Full text available for a fee. The main purpose of the research reported here is to show that a new and more powerful type of computer-assisted instruction (CAI), based on extensive application of artificial-intelligence (AI) techniques, is feasible, and to demonstrate some of its major capabilities. A set of computer programs was written and given the name SCHOLAR. Due to its complexity, only the conception and educational aspects of this system (including an actual on-line protocol) are presented in this paper. IEEE Transactions on Man-Machine Systems MMS-ll:190-202

Full text available for a fee. "The use of augmented transition network grammars for the analysis of natural language sentences is described. Structure-building actions associated with the arcs of the grammar network allow for the reordering, restructuring, and copying of constituents necessary to produce deep-structure representations of the type normally obtained from a transformational analysis, and conditions on the arcs allow for a powerful selectivity which can rule out meaningless analyses and take advantage of semantic information to guide the parsing. The advantages of this model for natural language analysis are discussed in detail and illustrated by examples. An implementation of an experimental parsing system for transition network grammars is briefly described." Communications of the ACM, Vol. 13, No. 10, October, 1970, pp. 591-606 (reprinted in RNLP: 71-88).

This paper explores new approaches to the symmetric traveling-salesman problem in which 1-trees, which are a slight variant of spanning trees, play an essential role. A 1-tree is a tree together with an additional vertex connected to the tree by two edges. We observe that (i) a tour is precisely a 1-tree in which each vertex has degree 2, (ii) a minimum 1-tree is easy to compute, and (iii) the transformation on “intercity distances” cij → Cij + πi + πj leaves the traveling-salesman problem invariant but changes the minimum 1-tree. Operations Research, 18, 1138–1162.

The M & N procedure is an improvement to the mini-max backing-up procedure widely used in computer programs for game-playing and other purposes. It is based on the principle that it is desirable to have many options when making decisions in the face of uncertainty. The mini-max procedure assigns to a MAX (MIN) node the value of the highest (lowest) valued successor to that node. The M & N procedure assigns to a MAX (MIN) node some function of the M (N) highest (lowest) valued successors. An M & N procedure was written in LISP to play the game of kalah, and it was demonstrated that the M & N procedure is significantly superior to the mini-max procedure.

Kuhn (1962) has persuasively argued that science progresses by means of its paradigms--its models of the general nature of a research area--and that at the frontiers of research the primary quest is for a good paradigm. The small frontier outpost of language data processing has been characterized by an intensive seeking for a paradigm suitable to guide its researchers as they survey the complex topography of natural language structures. The earliest paradigm--one that led mechanical translators and early information retrievalists into a hopeless cul-de-sac--was that words (i.e.

A parsing algorithm which seems to be the most efficient general context-free algorithm known is described. It is similar to both Knuth's LR(k) algorithm and the familiar top-down algorithm. It has a time bound proportional to n3 (where n is the length of the string being parsed) in general; it has an n2 bound for unambiguous grammars; and it runs in linear time on a large class of grammars, which seems to include most practical context-free programming language grammars. In an empirical comparison it appears to be superior to the top-down and bottom-up algorithms studied by Griffiths and Petrick.

Russian Mathematical Surveys, 25, 221-262

Some of the world's most advanced artificial intelligence (AI) systems, at least the ones the public hear about, are famous for beating human players at chess or poker. Other algorithms are known for their ability to learn how to recognize cats or their inability to recognize people with darker skin. But are current AI systems anything more than toys? Sure, their ability to play games or identify animals is impressive, but does this help toward creating useful AI systems? To answer this, we need to take a step back and question what the goals of AI are.

Artificial Intelligence(AI), a very old subject in computer science, explores how to make machines intelligent. Machine learning(ML) is a subset of AI, which focuses on teaching machines how to learn patterns, based on given a set of data. In short, try to replicate, how humans learn new skills or make decisions. Good news is, AL/ML is not restricted to Sci-Fi movies OR big corporations. Due to explosive growth in hardware capabilities, open source libraries and ability to pay per use model to access this computing power, AI/ML is now within reach of small businesses.

Time is a critical context for understanding how things function. It serves as the digital history for businesses. When you think about institutional knowledge, that's not just bound up in people. Data is part of that knowledge base as well. So, when companies can capture, store and analyze that data in an effective way, it produces better results.