Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

/pdf/case-based-reasoning-foundational-issues-methodological-mrthek14md.pdf

Case-based reasoning: foundational issues, methodological variations, and system approaches

http://www-anw.cs.umass.edu/~barto/courses/cs687/Sutton-Precup-Singh-AIJ99.pdf

Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning

Explanation in artificial intelligence: Insights from the social sciences

From the Publisher:
This book presents a selection of recent progress, issues, and directions for the future of case-based reasoning. It includes chapters addressing fundamental issues and approaches in indexing and retrieval, situation assessment and similarity assessment, and in case adaptation. Those chapters provide a "case-based" view of key problems and solutions in context of the tasks for which they were developed. It also presents lessons learned about how to design CBR systems and how to apply them to real-world problems. The final chapters include a perspective on the state of the field and the most important directions for future impact. The case studies presented involve a broad sampling of tasks, such as design, education, legal reasoning, planning, decision support, problem-solving, and knowledge navigation. In addition, they experimentally examine one of the fundamental tenets of CBR, that reasoning from prior experiences improves performance. The chapters also address other issues that, while not restricted to CBR per se, have been vigorously attacked by the CBR community, including creative problem-solving, strategic memory search, and opportunistic retrieval. This volume provides a vision of the present, and a challenge for the future, of case-based reasoning research and applications.

Case-Based Reasoning: Experiences, Lessons and Future Directions

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

/pdf/retrieval-reuse-revision-and-retention-in-case-based-51qpcadmkh.pdf

Retrieval, reuse, revision and retention in case-based reasoning

Experience with the growing number of large-scale CBR systems has led to increasing recognition of the importance of case-base maintenance. Multiple researchers have addressed pieces of the case-base maintenance problem, considering such issues as maintaining consistency and controlling case-base growth. However, despite the existence of these cases of case-base maintenance, there is no general framework of dimensions for describing case-base maintenance systems. Such a framework would be useful both to understand the state of the art in case-base maintenance and to suggest new avenues of exploration by identifying points along the dimensions that have not yet been studied. This paper presents a first attempt at identifying the dimensions of case-base maintenance. It shows that characterizations along such dimensions can suggest avenues for future case-base maintenance research and presents initial steps exploring one of those avenues: identifying patterns of problems that require generalized revisions and addressing them with lazy updating.

Categorizing Case-Base Maintenance: Dimensions and Directions

Explanation-based learning (EBL) is a very powerful method for category formation. Since EBL algorithms depend on having good explanations, it is crucial to have effective ways to build explanations, especially in complex real-world situations where complete causal information is not available. When people encounter new situations, they often explain them by remembering old explanations, and adapting them to fit. We believe that this case-based approach to explanation holds promise for use in AI systems, both for routine explanation and to creatively explain situations quite unlike what the system has encountered before. Building new explanations from old ones relies on having explanations available in memory. We describe explanation patterns (XPs), knowledge structures that package the reasoning underlying explanations. Using the SWALE system as a base, we discuss the retrieval and modification process, and the criteria used when deciding which explanation to accept. We also discuss issues in learning XPs: what generalization strategies are appropriate for real-world explanations, and which indexing strategies are appropriate for XPs. SWALE's explanations allow it to understand nonstandard stories, and the XPs it learns increase its efficiency in dealing with similar anomalies in the future.

Creativity and learning in a case-based explainer

Learning, goals, and learning goals, Ashwin Ram and David B. Leake. Part 1 Current state of the field: planning to learn, Lawrence Hunter quantitative results concerning the utility of explanation-based learning, Steven Minton the use of explicit goals for knowledge to guide inference and learning, Ashwin Ram and Lawrence Hunter deriving categories to achieve goals, Lawrence W. Barsalou harpoons and long sticks - the interaction of theory and similarity in rule induction, Edward J. Wisniewski and Douglas L. Medlin introspective reasoning using meta-explanations for multistrategy learning, Ashwin Ram and Michael T. Cox goal-directed learning - a decision-theoretic model for deciding what to learn next, Marie desJardins goal-based explanation evaluation, David B. Leake planning to perceive, Louise Pryor and Gregg Collins planning and learning in PRODIGY - overview of an integrated architecture, Jaime Carbonell et al a learning model for the selection of problem-solving strategies in continuous physical systems, Xiaodong Xia and Dit-Yan Yeung explicitly biased generalization, Diana Gordon and Donald Perlis three levels of goal orientation in learning, Evelyn Ng and Carl Bereiter characterizing the application of computer simulations in education - instructional criteria, Jos J.A. van Berkum et al. Part 2 Current research and recent directions: goal-driven learning - fundamental issues and symposium report, David B. Leake and Ashwin Ram storage side effects - studying processing to understand learning, Lawrence W. Barsalou goal-driven learning in multistrategy reasoning and learning systems, Ashwin Ram et al inference to the best plan - a coherence theory of decision, Paul Thagard and Elijah Millgram toward goal-driven integration of explanation and action, David B. Leake learning as goal-driven inference, Ryszard Michalski and Ashwin Ram.

David B. Leake

Papers

Case-Based Reasoning: Experiences, Lessons and Future Directions

Retrieval, reuse, revision and retention in case-based reasoning

Categorizing Case-Base Maintenance: Dimensions and Directions

Creativity and learning in a case-based explainer

Goal-driven learning