent with an appropriate weight choice, fail at least some fixed fraction of the time to find a weight choice that will correctly classify more than a 1 fraction of the future test examples.
The automatic interpretation of natural language (in this work, English), database questions formulated by a user untrained in the technical aspects of database querying is an established problem in the field of artificial intelligence. State-of-the-art approaches involve the analysis of queries with syntactic and semantic grammars expressed in phrase structure grammar or transition network formalisms. With such method difficulties exist with the detection and resolution of ambiguity, with the misinterpretation possibilities inherent with finite length look-ahead, and with the modification and extension of a mechanism for other sources of semantic knowledge. This work examines the potential of optimization techniques tomore » solve these problems and interpret natural language, database queries. The proposed method involves developing a 0-1 integer programming problem for each query.
A class of connectionist networks is described that has learned to play backgammon at an intermediate-to-advanced level. The networks were trained by back-propagation learning on a large set of sample positions evaluated by a human expert. In actual match play against humans and conventional computer programs, the networks have demonstrated substantial ability to generalize on the basis of expert knowledge of the game. This is possibly the most complex domain yet studied with connectionist learning. New techniques were needed to overcome problems due to the scale and complexity of the task.