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Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume. In this book, Joseph Halpern explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression. Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification.

This book examines new forms of nascent life that emerge through technical interactions within human-constructed environments—“machinic life”—in the sciences of cybernetics, artificial life, and artificial intelligence. With the development of such research initiatives as the evolution of digital organisms, computer immune systems, artificial protocells, evolutionary robotics, and swarm systems, it argues, machinic life has achieved a complexity and autonomy worthy of study in its own right. Drawing on the publications of scientists as well as a range of work in contemporary philosophy and cultural theory, but always with the primary focus on the “objects at hand”—the machines, programs, and processes that constitute machinic life—the book shows how they come about, how they operate, and how they are already changing. This understanding is a necessary first step, it further argues, that must precede speculation about the meaning and cultural implications of these new forms of life. Developing the concept of the “computational assemblage” (a machine and its associated discourse) as a framework to identify both resemblances and differences in form and function, the book offers a conceptual history of each of the three sciences. It considers the new theory of machines proposed by cybernetics from several perspectives, including Lacanian psychoanalysis and “machinic philosophy.” The book examines the history of the new science of artificial life and its relation to theories of evolution, emergence, and complex adaptive systems (as illustrated by a series of experiments carried out on various software platforms).

This book addresses the connections between computers, life, evolution, brains, and minds. Digital computers are recent and have changed our society. However, they represent just the latest way to process information, using algorithms to create order out of chaos. Before computers, the job of processing information was done by living organisms, which are nothing more than complex information processing devices, shaped by billions of years of evolution. The most advanced of these information processing devices is the human brain. Brains enable humans to process information in a way unparalleled by any other species, living or extinct, or by any existing machine. They provide humans with intelligence, consciousness and, some believe, even with a soul. Brains also enabled humans to develop science and technology to a point where it is possible to design computers with a power comparable to that of the human brain. Machine learning and artificial intelligence technologies will one day make it possible to create intelligent machines and computational biology will one day enable us to model, simulate, and understand biological systems and even complete brains, with unprecedented levels of detail. From these efforts, new minds will eventually emerge, minds that will emanate from the execution of programs running in powerful computers. These digital minds may one day rival our own, become our partners, and replace humans in many tasks. They may usher in a technological singularity, may make humans obsolete or even a threatened species. They make us super-humans or demi-gods.

Logic-based formalizations of argumentation, which assume a set of formulae and then lay out arguments and counterarguments that can be obtained from these formulae, have been refined in recent years in an attempt to capture more closely real-world practical argumentation. This book introduces techniques for formalizing deductive argumentation in artificial intelligence, emphasizing emerging formalizations for practical argumentation. It discusses how arguments can be constructed, how key intrinsic and extrinsic factors can be identified, and how these analyses can be harnessed for formalizing argumentation for use in real-world problem analysis and decision making. The book focuses on a monological approach to argumentation, in which there is a set of possibly conflicting pieces of information (each represented by a formula) that has been collated by an agent or a pool of agents. The role of argumentation is to construct a collection of arguments and counterarguments pertaining to some particular claim of interest to be used for analysis or presentation. The book elucidates and formalizes key elements of deductive argumentation.

The idea of intelligent machines has become part of popular culture. But tracing the history of the actual science of machine intelligence reveals a rich network of cross-disciplinary contributions—the unrecognized origins of ideas now central to artificial intelligence, artificial life, cognitive science, and neuroscience. In this book, scientists, artists, historians, and philosophers discuss the multidisciplinary quest to formalize and understand the generation of intelligent behavior in natural and artificial systems as a wholly mechanical process. The chapters illustrate the diverse and interacting notions that chart the evolution of the idea of the mechanical mind. They describe the mechanized mind as, among other things, an analogue system, an organized suite of chemical interactions, a self-organizing electromechanical device, an automated general-purpose information processor, and an integrated collection of symbol-manipulating mechanisms. The chapters investigate the views of pivotal figures that range from Descartes and Heidegger to Alan Turing and Charles Babbage, and emphasize such frequently overlooked areas as British cybernetic and pre-cybernetic thinkers. The book concludes with the personal insights of five highly influential figures in the field: John Maynard Smith, John Holland, Oliver Selfridge, Horace Barlow, and Jack Cowan.

The capacity to think about our own thinking may lie at the heart of what it means to be both human and intelligent. Philosophers and cognitive scientists have investigated these matters for many years. Researchers in artificial intelligence have gone further, attempting to implement actual machines that mimic, simulate, and perhaps even replicate this capacity, called metareasoning. This book offers a variety of perspectives—drawn from philosophy, cognitive psychology, and computer science—on reasoning about the reasoning process. It offers a simple model of reasoning about reason as a framework for its discussions. Following this framework, the contributors consider metalevel control of computational activities, introspective monitoring, distributed metareasoning, and, putting all these aspects of metareasoning together, models of the self. Taken together, the chapters offer an integrated narrative on metareasoning themes from both artificial intelligence and cognitive science perspectives.

This book guides students through an exploration of the idea that thinking might be understood as a form of computation. Students make the connection between thinking and computing by learning to write computer programs for a variety of tasks that require thought, including solving puzzles, understanding natural language, recognizing objects in visual scenes, planning courses of action, and playing strategic games. The material is presented with minimal technicalities and is accessible to undergraduate students with no specialized knowledge or technical background beyond high school mathematics. Students use Prolog, learning to express what they need as a Prolog program and letting Prolog search for answers. After an introduction to the basic concepts, the book offers three chapters on Prolog, covering back-chaining, programs and queries, and how to write the sorts of Prolog programs used in the book. The book follows this with case studies of tasks that appear to require thought, then looks beyond Prolog to consider learning, explaining, and propositional reasoning. Most of the chapters conclude with short bibliographic notes and exercises.