1800673
Why AM and Eurisko Appear to Work
Seven years ago, the AM program was constructed as an experiment in learning by discovery. Its source of power was a large body of heuristics, rules which guided it toward fruitful topics of investigation, toward profitable experiments to perform, toward plausible hypotheses and definitions. "Other heuristics evaluated those discoveries for utility and "interestingness", and they were added to AM's vocabulary of concepts. AM's ultimate limitation apparently was due to its inability to discover new, powerful, domain-specific heuristics for the various new fields it uncovered. At that time, it seemed straight-forward to simply add Heuretics (the study of heuristics) as one more field in which to let AM explore, observe, define, and develop. That task - learning new heuristics by discovery - turned out to be much more difficult than was realized initially, and we have just now achieved some successes at it. Along the way, it became clearer why AM had succeeded in the first place, and why it was so difficult to use the same paradigm to discover new heuristics. This paper discusses those recent insights. They spawn questions about "where the meaning really resides" in the concepts discovered by AM. This leads to an appreciation of the crucial and unique role of representation in theory formation, a role involving the relationship between Form and Content.
27879633
To dream the invisible dream
often credited as the creator of the computer game industry, explores the “Relationships between Fun and the Computer Business.” In his article he reminisces, assesses, and projects the effects from the game industry to the computer industry as a whole. Bushnell’s article makes tangible some of the incredible distance we’ve come since the 1970s—from a world where manufacturers didn’t take cathode-ray tube use in the computer industry seriously to a time when most two-year-old children know about computers. Bushnell’s way is to mix lessons learned from the game industry with some as-yet unexplored visions of the way we will play. Henry Lieberman’s work has been that of creating programming and idea presentation environments using his favorite tools of creation objects (his work on actors), dynamic languages (his work on garbage collection), and interactive environments (his work on programming by example). Lieberman’s article, “Intelligent Graphics,” focuses on the inventive serendipity that has and can come out of the relationship between AI and graphics—an idea promoted by yet another pioneer, Muriel Cooper. He also briefly discusses programming by example, a style of interaction that he has been working to demonstrate and promote over the last couple of decades. Ken Kahn also cares deeply about programming languages, having devoted his research career to creating Prolog interpreters, parallel logic systems, and logic-oriented systems. In his article, we see him engaged in his first love, that of creating visual approaches for thinking about programs. Watching programs execute, seeing their structure, seeing their function, organizing them—all with the underlying goal of making programming as easy as child’s play. Finally, as you look to my article, you see me not working or talking about any one idea as I could, but giving a review with some context that describes many of the ideas shaping the way people are using computers today. I hope this eclectic collection of articles can serve as a celebration of our temptations to explore and design the ways computers will help us communicate, create, and record our experiences.
38979633
A Qualitative Physics Based on Confluences
Abstract A qualitative physics predicts and explains the behavior of mechanisms in qualitative terms. The goals for the qualitative physics are (1) to be far simpler than the classical physics and yet retain all the important distinctions (e.g., state, oscillation, gain, momentum) without invoking the mathematics of continuously varying quantities and differential equations, (2) to produce causal accounts of physical mechanisms that are easy to understand, and (3) to provide the foundations for commonsense models for the next generation of expert systems. This paper presents a fairly encompassing account of qualitative physics. First, we discuss the general subject of naive physics and some of its methodological considerations. Second, we present a framework for modeling the generic behavior of individual components of a device based on the notions of qualitative differential equations (confluences) and qualitative state. This requires developing a qualitative version of the calculus. The modeling primitives induce two kinds of behavior, intrastate and interstate, which are governed by different laws. Third, we present algorithms for determining the behavior of a composite device from the generic behavior of its components. Fourth, we examine a theory of explanation for these predictions based on logical proof. Fifth, we introduce causality as an ontological commitment for explaining how devices behave.
45989851
The Social Life of Documents
Documents are evolving as means for structuring and navigating information space, and resources for constructing and negotiating social space
7675324
Applications of Artificial Intelligence
One of the aims of artificial intelligence has been to make machines behave intelligently as humans do. Thus the study of artificial intelligence includes the study of how humans acquire and apply knowledge, reason under uncertainty and in complex environments, and how they do planning and solve problems. Knowledge-based automation deals with the application of artificial intelligence to a production environment in order to reduce the involvement of human beings to a minimum.
7478879
G-functions for the hermeneutic circle of evolution
Evolution by natural selection may include both frequency and density-dependence. Frequency-dependent selection is a kind of hermeneutic circle. As a literary term (and a school of philosophy), a hermeneutic circle describes how each word of a sentence or paragraph simultaneously draws its meaning from its context (the other words) and contributes towards the context. Frequency dependence is like this. Under frequency dependence, the fitness of an individual is not only influenced by its own heritable phenotype (in evolutionary game theory heritable phenotype = strategy) but by the frequency of strategies found among others in the population. And, the fitness accrued by individuals with a particular strategy will in time influence the frequency of strategies in the population. The challenge is to develop conceptual and modeling tools that allow us to determine the outcome of this hermeneutic circle of evolution. We need a fitness concept that defines the fitness of (or payoff to) to individuals as influenced by both their own strategy and the strategies of others. And, this fitness formulation must describe how these payoffs translate into changes in strategy frequencies. The fitness generating function, or G-function, provides this service for evolutionary game theory.
7379001
The ribonuclease P database
The Ribonuclease P Sequence database is a compilation of RNase P sequences, sequence alignments, secondary structures, three-dimensional models, and accessory information. In its initial form, the database contains information on RNase P RNA in bacteria and archaea, and RNase P protein in bacteria. The sequences themselves are presented phylogenetically ordered and aligned. The database also contains secondary structures of bacterial and archaeal RNAs, including specially annotated 'reference' secondary structures of Escherichia coli and Bacillus subtilis RNase P RNAs, a minimum phylogenetic consensus structure, and coordinates for models of three-dimensional structure.
44284809
The strongly connected reliability of complete digraphs
Given a digraph D, consider the model where each vertex is always operational, but the edges are independently operational with probability p. The strongly connected reliability of D, scRel(D, p), is the probability that the spanning subgraph of D consisting of the operational edges is strongly connected. One can view strongly connected reliability as the probability that any vertex can send information to any other vertex, given that edges fail independently. ∗Partially supported by a grant from NSERC. Communicating author.
19128559
Bounding the Roots of Independence Polynomials