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

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

The computer revolution in science: steps towards the realization of computer-supported discovery environments

The tools that scientists use in their search processes together form so-called discovery environments. The promise of artificial intelligence and other branches of computer science is to radically transform conventional discovery environments by equipping scientists with a range of powerful computer tools including large-scale, shared knowledge bases and discovery programs. We will describe the future computer-supported discovery environments that may result, and illustrate by means of a realistic scenario how scientists come to new discoveries in these environments. In order to make the step from the current generation of discovery tools to computer-supported discovery environments like the one presented in the scenario, developers should realize that such environments are large-scale sociotechnical systems. They should not just focus on isolated computer programs, but also pay attention to the question how these programs will be used and maintained by scientists in research practices. In order to help developers of discovery programs in achieving the integration of their tools in discovery environments, we will formulate a set of guidelines that developers could follow

Chemical discovery as belief revision

In this paper we describe STAHLp, a system that constructs componential models of chemical substances. STAHLp is a descendent of Zytkow and Simon's (1986) STAHL system, and both use chemical reactions and known componential models in order to construct new chemical models. However, STAHLp employs a more unified and effective strategy for recovering from erroneous inferences, based partly on de Kleer's (1984) assumption-based method of belief revision. This involves recording the underlying source beliefs or premises which lead to each inferred reaction or model. Where Zytkow and Simon's system required multiple methods for detecting errors and recovering from them, STAHLp uses a more powerful representation and additional rules which allow a unified method for error detection and recovery. When given the same initial data, the new system constructs the same historically correct models as STAHL, but it has other capabilities as well. In particular, STAHLp can modify data it has been given if this is necessary to achieve consistent models, and then proceed to construct new models based on the revised data

Inference in classifier systems

Classifier systems (Css) provide a rich framework for learning and induction, and they have beenı successfully applied in the artificial intelligence literature for some time. In this paper, both theı architecture and the inferential mechanisms in general CSs are reviewed, and a number of limitations and extensions of the basic approach are summarized. A system based on the CS approach that is capable of quantitative data analysis is outlined and some of its peculiarities discussed

What Can Artificial Intelligence Do for Scientific Realism?

The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling

Experimentation in machine discovery

KEKADA, a system that is capable of carrying out a complex series of experiments on problems from the history of science, is described. The system incorporates a set of experimentation strategies that were extracted from the traces of the scientists' behavior. It focuses on surprises to constrain its search, and uses its strategies to generate hypotheses and to carry out experiments. Some strategies are domain independent, whereas others incorporate knowledge of a specific domain. The domain independent strategies include magnification, determining scope, divide and conquer, factor analysis, and relating different anomalous phenomena. KEKADA represents an experiment as a set of independent and dependent entities, with apparatus variables and a goal. It represents a theory either as a sequence of processes or as abstract hypotheses. KEKADA's response is described to a particular problem in biochemistry. On this and other problems, the system is capable of carrying out a complex series of experiments to refine domain theories. Analysis of the system and its behavior on a number of different problems has established its generality, but it has also revealed the reasons why the system would not be a good experimental scientist