Generic task problem solvers in Soar

Two trends can be discerned in research in problem solving architectures in the last few years. On one hand, interest in task-specific architectures has grown, wherein types of problems of general utility are identified, and special architectures that support the development of problem solving systems for those types of problems are proposed. These architectures help in the acquisition and specification of knowledge by providing inference methods that are appropriate for the type of problem. However, knowledge based systems which use only one type of problem solving method are very brittle, and adding more types of methods requires a principled approach to integrating them in a flexible way. Contrasting with this trend is the proposal for a flexible, general architecture contained in the work on Soar. Soar has features which make it attractive for flexible use of all potentially relevant knowledge or methods. But as the theory Soar does not make commitments to specific types of problem solvers or provide guidance for their construction. It was investigated how task-specific architectures can be constructed in Soar to retain as many of the advantages as possible of both approaches. Examples were used from the Generic Task approach for building knowledge based systems. Though this approach was developed and applied for a number of problems, the ideas are applicable to other task-specific approaches as well

Theory formation by abduction : a case study based on the chemical revolution

Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" - dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction

A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent

A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation

Theory formation by abduction : initial results of a case study based on the chemical revolution

Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" — dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction

Eliciting a Sensemaking Process from Verbal Protocols of Reverse Engineers

Abstract A process of sensemaking in reverse engineering was elicited from verbal protocols of reverse engineers as they investigated the assembly code of executable programs. Four participants were observed during task performance and verbal protocols were collected and analyzed from two of the participants to determine their problem-solving states and characterize likely transitions between those states. From this analysis, a highlevel process of sensemaking is described which represents hypothesis generation and information-seeking behaviors in reverse engineering within a framework of goal-directed planning. Future work in validation and application of the process is discussed

Moons Are Planets: Scientific Usefulness Versus Cultural Teleology in the Taxonomy of Planetary Science

We argue that taxonomical concept development is vital for planetary science as in all branches of science, but its importance has been obscured by unique historical developments. The literature shows that the concept of planet developed by scientists during the Copernican Revolution was theory-laden and pragmatic for science. It included both primaries and satellites as planets due to their common intrinsic, geological characteristics. About two centuries later the non-scientific public had just adopted heliocentrism and was motivated to preserve elements of geocentrism including teleology and the assumptions of astrology. This motivated development of a folk concept of planet that contradicted the scientific view. The folk taxonomy was based on what an object orbits, making satellites out to be non-planets and ignoring most asteroids. Astronomers continued to keep primaries and moons classed together as planets and continued teaching that taxonomy until the 1920s. The astronomical community lost interest in planets ca. 1910 to 1955 and during that period complacently accepted the folk concept. Enough time has now elapsed so that modern astronomers forgot this history and rewrote it to claim that the folk taxonomy is the one that was created by the Copernican scientists. Starting ca. 1960 when spacecraft missions were developed to send back detailed new data, there was an explosion of publishing about planets including the satellites, leading to revival of the Copernican planet concept. We present evidence that taxonomical alignment with geological complexity is the most useful scientific taxonomy for planets. It is this complexity of both primary and secondary planets that is a key part of the chain of origins for life in the cosmos.Comment: 68 pages, 16 figures. For supplemental data files, see https://www.philipmetzger.com/moons_are_planets