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Books on the topic 'Expert heuristics'

Author: Grafiati

Published: 29 June 2021

Last updated: 4 February 2022

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1

Clancey, William J. Heuristic classification. [Alexandria, Va.]: DTIC, 1985.

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2

Lewis, John N. Expert systems development utilizing heuristic methods. Monterey, Calif: Naval Postgraduate School, 1996.

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3

Luca, Console, ed. Diagnostic problem solving: Combining heuristic, approximate and causal reasoning. (London): North Oxford Academic, 1989.

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4

Torasso, Pietro. Diagnostic problem solving: Combining heuristic, approximate and casual reasoning. [London]: North Oxford Academic, 1989.

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5

Luca, Console, ed. Diagnostic problem solving: Combining heuristic, approximate, and causal reasoning. New York: Van Nostrand Reinhold, 1989.

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6

Lowe, S. Knowledge acquisition for expert systems: A preliminary investigation into the heuristics of online searching. 1987.

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7

Boden, Margaret A. 2. General intelligence as the Holy Grail. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0002.

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A host of state-of-the-art AI applications exist, designed for countless specific tasks and used in almost every area of life, by laymen and professionals alike. Many outperform even the most expert humans. In that sense, progress has been spectacular. But the AI pioneers were also hoping for systems with general intelligence. ‘General intelligence as the Holy Grail’ explains why artificial general intelligence is still highly elusive despite recent increases in computer power. It considers the general AI strategies in recent research—heuristics, planning, mathematical simplification, and different forms of knowledge representation—and discusses the concepts of the frame problem, agents and distributed cognition, machine learning, and generalist systems.

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8

Expert Systems Development Utilizing Heuristic Methods. Storming Media, 1996.

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9

Peters, Ellen. Overcoming Innumeracy and the Use of Heuristics When Communicating Science. Edited by Kathleen Hall Jamieson, Dan M. Kahan, and Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.42.

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Science communication is difficult because, rather than understanding and using important, often numeric, information, lay people and experts alike resort to superficial heuristic processing of information. This chapter examines heuristic processing with respect to the power of experience, the affect heuristic, and framing effects along with their interactions with innumeracy. Recommendations are made for how to improve science communication to decrease use of heuristic processing and improve use of numeric information in risk perceptions and decision making. Based on existing evidence, science communicators should carefully identify communication goals and then choose evidence-based strategies to meet them. Evidence-based strategies include providing numeric information (as opposed to not providing it), reducing cognitive effort, increasing affective meaning, and drawing attention to key information.

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10

Yeh, Chune-Sin. An expert system approach to the optimal design of single-junction and multijunction tandem solar cells. 1988.

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11

Siegrist, Michael, and Christina Hartmann. Overcoming the Challenges of Communicating Uncertainties Across National Contexts. Edited by Kathleen Hall Jamieson, Dan M. Kahan, and Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.47.

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The goal of risk communication is to provide information about risks and uncertainties in a way that enables people to make the best decisions, based on their own values. Various factors influence the success of risk communication. This chapter first highlights risk communication methods and formats that determine successful risk communication. For example, laypeople do not understand all presentation formats of numbers equally well, and risk comparisons help them improve their evaluation of risk information. It also introduces the influence of heuristics, trust, and cultural values for decisions under uncertainty conditions. In the case of controversial topics, heuristics and trust influence how people interpret uncertainties. Research suggests that most people depend on experts with whom they share salient values in a given context. Based on the available evidence, the chapter provides some recommendations for communicating uncertainties at the end of the chapter and further describe some avenues for research.

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12

Intelligent control systems research. [Washington, DC: National Aeronautics and Space Administration, 1994.

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13

Jack, Jordynn. Interpreting Gender. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252038372.003.0002.

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This chapter explores how the gendered character of the refrigerator mother offered an interpretive lens through which experts viewed autism when it was first identified as a unique disorder. Character sketches of these early “autism mothers” emerged from a set of topoi—culturally available ideas and images—about mothers in the 1960s and were found lacking compared to the standard of a warm, devoted, and loving mother. The chapter shows that typified gendered characters can be interpreted as explanations for autism, thereby functioning as heuristics for scientific theorizing. It then considers how mothers began to counter this character and construct a new one that would grant them greater epistemic authority.

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14

Jennings, Len, and Thomas Skovholt. Expertise in Counseling and Psychotherapy. Oxford University Press, 2016. http://dx.doi.org/10.1093/med:psych/9780190222505.001.0001.

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Expertise in Counseling and Psychotherapy features seven master therapist studies from around the world and provides an extensive synthesis of these studies to produce the first international perspective of expert counselors and psychotherapists. The study of expertise has a rich history, whereas research on psychotherapy expertise has mostly surfaced in the past two decades. Jennings and Skovholt first applied qualitative methodology to the study of expert therapists in 1996. Qualitative research has proven to be an extremely effective method for capturing the complexity of the master therapist construct. One limitation of this line of research is that most studies have been conducted in the United States. Fortunately, there are a small but growing number of international qualitative studies on psychotherapy expertise. Moreover, these studies utilized essentially the same research questions and methodologies as our first study on expert therapists, making the consolidation of the findings seamless and trustworthy. The studies include three therapist expertise research projects in Southeast Asia, including Singapore, Japan, and Korea. In North America, there are studies from the United States and Canada. In Europe, there are studies from Portugal and the Czech Republic. The qualitative meta-analysis of all seven data sets is the highlight of our book on master therapists from around the world. The findings and recommendations from this book will enhance the training of future psychotherapists and counselors. Understanding the universal characteristics of expert therapists practicing around the world offers training programs and mental health practitioners a heuristic for optimal therapist and counselor development.

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15

Schulz, Armin W. Efficient Cognition. The MIT Press, 2018. http://dx.doi.org/10.7551/mitpress/9780262037600.001.0001.

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It is now widely accepted that many organisms (including humans) don’t just react to the world using behavioral reflexes, but also, at times, decide what to do by relying on mental representations. More specifically, the behavior of many organisms is not simply triggered by a perception of the state of their environment, but inferred using higher-level mental states downstream from their perceptual states. What is far less clear is why this is the case: what benefits does representational decision making bring to an organism, and what implications do these benefits have for the exact role that mental representations play in an organism’s decision making machinery? In my book, I provide answers to these questions. Specifically, I defend a cognitive-efficiency-based account of the evolution of mental representations, according to which a key driver of the evolution of representational decision making is the fact that mental representations can enable an organism to save a number of cognitive resources and to adjust more easily to changed environments. I then apply this account to a number of open questions in different sciences, including: when should we expect cognition to essentially involve parts of the environment? When should we expect decision making to rely on simple, satisficing heuristics? When should we expect organisms to be altruistically motivated to help others? Along the way, I also respond to concerns about the plausibility of evolutionary psychological projects more generally.

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