It first reviews the core issues in experiencebased design, for example, (1) the content of a design experience (or case), (2) the internal organization of design cases, (3) the language for indexing the cases, (4) the mechanism for retrieving a case relevant to a given design task, (5) the mechanism for adapting a retrieved design to satisfy the constraints of the design task, (6) the mechanism for evaluating a design against the specification of the design task, (7) the mechanism for redesigning a failed design, (8) the mechanism for acquiring new design knowledge, (9) the mechanism for chunking information about a design into a new case, and (10) the mechanism for storing a new case in memory for potential reuse in the future. It then proposes that decisions about these issues might lie in the designer's comprehension of the designs of artifacts he/she has encountered in the past, that is, in his/her mental models of how the designs achieve the functions and satisfy the constraints of the artifacts. To elaborate and evaluate this proposal, the dissertation analyzes the design of physical devices such as simple electric circuits, heat exchangers, and angular momentum controllers. It develops a theory of designers' comprehension of device designs in terms of functional models of how devices work. The functional model of a device provides a causal explanation of how the structure of the device produces its functions.

Through the technology of expert systems, the expertise of highly skilled personnel can be automated and used to assist lesser skilled personnel in the diagnosis and repair of complex machines. Expert systems that incorporate causal reasoning represent a second-generation approach to the provision of diagnostic assistance. The technology involved performs postdiction by reasoning from first principles. This article is based on research in qualitative physics and the philosophy of causality. A new implementation vehicle for causal reasoning is described, one that embodies hypothetical or counterfactual reasoning (Roach, Eichelman, and Whitehead 1985) in a language called Wif (What IF).

The traditional reliability design methods are imperfect because the designed systems aim at fewer faults, but once a fault happens, the systems might hard fail. To solve this problem, we present a self-maintenance machine (SMM), one that can maintain its functions flexibly even though faults occur. To achieve the capabilities of diagnosing and repair planning, a model-based approach that uses qualitative physics was proposed. Regarding the repair-executing capability, a control-type repair strategy was followed. A prototype of the SMM was developed, and it succeeded in maintaining its functions if the structure did not change.

An important problem faced by auditors is gauging how much reliance can be placed on the accounting systems that process millions of transactions to produce the numbers summarized in a company's financial statements. Accounting systems contain internal controls, procedures designed to detect and correct errors and irregularities that can occur in the processing of transactions. In a complex accounting system, it can be an extremely difficult task for the auditor to anticipate the possible errors that can occur and evaluate the effectiveness of the controls at detecting them. An accurate analysis must take into account the unique features of each company's business processes. An important problem faced by auditors is gauging how much reliance can be placed on the accounting systems that produce the numbers summarized in the financial statements. Accounting systems contain internal controls, procedures designed to detect and correct errors and irregularities that can occur in the processing of transactions. In a complex accounting system, it can be an extremely difficult task for the auditor to anticipate the possible errors that can occur, determine their downstream effects in the accounting system, and evaluate the effectiveness of the controls at detecting them. An accurate analysis must take into account the unique features of each company's business processes. In the United States, the Securities and Exchange Commission requires a yearly independent audit of the financial statements of public companies.

A common difficulty in diagnosing failures within Pratt & Whitney's F100-PW-100/200 gas turbine engine occurs when a fault in one part of a system--comprising an engine, an airframe, a test cell, and automated ground engine test set (AGETS) equipment--is manifested as an out-ofbound parameter elsewhere in the system. In such cases, the normal procedure is to run AGETS self-diagnostics on the abnormal parameter. However, because the self-diagnostics only test the specified local parameter, it will pass, leaving only the operators' experience and traditional fault-isolation manuals to locate the source of the problem in another part of the system. In such cases, the normal procedure is to run AGETS self-diagnostics on the abnormal parameter. However, because the self-diagnostics only test the parameter specified, it will pass because parameter tests are local tests that cannot uncover malfunctions in other parts of the system.

Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about our model of the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.

Many applications infer the structure of a probabilistic graphical model from data to elucidate the relationships between variables. But how can we train graphical models on a massive data set? In this paper, we show how to construct coresets -compressed data sets which can be used as proxy for the original data and have provably bounded worst case error- for Gaussian dependency networks (DNs), i.e., cyclic directed graphical models over Gaussians, where the parents of each variable are its Markov blanket. Specifically, we prove that Gaussian DNs admit coresets of size independent of the size of the data set. Unfortunately, this does not extend to DNs over members of the exponential family in general. As we will prove, Poisson DNs do not admit small coresets. Despite this worst-case result, we will provide an argument why our coreset construction for DNs can still work well in practice on count data. To corroborate our theoretical results, we empirically evaluated the resulting Core DNs on real data sets. The results

The Steam page for "Snake Pass" is already live, so fans can now get to know Sumo Digital's upcoming physics-based adventure game ahead of its release next week. If you are one of the players who are looking for a familiar yet challenging tactile experience, "Snake Pass" could be the one for you. This indie game boasts of a charming adventure that involves coiling, slithering and climbing one's way up to the top. The puzzles this game has are beyond ordinary. Though some may look simple, players are still advised to brace themselves because everything in this game is just mind-blowing.

Math word problems provide a natural abstraction to a range of natural language understanding problems that involve reasoning about quantities, such as interpreting election results, news about casualties, and the financial section of a newspaper. Units associated with the quantities often provide information that is essential to support this reasoning. This paper proposes a principled way to capture and reason about units and shows how it can benefit an arithmetic word problem solver. This paper presents the concept of Unit Dependency Graphs (UDGs), which provides a compact representation of the dependencies between units of numbers mentioned in a given problem. Inducing the UDG alleviates the brittleness of the unit extraction system and allows for a natural way to leverage domain knowledge about unit compatibility, for word problem solving. We introduce a decomposed model for inducing UDGs with minimal additional annotations, and use it to augment the expressions used in the arithmetic word problem solver of (Roy and Roth 2015) via a constrained inference framework. We show that introduction of UDGs reduces the error of the solver by over 10 %, surpassing all existing systems for solving arithmetic word problems. In addition, it also makes the system more robust to adaptation to new vocabulary and equation forms .

Math word problems provide a natural abstraction to a range of natural language understanding problems that involve reasoning about quantities, such as interpreting election results, news about casualties, and the financial section of a newspaper. Units associated with the quantities often provide information that is essential to support this reasoning. This paper proposes a principled way to capture and reason about units and shows how it can benefit an arithmetic word problem solver. This paper presents the concept of Unit Dependency Graphs (UDGs), which provides a compact representation of the dependencies between units of numbers mentioned in a given problem. Inducing the UDG alleviates the brittleness of the unit extraction system and allows for a natural way to leverage domain knowledge about unit compatibility, for word problem solving. We introduce a decomposed model for inducing UDGs with minimal additional annotations, and use it to augment the expressions used in the arithmetic word problem solver of (Roy and Roth 2015) via a constrained inference framework. We show that introduction of UDGs reduces the error of the solver by over 10 %, surpassing all existing systems for solving arithmetic word problems. In addition, it also makes the system more robust to adaptation to new vocabulary and equation forms .