How to Use YTEX

Work on YTEX was supported by a desire to avoid doing real work, like research.YTEX—pronounced why-TEX or oops-TEX—is a TEX macro package. YTEX provides both an easy-to-use interface for TEX novices and a powerful macro-creation library for TEX programmers. It is this two-tier structure that makes YTEX more useful to a diverse TEX user community than other macro packages such as Plain or LaTEX. This paper contains YTEX instructions intended for novice users. It summarizes the facilities provided by YTEX and concludes with a table of useful commands. The version of YTEX documented her is release 2.0.MIT Artificial Intelligence Laborator

Program Understanding through Cliché Recognition

We propose research into automatic program understanding via recognition of common data structures and algorithms (clichés). Our goals are two-fold: first, to develop a theory of program structure which makes such recognition tractable; and second, to produce a program (named Inspector) which, given a Lisp program and a library of clichés, will construct a hierarchical decomposition of the program in terms of the clichés it uses. Our approach involves assuming constraints on the possible decompositions of programs according to the teleological relations between their parts. Programs are analyzed by translating them into a language-independent form and then parsing this representation in accordance with a context-free web grammar induced by the library of clichés. Decompositions produced by this analysis will in general be partial, since most programs will not be made up entirely of clichés. This work is motivated by the belief that identification of clichés used in program, together with knowledge of their properties, provides a sufficient basis for understanding large parts of that program's behavior. Inspector will become one component of a system of programs known as a programmer's apprentice, in which Inspector's output will be used to assist a programmer with program synthesis, debugging, and maintenance.MIT Artificial Intelligence Laborator

The Role of Intensional and Extensional Representations in Simulation

This paper was prepared as the author's area examination.I review three systems which do simulation in different domains. I observe the following commonality in the representations underlying the simulations: • The representations used for individuals tend to be domain-dependent. These representations are highly structured, concentrating in one place all the information concerning any particular individual. I call these representations intensional because two such representations are considered equal if their forms are identical. • With important exceptions, the representations used for classes of individuals tend to be domain-independent. These representations are unstructured sets of predications involving the characteristics of class members. I call these representations extensional because two such representations are considered equal if the classes they specify are identical. I draw out various ramifications of this dichotomy, and speculate as to its cause. In conclusion, I suggest research into the process of debugging extensional class representations and the development of intensional ones.MIT Artificial Intelligence Laborator

An Algorithm for Parsing Flow Graphs

This report describes research about flow graphs - labeled, directed, acyclic graphs which abstract representations used in a variety of Artificial Intelligence applications. Flow graphs may be derived from flow grammars much as strings may be derived from string grammars; this derivation process forms a useful model for the stepwise refinement processes used in programming and other engineering domains. The central result of this report is a parsing algorithm for flow graphs. Given a flow grammar and a flow graph, the algorithm determines whether the grammar generates the graph and, if so, finds all possible derivations for it. The author has implemented the algorithm in LISP. The intent of this report is to make flow-graph parsing available as an analytic tool for researchers in Artificial Intelligence. The report explores the intuitions behind the parsing algorithm, contains numerous, extensive examples of its behavior, and provides some guidance for those who wish to customize the algorithm to their own uses

Analysis of discriminative control by social behavioral stimuli

Visual discriminative control of the behavior of one rat by the behavior of another was studied in a two-compartment chamber. Each rat's compartment had a food cup and two response keys arranged vertically next to the clear partition that separated the two rats. Illumination of the leader's key lights signaled a “search” period when a response by the leader on the unsignaled and randomly selected correct key for that trial illuminated the follower's keys. Then, a response by the follower on the corresponding key was reinforced, or a response on the incorrect key terminated the trial without reinforcement. Accuracy of following the leader increased to 85% within 15 sessions. Blocking the view of the leader reduced accuracy but not to chance levels. Apparent control by visual behavioral stimuli was also affected by auditory stimuli and a correction procedure. When white noise eliminated auditory cues, social learning was not acquired as fast nor as completely. A reductionistic position holds that behavioral stimuli are the same as nonsocial stimuli; however, that does not mean that they do not require any separate treatment. Behavioral stimuli are usually more variable than nonsocial stimuli, and further study is required to disentangle behavioral and nonsocial contributions to the stimulus control of social interactions