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This book by one of the world's foremost philosophers in the fields of epistemology and logic offers an account of suppositional reasoning relevant to practical deliberation, explanation, prediction and hypothesis testing. Suppositions made 'for the sake of argument' sometimes conflict with our beliefs, and when they do, some beliefs are rejected and others retained. Thanks to such belief contravention, adding content to a supposition can undermine conclusions reached without it. Subversion can also arise because suppositional reasoning is ampliative. These two types of nonmonotonic logic are the focus of this book. A detailed comparison of nonmonotonicity appropriate to both belief contravening and ampliative suppositional reasoning reveals important differences that have been overlooked.
Commonsense reasoning. --- Conditionals (Logic) --- Hypothesis. --- Induction (Logic) --- Inference. --- Nonmonotonic reasoning. --- Conditionals (Logic). --- Induction (Logic). --- Commonsense reasoning --- Hypothesis --- Inference --- Nonmonotonic reasoning --- Non-monotonic reasoning --- Reasoning --- Ampliative induction --- Induction, Ampliative --- Inference (Logic) --- Inductive logic --- Logic, Inductive --- Logic --- Assumption --- Supposition --- Science --- Conditional statements (Logic) --- Grammar, Comparative and general --- Common sense reasoning --- Methodology --- Induction (logique) --- Arts and Humanities --- Philosophy --- Philosophie --- Artificial intelligence --- Logique --- Knowledge representation
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Using arresting case studies of how ordinary people understand the concepts of race, class, and gender, Celine-Marie Pascale shows that the peculiarity of commonsense is that it imposes obviousness-that which we cannot fail to recognize. As a result, how we negotiate the challenges of inequality in the twenty-first century may depend less on what people consciously think about ""difference"" and more on what we inadvertently assume. Through an analysis of commonsense knowledge, Pascale expertly provides new insights into familiar topics. In addition, by analyzing local practices in the cont
Classism - United States. --- Classism -- United States. --- Commonsense reasoning - United States. --- Commonsense reasoning -- United States. --- Discourse analysis. --- Racism - United States. --- Racism -- United States. --- Racism in language. --- Sexism - United States. --- Sexism -- United States. --- Sexism in language - United States. --- Sexism in language -- United States. --- Social classes in mass media. --- Social perception - United States. --- Social perception -- United States. --- Social perception --- Classism --- Racism --- Sexism --- Sexism in language --- Racism in language --- Social classes in mass media --- Commonsense reasoning --- Discourse analysis --- Sociology & Social History --- Social Sciences --- Social Conditions --- Discourse grammar --- Text grammar --- Semantics --- Semiotics --- Common sense reasoning --- Reasoning --- Mass media --- Language and racism --- Racism and language --- Racist language --- Language and languages --- Sexist language --- Language and sex --- Nonsexist language --- Attitude (Psychology) --- Prejudices --- Social classes --- Cognition, Social --- Interpersonal perception --- Social cognition --- Interpersonal relations --- Perception --- Social cognitive theory --- Sex differences
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To endow computers with common sense is one of the major long-term goals of artificial intelligence research. One approach to this problem is to formalize commonsense reasoning using mathematical logic. Commonsense Reasoning: An Event Calculus Based Approach is a detailed, high-level reference on logic-based commonsense reasoning. It uses the event calculus, a highly powerful and usable tool for commonsense reasoning, which Erik Mueller demonstrates as the most effective tool for the broadest range of applications. He provides an up-to-date work promoting the use of the event calculus for commonsense world.The Second Edition features new chapters on commonsense reasoning using unstructured information including the Watson system, commonsense reasoning using answer set programming, and techniques for acquisition of commonsense knowledge including crowdsourcing.
Commonsense reasoning --- Artificial intelligence --- Logic, Symbolic and mathematical --- Automation. --- Mathematics. --- Data processing. --- Algebra of logic --- Logic, Universal --- Mathematical logic --- Symbolic and mathematical logic --- Symbolic logic --- Mathematics --- Algebra, Abstract --- Metamathematics --- Set theory --- Syllogism --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Common sense reasoning --- Reasoning
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To endow computers with common sense is one of the major long-term goals of Artificial Intelligence research. One approach to this problem is to formalize commonsense reasoning using mathematical logic. Commonsense Reasoning is a detailed, high-level reference on logic-based commonsense reasoning. It uses the event calculus, a highly powerful and usable tool for commonsense reasoning, which Erik T. Mueller demonstrates as the most effective tool for the broadest range of applications. He provides an up-to-date work promoting the use of the event calculus for commonsense reasoning, and bringing
Commonsense reasoning --- Artificial intelligence --- Logic, Symbolic and mathematical --- Automation --- Mathematics --- Data processing --- 681.3*I24 --- 681.3*I28 --- Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence) --- Problem solving, control methods and search: backtracking; dynamic program- ming; graph and tree search strategies; heuristics; plan execution, formationand generation (Artificial intelligence)--See also {681.3*F22} --- 681.3*I24 Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence) --- 681.3*I28 Problem solving, control methods and search: backtracking; dynamic program- ming; graph and tree search strategies; heuristics; plan execution, formationand generation (Artificial intelligence)--See also {681.3*F22} --- Algebra of logic --- Logic, Universal --- Mathematical logic --- Symbolic and mathematical logic --- Symbolic logic --- Algebra, Abstract --- Metamathematics --- Set theory --- Syllogism --- Common sense reasoning --- Reasoning --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Mathematics. --- Data processing. --- Automation.
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