: Knowledge Interchange Format (KIF) is a computer-oriented language for the interchange of knowledge among disparate programs. It has declarative semantics (i.e. the meaning of expressions in the representation can be understood without appeal to an interpreter for manipulating those expressions

We give a precise semantics for a proposed revised version of the Knowledge Interchange Format. We show that quantification over relations is possible in a first-order logic, but sequence variables take the language beyond first-order.

There has been a good deal of discussion recently about the possibility of standardizing knowledge representation efforts, including the development of an interlingua, or KIF (knowledge interchange format) that would allow developers of declarative knowledge to share their results with other AI

Knowledge Interchange Format (KIF) is a computer-oriented language for the interchange of knowledge among disparate programs. It has declarative semantics (i.e. the meaning of expressions in the representation can be understood without appeal to an interpreter for manipulating those expressions

this paper we show through several examples that two versions of KIF failed to do so, and that this failure had disastrous results with respect to the expressiveness, scalability, and most importantly, correctness of representations of the meanings of common mathematical operators in these languages.

, Knowledge Interchange Format.

This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation algorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory

Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforcement learning and Markov decision processes (MDPs). We

In response to the proposition that communication lacks disciplinary status because of deficient core knowledge, I propose that we turn an ostensible weakness into a strength. We should identify our mission as bringing together insights and theories that would otherwise remain scattered in other

Recommender systems apply statistical and knowledge discovery techniques to the problem of making product recommendations during a live customer interaction and they are achieving widespread success in E-Commerce nowadays. In this paper, we investigate several techniques for analyzing large