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frame problem in artificial intelligence: Solving the Frame Problem Murray Shanahan, 1997 In 1969, John McCarthy and Pat Hayes uncovered a problem that has haunted the field of artificial intelligence ever since--the frame problem. The problem arises when logic is used to describe the effects of actions and events. Put simply, it is the problem of representing what remains unchanged as a result of an action or event. Many researchers in artificial intelligence believe that its solution is vital to the realization of the field's goals. Solving the Frame Problem presents the various approaches to the frame problem that have been proposed over the years. The author presents the material chronologically--as an unfolding story rather than as a body of theory to be learned by rote. There are lessons to be learned even from the dead ends researchers have pursued, for they deepen our understanding of the issues surrounding the frame problem. In the book's concluding chapters, the author offers his own work on event calculus, which he claims comes very close to a complete solution to the frame problem. Artificial Intelligence series |
frame problem in artificial intelligence: The Robots Dilemma Zenon W. Pylyshyn, 1987 Each of the chapters in this volume devotes considerable attention to defining and elaborating the notion of the frame problem-one of the hard problems of artificial intelligence. Not only do the chapters clarify the problems at hand, they shed light on the different approaches taken by those in artificial intelligence and by certain philosophers who have been concerned with related problems in their field. The book should therefore not be read merely as a discussion of the frame problem narrowly conceived, but also as a general analysis of what could be a major challenge to the design of computer systems exhibiting general intelligence. |
frame problem in artificial intelligence: The Robots Dilemma Revisited Kenneth M. Ford, 1996 The chapters in this book have evolved from talks originally presented at The First International Workshop on Human and Machine Cognition. Although the workshop took place in1989, the papers that appear here are more recent, completed some time after the workshop. They reflect both the spontaneous exchanges in that halcyon setting and the extensive review process. |
frame problem in artificial intelligence: The Frame Problem in Artificial Intelligence Frank M. Brown, 2014-05-12 The Frame Problem in Artificial Intelligence: Proceedings of the 1987 Workshop focuses on the approaches, principles, and concepts related to the frame problem in artificial intelligence (AI). The selection first tackles the definition of the frame problem, circumscription approaches and criticisms, modal logic approaches, and syntactic consistency approaches. The text then takes a look at two frame problems, frame problem in AI, and the frame problem in AI histories, including frame problem defined, mathematical frame problem, commonsense frame problem, and the problems of qualification and extended prediction and their relation to the frame problem. The publication examines tense-logic-based mitigation of the frame problem, unframing the frame problem, a truth maintenance based approach to the frame problem, and qualification problem. Topics include possible worlds, qualification and possible worlds, epistemological issues, truth maintenance, contradiction handling, application of intensional logic, development and implementation of chronolog, and approaches to solving the frame problem. The selection is a dependable source of data for researchers interested in the frame problem. |
frame problem in artificial intelligence: Foundational Issues in Artificial Intelligence and Cognitive Science M.H. Bickhard, L. Terveen, 1996-10-15 The book focuses on a conceptual flaw in contemporary artificial intelligence and cognitive science. Many people have discovered diverse manifestations and facets of this flaw, but the central conceptual impasse is at best only partially perceived. Its consequences, nevertheless, visit themselves as distortions and failures of multiple research projects - and make impossible the ultimate aspirations of the fields. The impasse concerns a presupposition concerning the nature of representation - that all representation has the nature of encodings: encodingism. Encodings certainly exist, but encodingism is at root logically incoherent; any programmatic research predicted on it is doomed too distortion and ultimate failure. The impasse and its consequences - and steps away from that impasse - are explored in a large number of projects and approaches. These include SOAR, CYC, PDP, situated cognition, subsumption architecture robotics, and the frame problems - a general survey of the current research in AI and Cognitive Science emerges. Interactivism, an alternative model of representation, is proposed and examined. |
frame problem in artificial intelligence: The Robots Dilemma Zenon W. Pylyshyn, 1987 Each of the chapters in this volume devotes considerable attention to defining and elaborating the notion of the frame problem-one of the hard problems of artificial intelligence. Not only do the chapters clarify the problems at hand, they shed light on the different approaches taken by those in artificial intelligence and by certain philosophers who have been concerned with related problems in their field. The book should therefore not be read merely as a discussion of the frame problem narrowly conceived, but also as a general analysis of what could be a major challenge to the design of computer systems exhibiting general intelligence. |
frame problem in artificial intelligence: Boomeritis Ken Wilber, 2003-09-09 Ken Wilber's latest book is a daring departure from his previous writings—a highly original work of fiction that combines brilliant scholarship with tongue-in-cheek storytelling to present the integral approach to human development that he expounded in more conventional terms in his recent A Theory of Everything. The story of a naïve young grad student in computer science and his quest for meaning in a fragmented world provides the setting in which Wilber contrasts the alienated flatland of scientific materialism with the integral vision, which embraces body, mind, soul, and spirit in self, culture, and nature. The book especially targets one of the most stubborn obstacles to realizing the integral vision: a disease of egocentrism and narcissism that Wilber calls boomeritis because it seems to plague the baby-boomer generation most of all. Through a series of sparkling seminar-lectures skillfully interwoven with the hero's misadventures in the realms of sex, drugs, and popular culture, all of the major tenets of extreme postmodernism are criticized—and exemplified—including the author's having a bad case of boomeritis himself. Parody, intellectual slapstick, and a mind-twisting surprise ending unite to produce a highly entertaining summary of the work of cutting-edge theorists in human development from around the world. |
frame problem in artificial intelligence: Reasoning Robots Michael Thielscher, 2005-07-05 The creation of intelligent robots is surely one of the most exciting and ch- lenginggoals of Arti?cial Intelligence. A robot is, ?rst of all, nothing but an inanimate machine with motors and sensors. In order to bring life to it, the machine needs to be programmed so as to make active use of its hardware c- ponents. This turns a machine into an autonomous robot. Since about the mid nineties of the past century, robot programming has made impressive progress. State-of-the-art robots are able to orient themselves and move around freely in indoor environments or negotiate di?cult outdoor terrains, they can use stereo vision to recognize objects, and they are capable of simple object manipulation with the help of arti?cial extremities. At a time where robots perform these tasks more and more reliably,weare ready to pursue the next big step, which is to turn autonomous machines into reasoning robots.Areasoning robot exhibits higher cognitive capabilities like following complex and long-term strategies, making rational decisions on a high level, drawing logical conclusions from sensor information acquired over time, devising suitable plans, and reacting sensibly in unexpected situations. All of these capabilities are characteristics of human-like intelligence and ultimately distinguish truly intelligent robots from mere autonomous machines. |
frame problem in artificial intelligence: Philosophy and Theory of Artificial Intelligence 2017 Vincent C. Müller, 2018-08-28 This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. |
frame problem in artificial intelligence: Philosophy and Theory of Artificial Intelligence Vincent C. Müller, 2012-08-23 Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This consensus has now come under threat and the agenda for the philosophy and theory of AI must be set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we stand and where we should go from here. |
frame problem in artificial intelligence: Minds, Machines and Evolution Christopher Hookway, 1984 Original essays written by philosophers and scientists and dealing with philosophical questions arising from work in evolutionary biology and artificial intelligence. |
frame problem in artificial intelligence: Knowledge Representation and Defeasible Reasoning Henry E. Kyburg Jr., R.P. Loui, G.N. Carlson, 2012-12-06 This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information, and data processing systems of all kinds, no matter whether human, (other) ani mal, or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and philosophical psy chology through issues in cognitive psychology and sociobiology (concerning the mental capabilities of other species) to ideas related to artificial intelli gence and computer science. While primary emphasis will be placed upon theoretical, conceptual, and epistemological aspects of these problems and domains, empirical, experimental, and methodological studies will also ap pear from time to time. The present volume provides a collection of studies that focus on some of the central problems within the domain of artificial intelligence. These difficulties fall into four principal areas: defeasible reasoning (including the frame problem as apart), ordinary language (and the representation prob lems that it generates), the revision of beliefs (and its rules of inference), and knowledge representation (and the logical problems that are encountered there). These papers make original contributions to each of these areas of inquiry and should be of special interest to those who understand the crucial role that is played by questions of logical form. They vividly illustrate the benefits that can emerge from collaborative efforts involving scholars from linguistics, philosophy, computer science, and AI. J. H. F. |
frame problem in artificial intelligence: Deterministic Artificial Intelligence Timothy Sands, 2020-05-27 Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book. |
frame problem in artificial intelligence: The Economics of Artificial Intelligence Ajay Agrawal, Joshua Gans, Avi Goldfarb, Catherine E. Tucker, 2024-03-14 A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system. |
frame problem in artificial intelligence: Fundamental Issues of Artificial Intelligence Vincent C. Müller, 2016-06-15 This volume offers a look at the fundamental issues of present and future AI, especially from cognitive science, computer science, neuroscience and philosophy. This work examines the conditions for artificial intelligence, how these relate to the conditions for intelligence in humans and other natural agents, as well as ethical and societal problems that artificial intelligence raises or will raise. The key issues this volume investigates include the relation of AI and cognitive science, ethics of AI and robotics, brain emulation and simulation, hybrid systems and cyborgs, intelligence and intelligence testing, interactive systems, multi-agent systems, and super intelligence. Based on the 2nd conference on “Theory and Philosophy of Artificial Intelligence” held in Oxford, the volume includes prominent researchers within the field from around the world. |
frame problem in artificial intelligence: Wittgenstein's Remarks on the Foundations of AI Stuart G. Shanker, 2002-01-31 Wittgenstein's Remarks on the Foundations of AI is a valuable contribution to the study of Wittgenstein's theories and his controversial attack on artifical intelligence, which successfully crosses a number of disciplines, including philosophy, psychology, logic, artificial intelligence and cognitive science, to provide a stimulating and searching analysis. |
frame problem in artificial intelligence: AI Margaret A. Boden, 2016-05-19 The applications of Artificial Intelligence lie all around us; in our homes, schools and offices, in our cinemas, in art galleries and - not least - on the Internet. The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle. As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings. Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative or even conscious, and shows how the pursuit of Artificial Intelligence has helped us to appreciate how human and animal minds are possible. |
frame problem in artificial intelligence: The Alignment Problem: Machine Learning and Human Values Brian Christian, 2020-10-06 If you’re going to read one book on artificial intelligence, this is the one. —Stephen Marche, New York Times A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful. |
frame problem in artificial intelligence: Fundamental Issues of Artificial Intelligence Vincent C. Müller, 2016-06-07 This volume offers a look at the fundamental issues of present and future AI, especially from cognitive science, computer science, neuroscience and philosophy. This work examines the conditions for artificial intelligence, how these relate to the conditions for intelligence in humans and other natural agents, as well as ethical and societal problems that artificial intelligence raises or will raise. The key issues this volume investigates include the relation of AI and cognitive science, ethics of AI and robotics, brain emulation and simulation, hybrid systems and cyborgs, intelligence and intelligence testing, interactive systems, multi-agent systems, and super intelligence. Based on the 2nd conference on “Theory and Philosophy of Artificial Intelligence” held in Oxford, the volume includes prominent researchers within the field from around the world. |
frame problem in artificial intelligence: Artificial Intelligence George F. Luger, 2011-11-21 This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one- or two-semester undergraduate course on AI. In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence–solving the complex problems that arise wherever computer technology is applied. Ideal for an undergraduate course in AI, the Sixth Edition presents the fundamental concepts of the discipline first then goes into detail with the practical information necessary to implement the algorithms and strategies discussed. Readers learn how to use a number of different software tools and techniques to address the many challenges faced by today’s computer scientists. |
frame problem in artificial intelligence: Frame Innovation Kees Dorst, 2015-03-27 How organizations can use practices developed by expert designers to solve today's open, complex, dynamic, and networked problems. When organizations apply old methods of problem-solving to new kinds of problems, they may accomplish only temporary fixes or some ineffectual tinkering around the edges. Today's problems are a new breed—open, complex, dynamic, and networked—and require a radically different response. In this book, Kees Dorst describes a new, innovation-centered approach to problem-solving in organizations: frame creation. It applies “design thinking,” but it goes beyond the borrowed tricks and techniques that usually characterize that term. Frame creation focuses not on the generation of solutions but on the ability to create new approaches to the problem situation itself. The strategies Dorst presents are drawn from the unique, sophisticated, multilayered practices of top designers, and from insights that have emerged from fifty years of design research. Dorst describes the nine steps of the frame creation process and illustrates their application to real-world problems with a series of varied case studies. He maps innovative solutions that include rethinking a store layout so retail spaces encourage purchasing rather than stealing, applying the frame of a music festival to understand late-night problems of crime and congestion in a club district, and creative ways to attract young employees to a temporary staffing agency. Dorst provides tools and methods for implementing frame creation, offering not so much a how-to manual as a do-it-yourself handbook—a guide that will help practitioners develop their own approaches to problem-solving and creating innovation. |
frame problem in artificial intelligence: Existential Cognition Ron McClamrock, 1995-03-15 While the notion of the mind as information-processor—a kind of computational system—is widely accepted, many scientists and philosophers have assumed that this account of cognition shows that the mind's operations are characterizable independent of their relationship to the external world. Existential Cognition challenges the internalist view of mind, arguing that intelligence, thought, and action cannot be understood in isolation, but only in interaction with the outside world. Arguing that the mind is essentially embedded in the external world, Ron McClamrock provides a schema that allows cognitive scientists to address such long-standing problems in artificial intelligence as the frame problem and the issue of bounded rationality. Extending this schema to cover progress in other studies of behavior, including language, vision, and action, McClamrock reinterprets the importance of the organism/environment distinction. McClamrock also considers the broader philosophical question of the place of mind in the world, particularly with regard to questions of intentionality, subjectivity, and phenomenology. With implications for philosophy, cognitive and computer science, AI, and psychology, this book synthesizes state-of-the-art work in philosophy and cognitive science on how the mind interacts with the world to produce thoughts, ideas, and actions. |
frame problem in artificial intelligence: Logic-Based Artificial Intelligence Jack Minker, 2012-11-09 The use of mathematical logic as a formalism for artificial intelligence was recognized by John McCarthy in 1959 in his paper on Programs with Common Sense. In a series of papers in the 1960's he expanded upon these ideas and continues to do so to this date. It is now 41 years since the idea of using a formal mechanism for AI arose. It is therefore appropriate to consider some of the research, applications and implementations that have resulted from this idea. In early 1995 John McCarthy suggested to me that we have a workshop on Logic-Based Artificial Intelligence (LBAI). In June 1999, the Workshop on Logic-Based Artificial Intelligence was held as a consequence of McCarthy's suggestion. The workshop came about with the support of Ephraim Glinert of the National Science Foundation (IIS-9S2013S), the American Association for Artificial Intelligence who provided support for graduate students to attend, and Joseph JaJa, Director of the University of Maryland Institute for Advanced Computer Studies who provided both manpower and financial support, and the Department of Computer Science. We are grateful for their support. This book consists of refereed papers based on presentations made at the Workshop. Not all of the Workshop participants were able to contribute papers for the book. The common theme of papers at the workshop and in this book is the use of logic as a formalism to solve problems in AI. |
frame problem in artificial intelligence: The Turing Test and the Frame Problem Larry Crockett, 1994 |
frame problem in artificial intelligence: The frame problem and related problems on artificial intelligence P. J. Hayes, 1971 The frame problem arises in considering the logical structure of a robot's beliefs. It has been known for some years, but only recently has much progress been made. The problem is described and discussed. Various suggested methods for its solution are outlined, and described in a uniform notation. Finally, brief consideration is given to the problem of adjusting a belief system in the face of evidence which contradists beliefs. It is shown that a variation on the situation notation of (McCarthy and Hayes, 1969) permits an elegant approach, and relates this problem to the frame problem. (Author). |
frame problem in artificial intelligence: Artificial and Mathematical Theory of Computation Vladimir Lifschitz, 2012-12-02 Artificial and Mathematical Theory of Computation is a collection of papers that discusses the technical, historical, and philosophical problems related to artificial intelligence and the mathematical theory of computation. Papers cover the logical approach to artificial intelligence; knowledge representation and common sense reasoning; automated deduction; logic programming; nonmonotonic reasoning and circumscription. One paper suggests that the design of parallel programming languages will invariably become more sophisticated as human skill in programming and software developments improves to attain faster running programs. An example of metaprogramming to systems concerns the design and control of operations of factory devices, such as robots and numerically controlled machine tools. Metaprogramming involves two design aspects: that of the activity of a single device and that of the interaction with other devices. One paper cites the application of artificial intelligence pertaining to the project proof checker for first-order logic at the Stanford Artificial Intelligence Laboratory. Another paper explains why the bisection algorithm widely used in computer science does not work. This book can prove valuable to engineers and researchers of electrical, computer, and mechanical engineering, as well as, for computer programmers and designers of industrial processes. |
frame problem in artificial intelligence: Artificial Intelligence Today Michael J. Wooldridge, Manuela Veloso, 2007-03-06 Artificial Intelligence is one of the most fascinating and unusual areas of academic study to have emerged this century. For some, AI is a true scientific discipline, that has made important and fundamental contributions to the use of computation for our understanding of nature and phenomena of the human mind; for others, AI is the black art of computer science. Artificial Intelligence Today provides a showcase for the field of AI as it stands today. The editors invited contributions both from traditional subfields of AI, such as theorem proving, as well as from subfields that have emerged more recently, such as agents, AI and the Internet, or synthetic actors. The papers themselves are a mixture of more specialized research papers and authorative survey papers. The secondary purpose of this book is to celebrate Springer-Verlag's Lecture Notes in Artificial Intelligence series. |
frame problem in artificial intelligence: Understanding Machine Learning Shai Shalev-Shwartz, Shai Ben-David, 2014-05-19 Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage. |
frame problem in artificial intelligence: Foundational Issues in Artificial Intelligence and Cognitive Science Mark H. Bickhard, L. Terveen, 1995-03-07 The book focuses on a conceptual flaw in contemporary artificial intelligence and cognitive science. Many people have discovered diverse manifestations and facets of this flaw, but the central conceptual impasse is at best only partially perceived. Its consequences, nevertheless, visit themselves as distortions and failures of multiple research projects - and make impossible the ultimate aspirations of the fields. The impasse concerns a presupposition concerning the nature of representation - that all representation has the nature of encodings: encodingism. Encodings certainly exist, but encodingism is at root logically incoherent; any programmatic research predicted on it is doomed too distortion and ultimate failure. The impasse and its consequences - and steps away from that impasse - are explored in a large number of projects and approaches. These include SOAR, CYC, PDP, situated cognition, subsumption architecture robotics, and the frame problems - a general survey of the current research in AI and Cognitive Science emerges. Interactivism, an alternative model of representation, is proposed and examined. |
frame problem in artificial intelligence: Artificial Intelligence John Haugeland, 1989-01-06 Machines who think—how utterly preposterous, huff beleaguered humanists, defending their dwindling turf. Artificial Intelligence—it's here and about to surpass our own, crow techno-visionaries, proclaiming dominion. It's so simple and obvious, each side maintains, only a fanatic could disagree. Deciding where the truth lies between these two extremes is the main purpose of John Haugeland's marvelously lucid and witty book on what artificial intelligence is all about. Although presented entirely in non-technical terms, it neither oversimplifies the science nor evades the fundamental philosophical issues. Far from ducking the really hard questions, it takes them on, one by one. Artificial intelligence, Haugeland notes, is based on a very good idea, which might well be right, and just as well might not. That idea, the idea that human thinking and machine computing are radically the same, provides the central theme for his illuminating and provocative book about this exciting new field. After a brief but revealing digression in intellectual history, Haugeland systematically tackles such basic questions as: What is a computer really? How can a physical object mean anything? What are the options for computational organization? and What structures have been proposed and tried as actual scientific models for intelligence? In a concluding chapter he takes up several outstanding problems and puzzles—including intelligence in action, imagery, feelings and personality—and their enigmatic prospects for solution. |
frame problem in artificial intelligence: The Technological Singularity Murray Shanahan, 2015-08-07 The idea of technological singularity, and what it would mean if ordinary human intelligence were enhanced or overtaken by artificial intelligence. The idea that human history is approaching a “singularity”—that ordinary humans will someday be overtaken by artificially intelligent machines or cognitively enhanced biological intelligence, or both—has moved from the realm of science fiction to serious debate. Some singularity theorists predict that if the field of artificial intelligence (AI) continues to develop at its current dizzying rate, the singularity could come about in the middle of the present century. Murray Shanahan offers an introduction to the idea of the singularity and considers the ramifications of such a potentially seismic event. Shanahan's aim is not to make predictions but rather to investigate a range of scenarios. Whether we believe that singularity is near or far, likely or impossible, apocalypse or utopia, the very idea raises crucial philosophical and pragmatic questions, forcing us to think seriously about what we want as a species. Shanahan describes technological advances in AI, both biologically inspired and engineered from scratch. Once human-level AI—theoretically possible, but difficult to accomplish—has been achieved, he explains, the transition to superintelligent AI could be very rapid. Shanahan considers what the existence of superintelligent machines could mean for such matters as personhood, responsibility, rights, and identity. Some superhuman AI agents might be created to benefit humankind; some might go rogue. (Is Siri the template, or HAL?) The singularity presents both an existential threat to humanity and an existential opportunity for humanity to transcend its limitations. Shanahan makes it clear that we need to imagine both possibilities if we want to bring about the better outcome. |
frame problem in artificial intelligence: Framers Kenneth Cukier, Viktor Mayer-Schönberger, Francis de Véricourt, 2021-05-11 “Cukier and his co-authors have a more ambitious project than Kahneman and Harari. They don’t want to just point out how powerfully we are influenced by our perspectives and prejudices—our frames. They want to show us that these frames are tools, and that we can optimise their use.” —Forbes From pandemics to populism, AI to ISIS, wealth inequity to climate change, humanity faces unprecedented challenges that threaten our very existence. The essential tool that will enable humanity to find the best way foward is defined in Framers by internationally renowned authors Kenneth Cukier, Viktor Mayer-Schönberger, and Francis de Véricourt. To frame is to make a mental model that enables us to make sense of new situations. Frames guide the decisions we make and the results we attain. People have long focused on traits like memory and reasoning, leaving framing all but ignored. But with computers becoming better at some of those cognitive tasks, framing stands out as a critical function—and only humans can do it. This book is the first guide to mastering this human ability. Illustrating their case with compelling examples and the latest research, authors Cukier, Mayer-Schönberger, and de Véricourt examine: · Why advice to “think outside the box” is useless · How Spotify beat Apple by reframing music as an experience · How the #MeToo twitter hashtag reframed the perception of sexual assault · The disaster of framing Covid-19 as equivalent to seasonal flu, and how framing it akin to SARS delivered New Zealand from the pandemic Framers shows how framing is not just a way to improve how we make decisions in the era of algorithms—but why it will be a matter of survival for humanity in a time of societal upheaval and machine prosperity. |
frame problem in artificial intelligence: Handbook of Knowledge Representation Bruce Porter, 2008-01 Knowledge representation, which lies at the core of artificial intelligence, is concerned with encoding knowledge on computers to enable systems to reason automatically. The aims are to help readers make their computer smarter, handle qualitative and uncertain information, and improve computational tractability. |
frame problem in artificial intelligence: How the Body Shapes the Way We Think Rolf Pfeifer, Josh Bongard, 2006-10-27 An exploration of embodied intelligence and its implications points toward a theory of intelligence in general; with case studies of intelligent systems in ubiquitous computing, business and management, human memory, and robotics. How could the body influence our thinking when it seems obvious that the brain controls the body? In How the Body Shapes the Way We Think, Rolf Pfeifer and Josh Bongard demonstrate that thought is not independent of the body but is tightly constrained, and at the same time enabled, by it. They argue that the kinds of thoughts we are capable of have their foundation in our embodiment—in our morphology and the material properties of our bodies. This crucial notion of embodiment underlies fundamental changes in the field of artificial intelligence over the past two decades, and Pfeifer and Bongard use the basic methodology of artificial intelligence—understanding by building—to describe their insights. If we understand how to design and build intelligent systems, they reason, we will better understand intelligence in general. In accessible, nontechnical language, and using many examples, they introduce the basic concepts by building on recent developments in robotics, biology, neuroscience, and psychology to outline a possible theory of intelligence. They illustrate applications of such a theory in ubiquitous computing, business and management, and the psychology of human memory. Embodied intelligence, as described by Pfeifer and Bongard, has important implications for our understanding of both natural and artificial intelligence. |
frame problem in artificial intelligence: An Intelligence in Our Image Osonde A. Osoba, William Welser IV, 2017-04-05 Machine learning algorithms and artificial intelligence influence many aspects of life today and have gained an aura of objectivity and infallibility. The use of these tools introduces a new level of risk and complexity in policy. This report illustrates some of the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems. |
frame problem in artificial intelligence: Artificial Intelligence in Society OECD, 2019-06-11 The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises. |
frame problem in artificial intelligence: Environment Modeling-Based Requirements Engineering for Software Intensive Systems Zhi Jin, 2017-12-05 Environment Modeling-Based Requirements Engineering for Software Intensive Systems provides a new and promising approach for engineering the requirements of software-intensive systems, presenting a systematic, promising approach to identifying, clarifying, modeling, deriving, and validating the requirements of software-intensive systems from well-modeled environment simulations. In addition, the book presents a new view of software capability, i.e. the effect-based software capability in terms of environment modeling. - Provides novel and systematic methodologies for engineering the requirements of software-intensive systems - Describes ontologies and easily-understandable notations for modeling software-intensive systems - Analyzes the functional and non-functional requirements based on the properties of the software surroundings - Provides an essential, practical guide and formalization tools for the task of identifying the requirements of software-intensive systems - Gives system analysts and requirements engineers insight into how to recognize and structure the problems of developing software-intensive systems |
frame problem in artificial intelligence: Artificial Intelligence Stuart Jonathan Russell, Peter Norvig, 2013-07-31 In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics. The book also includes hundreds of new exercises. |
frame problem in artificial intelligence: Making AI Intelligible Herman Cappelen, 2021 Can humans and artificial intelligences share concepts and communicate? This book shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can understand each other. In doing so, they illustrate ways in which that philosophical tradition can be improved. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. |
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