5243593
Steps Toward Automatic Theory Formation
This paper describes a theory formation system which can discover a partial axiomization of a data base represented as extensionally defined binary relations.- The system first discovers all possible intensional definitions of each binary relation in terms of the others. It then determines a minimal set of these relations from which the others can be defined. It then attempts to discover all the ways the relations of this minimal set can interact with each other, thus generating a set of inference rules. Although the system was originally designed to explore automatic techniques for theory construction for question-answering systems, it is currently being expanded to function as a symbiotic system to help social scientists explore certain kinds of data bases.
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
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.
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.
15658893
Theories of Causal Ordering
Abstract This paper is a response to Iwasaki and Simon [14] which criticizes de Kleer and Brown [8]. We argue that many of their criticisms, particularly concerning causality, modeling and stability, originate from the difference of concerns between engineering and economics. Our notion of causality arises from considering the interconnections of components, not equations. When no feedback is present, the ordering produced by our qualitative physics is similar to theirs. However, when feedback is present, our qualitative physics determines a causal ordering around feedback loops as well. Causal ordering is a general technique not only applicable to qualitative reasoning. Therefore we also explore the relationship between causal ordering and propagation of constraints upon which the methods of qualitative physics are based.
17008578
Foundations of Envisioning
This paper explores a particular kind of qualitative reasoning, called envisioning, that is capable of producing causal explanations for device behavior. It has been implemented in a computer program, ENVISION, which can analyze a wide variety of thermal, fluid, electrical, translational and rotational devices. Rather than present the technical details of the envisioning process, this paper examines the theoretical foundations upon which it is built. Many of these considerations are ones that any builder of qualitative reasoning systems must pay attention to. Two such considerations are explanation and robustness: What notion of causality is adequate for causal explanations of device behavior? How can there be any confidence in the analysis of a novel device?
7956229
How laminar frontal cortex and basal ganglia circuits interact to control planned and reactive saccades
How does the brain learn to balance between reactive and planned behaviors? The basal ganglia (BG) and frontal cortex together allow animals to learn planned behaviors that acquire rewards when prepotent reactive behaviors are insufficient. This paper proposes a new model, called TELOS, to explain how laminar circuitry of the frontal cortex, exemplified by the frontal eye fields, interacts with the BG, thalamus, superior colliculus, and inferotemporal and parietal cortices to learn and perform reactive and planned eye movements. The model is formulated as fourteen computational hypotheses. These specify how strategy priming and action planning (in cortical layers III, Va and VI) are dissociated from movement execution (in layer Vb), how the BG help to choose among and gate competing plans, and how a visual stimulus may serve either as a movement target or as a discriminative cue to move elsewhere. The direct, indirect and hyperdirect pathways through the BG are shown to enable complex gating functions, including deferred execution of selected plans, and switching among alternative sensory-motor mappings. Notably, the model can learn and gate the use of a What-to-Where transformation that enables spatially invariant object representations to selectively excite spatially coded movement plans. Model simulations show how dopaminergic reward and non-reward signals guide monkeys to learn and perform saccadic eye movements in the fixation, single saccade, overlap, gap, and delay (memory-guided) saccade tasks. Model cell activation dynamics quantitatively simulate seventeen established types of dynamics exhibited by corresponding real cells during performance of these tasks.
7522575
Handling long-latency loads in a simultaneous multithreading processor
Simultaneous multithreading architectures have been defined previously with fully shared execution resources. When one thread in such an architecture experiences a very long-latency operation, such as a load miss, the thread will eventually stall, potentially holding resources which other threads could be using to make forward progress.This paper shows that in many cases it is better to free the resources associated with a stalled thread rather than keep that thread ready to immediately begin execution upon return of the loaded data. Several possible architectures are examined, and some simple solutions are shown to be very effective, achieving speedups close to 6.0 in some cases, and averaging 15% speedup with four threads and over 100% speedup with two threads running. Response times are cut in half for several workloads in open system experiments.
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.
267100
Proximity-aware directory-based coherence for multi-core processor architectures
As the number of cores increases on chip multiprocessors, coherence is fast becoming a central issue for multi-core performance. This is exacerbated by the fact that interconnection speeds are not scaling well with technology. This paper describes mechanisms to accelerate coherence for a multi-core architecture that has multiple private L2 caches and a scalable point-to-point interconnect between cores. These techniques exploit the differences in geometry between chip multiprocessors and traditional multiprocessor architectures.
 Directory-based protocols have been proposed as a scalable alternative to snoop-based protocols. In this paper, we discuss implementations of coherence for CMPs and propose and evaluate a novel directory-based coherence scheme to improve the performance of parallel programs on such processors. Proximity-aware coherence accelerates read and write misses by initiating cache-to-cache transfers from the spatially closest sharer. This has the dual benefit of eliminating unnecessary accesses to off-chip memory, and minimizing the distance over which communicated data moves across the network. The proposed schemes result in speedups up to 74.9% for our workloads.
45045031
Well-Covered Vector Spaces of Graphs
For any field ${\bf F}$, the set of all functions $f : V(G) \rightarrow {\bf F}$ whose sum on each maximal independent set is constant forms a vector space over ${\bf F}$. In this paper, we show that the dimension can vary depending on the characteristic of the field. We also investigate the dimensions of these vector spaces and show that while some families, such as chordal graphs, have unbounded dimension, other families, such as nonempty circulant graphs of prime order, have bounded dimension.