LT revisited : explanation-based learning and the logic of Principia mathematica

This paper describes an explanation-based learning (EBL) system based on a version of Newell, Shaw and Simon's LOGIC-THEORIST (LT). Results of applying this system to propositional calculus problems from Principia Mathematica are compared with results of applying several other versions of the same performance element to these problems. The primary goal of this study is to characterize and analyze differences between not learning, rote learning (LT's original learning method), and EBL. Another aim is to provide base-line characterizations of the performance of a simple problem solver in the context of the Principa problems, in the hope that these problems can be used as a benchmark for testing improved learning methods, just as problems like chess and the eight puzzle have been used as benchmarks in research on search methods

Learning approximate diagnosis

Model-based diagnosis (MBD) provides several advantages over experiential rule-based systems. A principal shortcoming of MBD is that MBD learns nothing from any given example. An MBD system facing the same task a second time will incur the same computational effort as that incurred the first time. Our earlier work on incorporating explanation-based learning (EBL) in MBD [4] suggested a diagnostic architecture integrating EBL and MBD components. In this architecture, EBL was used to learn diagnostic rules. But the diagnoses proposed by the rules could be erroneous. So constraint suspension testing was used to check all proposed diagnoses. Insisting on perfect accuracy causes the performance of this scheme for "learning while doing" to deteriorate rapidly with the size of the device to be diagnosed. In this paper, we describe a method for trading off accuracy for efficiency. In this approach, most diagnosis problems are handled by the associational rules learned from previous problems. Model-based reasoning and learning are activated only when performance drops below a given threshold. We present empirical results on circuits of increasing number of components illustrating how this approach scales up

Explanation-based learning for diagnosis

Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required

A qualitative logic of decision

An important aspect of intelligent behavior is the ability to reason, make decisions, and act in spite of uncertainty. This paper presents a qualitative logic of decision that supports decision-making under uncertainty. To be specific, the paper presents a knowledge representation language based upon subjective Bayesian decision theory that aims to capture some aspects of common-sense reasoning associated with making decisions about actions. The language addresses the problem of describing justifications of rational choices in situations where the alternatives involve trading off potential losses and gains. The logic and an associated qualitative arithmetic are implemented in an efficient PROLOG program. Examples illustrate their use in several concrete decision-making situations

Learning multiple fault diagnosis

This paper describes two methods for integrating model-based diagnosis (MBD) and explanation-based learning. The first method (EBL) uses a generate-test-debug paradigm, generating diagnostic hypotheses using learned associational rules that summarize model-based diagnostic experiences. This strategy is a form of "learning while doing" model-based troubleshooting and could be called "online learning." The second diagnosis and learning method described here (EEL-STATIC) involves ''learning in advance." Learning begins in a training phase prior to performance or testing. Empirical results of computational experiments comparing the learning methods with MBD on two devices (the polybox and the binary full adder) are reported. For the same diagnostic performance, EBL-STATIC is several orders of magnitude faster than MBD while EBL can cause performance slow-down

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

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

Correlations Between a Cyanobacteria Bloom's Decline and Environmental Dynamics.

Lake Kainui is an ombrogenous peat lake that is eutrophic and has frequent cyanobacterial blooms. This research aimed to elucidate the factors that correlated with cyanobacterial bloom decline in Lake Kainui. Abiotic and biotic variables in Lake Kainui were monitored weekly across the bloom decline phase. Physicochemical parameters measured included nutrients, micronutrients, water temperature, dissolved oxygen (DO), pH and meteorological variables. Planktonic and particle-associated bacterial community composition (BCC) was assessed through time using molecular fingerprinting (ARISA). Total algal, cyanobacterial, planktonic bacteria, planktonic viruses and planktonic crustacean abundances were determined using microscopy. The bloom decline coincided with multiple factors including; an increasing ratio of TN:TP, changes in the ratio of mixing depth (z¬mix) to euphotic depth (zeu) and pH. The lake was stratified for several weeks before the decline and the resulting lake stability potentially created an adverse environment for cyanobacterial dominance. Changes in planktonic biota were various. Virus dynamics were positively correlated with phytoplankton dynamics. If virus abundance was negatively correlated to phytoplankton diversity, it would potentially mean that viruses were controlling the phytoplankton assemblage, however virus abundance was positively correlated to phytoplankton diversity, which suggests that viruses had a positive impact on the bloom. The positive effect was most likely due to to viruses upregulating the amount of dissolved organic matter through lysis. Bacterial abundance showed no clear correlation with other variables, but bacterial community composition (BCC) shifted as the bloom declined. Planktonic Cladocera spiked just after the bloom decline. Bosmina was the dominant species of Cladocera, but was probably too small to ingest colonial Microcystis, which suggests that the Bosmina may be consuming degradation products produced by Microcystis. These observations indicate that if planktonic biota played a role in bloom dynamics it was most likely due to shifts in the biota causing shifts in trophic cycling that alternately favour or disfavour blooming