Learning to improve iterative repair scheduling

This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone

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

Iterative repair for scheduling and rescheduling

An iterative repair search method is described called constraint based simulated annealing. Simulated annealing is a hill climbing search technique capable of escaping local minima. The utility of the constraint based framework is shown by comparing search performance with and without the constraint framework on a suite of randomly generated problems. Results are also shown of applying the technique to the NASA Space Shuttle ground processing problem. These experiments show that the search methods scales to complex, real world problems and reflects interesting anytime behavior

Values based practice and authoritarianism

Values based practice (VBP) is a radical view of the place of values in medicine which develops from a philosophical analysis of values, illness and the role of ethical principles. It denies two attractive and traditional but misguided views of medicine: that diagnosis is a merely factual matter and that the values that should guide treatment and management can be codified in principles. But, in the work of KWM (Bill) Fulford, it goes further in the form of a radical liberal view: that the idea of an antecedently good outcome should be replaced by that of a right process. That however leads to a dilemma as to whether it can account for its own normative status. Given that difficulty, why might one adopt the radical version? I sketch a possible motive drawing on Rorty’s rejection of authoritarianism which replaces objectivity with solidarity as the aim of judgement. But I argue that, nevertheless, this does not justify the rejection of the more modest particularist version of VBP

Environmental Harms, Use Conflicts, and Neutral Baselines in Environmental Law

Accounts of environmental law that rely on concepts of environmental harm and environmental protection oversimplify the tremendous variety of uses of environmental resources and the often complex relationships among those uses. Such approaches are analytically unclear and, more importantly, insert hidden normativity into putatively descriptive claims. Instead of thinking about environmental law in terms of preventing environmental harm, environmental problems can be understood more specifically and more meaningfully as disputes over conflicting uses of environmental resources. This Article proposes a use-conflict framework as a means of acquiring a deeper understanding of environmental problems and lawmaking without favoring any particular normative approach. The framework does not itself propose a resolution of any environmental problems but rather describes environmental problems and environmental lawmaking conceptually in a manner that exposes normative claims and attempts to establish some common ground across diverse normative perspectives

Rational Deployment of CSP Heuristics

Heuristics are crucial tools in decreasing search effort in varied fields of AI. In order to be effective, a heuristic must be efficient to compute, as well as provide useful information to the search algorithm. However, some well-known heuristics which do well in reducing backtracking are so heavy that the gain of deploying them in a search algorithm might be outweighed by their overhead. We propose a rational metareasoning approach to decide when to deploy heuristics, using CSP backtracking search as a case study. In particular, a value of information approach is taken to adaptive deployment of solution-count estimation heuristics for value ordering. Empirical results show that indeed the proposed mechanism successfully balances the tradeoff between decreasing backtracking and heuristic computational overhead, resulting in a significant overall search time reduction.Comment: 7 pages, 2 figures, to appear in IJCAI-2011, http://www.ijcai.org