The blind leading the blind: Mutual refinement of approximate theories

The mutual refinement theory, a method for refining world models in a reactive system, is described. The method detects failures, explains their causes, and repairs the approximate models which cause the failures. The approach focuses on using one approximate model to refine another

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Back to Keynes?

After a brief review of classical, Keynesian, New Classical and New Keynesian theories of macroeconomic policy, we assess whether New Keynesian Economics captures the quintessential features stressed by J.M. Keynes. Particular attention is paid to Keynesian features omitted in New Keynesian workhorses such as the micro-founded Keynesian multiplier and the New Keynesian Phillips curve. These theories capture wage and price sluggishness and aggregate demand externalities by departing from a competitive framework and give a key role to expectations. The main deficiencies, however, are the inability to predict a pro-cyclical real wage in the face of demand shocks, the absence of inventories, credit constraints and bankruptcies in explaining the business cycle, and no effect of the nominal as well as the real interest rate on aggregate demand. Furthermore, they fail to allow for quantity rationing and to model unemployment as a catastrophic event. The macroeconomics based on the New Keynesian Phillips curve has quite a way to go before the quintessential Keynesian features are captured.Keynesian economics, New Keynesian Phillips curve, monopolistic competition, nominal wage rigidity, welfare, pro-cyclical real wage, inventories, liquidity, bankruptcy, unemployment, monetary policy

Regulating Complacency: Human Limitations and Legal Efficacy

This Article examines how insights into limited human rationality can improve financial regulation. The Article identifies four categories of limitations—herd behavior, cognitive biases, overreliance on heuristics, and a proclivity to panic—that undermine the perfect-market regulatory assumptions that parties have full information and will act in their rational self-interest. The Article then analyzes how insights into these limitations can be used to correct resulting market failures. Requiring more robust disclosure and due diligence, for example, can help to reduce reliance on misleading information cascades that motivate herd behavior. Debiasing through law, such as requiring more specific, poignant, and concrete disclosure of risks and their consequences, can help to correct cognitive biases. Requiring firms to engage in more self-aware operational risk management and reporting can reduce the likelihood that parties will over-rely on heuristics. And legislating backstop market liquidity and other stabilizing controls can help to minimize panics. Regulation, however, can only partly overcome these limitations. Effective financial regulation should therefore be designed not only to address these limitations but also to try to mitigate the harm of inevitable financial failures

Barriers to energy efficiency: evidence from selected sectors

To combat climate change, it is essential to reduce the use of fossil fuels and minimise greenhouse gas emissions. To help to achieve that objective, energy must be used efficiently. However, many international studies claim that companies and other organisations are “leaving money on the floor” by neglecting highly cost-effective opportunities to invest in measures that would improve their energy efficiency. A new ESRI report, “Barriers to Energy Efficiency: Evidence from Selected Sectors”, examines these claims in the context of the Irish economy, and asks why organisations apparently ignore financially rewarding opportunities to improve their energy efficiency. The report is based on detailed case studies of organisations in the mechanical engineering, brewing and higher education sectors

Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network

The Farm Debt Crisis and Public Policy

macroeconomics, farm debt crisis, agricultural banking