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Different Types of Arguments

Description

The second of six lectures dealing with critical reasoning. In this lecture you will learn about the different types of arguments, in particular deductive and inductive arguments. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

deductive | inductive | critical reasoning | philosophy | argument | deduction | arguments | induction | reasoning | deductive | inductive | critical reasoning | philosophy | argument | deduction | arguments | induction | reasoning

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Different Types of Arguments (Slides)

Description

The second of six lectures dealing with critical reasoning. In this lecture you will learn about the different types of arguments, in particular deductive and inductive arguments. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

deductive | inductive | critical reasoning | philosophy | argument | deduction | arguments | induction | reasoning | deductive | inductive | critical reasoning | philosophy | argument | deduction | arguments | induction | reasoning

License

http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Different Types of Arguments

Description

The second of six lectures dealing with critical reasoning. In this lecture you will learn about the different types of arguments, in particular deductive and inductive arguments. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

deductive | inductive | critical reasoning | philosophy | argument | deduction | arguments | induction | reasoning | deductive | inductive | critical reasoning | philosophy | argument | deduction | arguments | induction | reasoning

License

http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.412J Cognitive Robotics (MIT) 16.412J Cognitive Robotics (MIT)

Description

Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.Technical RequirementsAny text editor can be used to view the .ascii, .binary, .map, and .pddl files found on this course site. Any number of development tools can be used to compile and run the .c and .h files found on this course site. Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.Technical RequirementsAny text editor can be used to view the .ascii, .binary, .map, and .pddl files found on this course site. Any number of development tools can be used to compile and run the .c and .h files found on this course site.

Subjects

cognitive robotics | cognitive robotics | robotic systems | robotic systems | intelligence algorithms | intelligence algorithms | robustness algorithms | robustness algorithms | intelligence paradigms | intelligence paradigms | robustness paradigms | robustness paradigms | autonomous robots | autonomous robots | mars explorers | mars explorers | cooperative air vehicles | cooperative air vehicles | embedded devices | embedded devices | real-time deduction | real-time deduction | real-time search | real-time search | temporal planning | temporal planning | decision-theoretic planning | decision-theoretic planning | contingency planning | contingency planning | dynamic execution | dynamic execution | dynamics re-planning | dynamics re-planning | reasoning | reasoning | path planning | path planning | reasoning under uncertainty | reasoning under uncertainty | mapping | mapping | localization | localization | cooperative robotics | cooperative robotics | distributed robotics | distributed robotics | mars exploration rover | mars exploration rover | nursebot | nursebot | museum tourguide | museum tourguide | human-interaction systems | human-interaction systems | navigation | navigation | state-aware robots | state-aware robots | fast planning | fast planning | cooperative planning | cooperative planning | vision-based exploration | vision-based exploration | preplanning | preplanning | 16.412 | 16.412 | 6.834 | 6.834

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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12.864 Inference from Data and Models (MIT) 12.864 Inference from Data and Models (MIT)

Description

The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themeslinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc. The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themeslinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc.

Subjects

observation | observation | kinematical models | kinematical models | dynamical models | dynamical models | basic statistics | basic statistics | linear algebra | linear algebra | inverse methods | inverse methods | singular value decompositions | singular value decompositions | control theory | control theory | sequential estimation | sequential estimation | Kalman filters | Kalman filters | smoothing algorithms | smoothing algorithms | adjoint/Pontryagin principle methods | adjoint/Pontryagin principle methods | model testing | model testing | stationary processes | stationary processes | Fourier methods | Fourier methods | z-transforms | z-transforms | sampling theorems | sampling theorems | spectra | spectra | multi-taper methods | multi-taper methods | coherences | coherences | filtering | filtering | quantitative combinations | quantitative combinations | realistic observations | realistic observations | data assimilations | data assimilations | deduction | deduction | regression | regression | objective mapping | objective mapping | time series analysis | time series analysis | inference | inference

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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24.241 Logic I (MIT) 24.241 Logic I (MIT)

Description

Introduction to the aims and techniques of formal logic. The logic of truth functions and quantifiers. The concepts of validity and truth and their relation to formal deduction. Applications of logic and the place of logic in philosophy. Introduction to the aims and techniques of formal logic. The logic of truth functions and quantifiers. The concepts of validity and truth and their relation to formal deduction. Applications of logic and the place of logic in philosophy.

Subjects

formal logic | formal logic | truth functions | truth functions | quantifiers | quantifiers | validity | validity | truth | truth | formal deduction | formal deduction | applications of logic | applications of logic | logic in philosophy | logic in philosophy

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.412J Cognitive Robotics (MIT) 16.412J Cognitive Robotics (MIT)

Description

Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover. Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.

Subjects

cognitive robotics | cognitive robotics | robotic systems | robotic systems | intelligence algorithms | intelligence algorithms | robustness algorithms | robustness algorithms | intelligence paradigms | intelligence paradigms | robustness paradigms | robustness paradigms | autonomous robots | autonomous robots | mars explorers | mars explorers | cooperative air vehicles | cooperative air vehicles | embedded devices | embedded devices | real-time deduction | real-time deduction | real-time search | real-time search | temporal planning | temporal planning | decision-theoretic planning | decision-theoretic planning | contingency planning | contingency planning | dynamic execution | dynamic execution | dynamics re-planning | dynamics re-planning | reasoning | reasoning | path planning | path planning | reasoning under uncertainty | reasoning under uncertainty | mapping | mapping | localization | localization | cooperative robotics | cooperative robotics | distributed robotics | distributed robotics | mars exploration rover | mars exploration rover | nursebot | nursebot | museum tourguide | museum tourguide | human-interaction systems | human-interaction systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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21L.017 The Art of the Probable: Literature and Probability (MIT) 21L.017 The Art of the Probable: Literature and Probability (MIT)

Description

"The Art of the Probable" addresses the history of scientific ideas, in particular the emergence and development of mathematical probability. But it is neither meant to be a history of the exact sciences per se nor an annex to, say, the Course 6 curriculum in probability and statistics. Rather, our objective is to focus on the formal, thematic, and rhetorical features that imaginative literature shares with texts in the history of probability. These shared issues include (but are not limited to): the attempt to quantify or otherwise explain the presence of chance, risk, and contingency in everyday life; the deduction of causes for phenomena that are knowable only in their effects; and, above all, the question of what it means to think and act rationally in an uncertain world. Our course "The Art of the Probable" addresses the history of scientific ideas, in particular the emergence and development of mathematical probability. But it is neither meant to be a history of the exact sciences per se nor an annex to, say, the Course 6 curriculum in probability and statistics. Rather, our objective is to focus on the formal, thematic, and rhetorical features that imaginative literature shares with texts in the history of probability. These shared issues include (but are not limited to): the attempt to quantify or otherwise explain the presence of chance, risk, and contingency in everyday life; the deduction of causes for phenomena that are knowable only in their effects; and, above all, the question of what it means to think and act rationally in an uncertain world. Our course

Subjects

philosophy | philosophy | scientific thought | scientific thought | scientific method | scientific method | mathematics | mathematics | chance | chance | risk | risk | statistics | statistics | history of science | history of science | quantitative measurement | quantitative measurement | chaos | chaos | uncertainty | uncertainty | induction | induction | deduction | deduction | inference | inference | luck | luck | gambling | gambling | cause and effect | cause and effect | games of chance | games of chance | fate | fate | prediction | prediction | rationality | rationality | decision making | decision making | religion | religion | randomness | randomness | knowledge | knowledge | fact | fact | human nature | human nature | mind | mind | senses | senses | intelligence | intelligence | metaphor | metaphor | Darwinism | Darwinism

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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24.241 Logic I (MIT) 24.241 Logic I (MIT)

Description

This course provides an introduction to the aims and techniques of formal logic. Logic is the science of correct argument, and our study of logic will aim to understand what makes a correct argument good, that is, what is it about the structure of a correct argument that guarantees that, if the premises are all true, the conclusion will be true as well? Our subject (though, to be sure, we can only scratch the surface) will be truth and proof, and the connection between them. This course provides an introduction to the aims and techniques of formal logic. Logic is the science of correct argument, and our study of logic will aim to understand what makes a correct argument good, that is, what is it about the structure of a correct argument that guarantees that, if the premises are all true, the conclusion will be true as well? Our subject (though, to be sure, we can only scratch the surface) will be truth and proof, and the connection between them.

Subjects

techniques of formal logic | techniques of formal logic | truth functions | truth functions | quantifiers | quantifiers | validity | validity | formal deduction | formal deduction | logic applications | logic applications | philosophy | philosophy

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.412J Cognitive Robotics (MIT)

Description

Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.

Subjects

cognitive robotics | robotic systems | intelligence algorithms | robustness algorithms | intelligence paradigms | robustness paradigms | autonomous robots | mars explorers | cooperative air vehicles | embedded devices | real-time deduction | real-time search | temporal planning | decision-theoretic planning | contingency planning | dynamic execution | dynamics re-planning | reasoning | path planning | reasoning under uncertainty | mapping | localization | cooperative robotics | distributed robotics | mars exploration rover | nursebot | museum tourguide | human-interaction systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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Critical Reasoning for Beginners

Description

Are you confident you can reason clearly? Are you able to convince others of your point of view? Are you able to give plausible reasons for believing what you believe? Do you sometimes read arguments in the newspapers, hear them on the television, or in the pub and wish you knew how to confidently evaluate them? In this six-part course, you will learn all about arguments, how to identify them, how to evaluate them, and how not to mistake bad arguments for good. Such skills are invaluable if you are concerned about the truth of your beliefs, and the cogency of your arguments.

Subjects

simple-podcasting | tpi3 | philosophy | arguments | critical reasoning | argument | reasoning | checked1 | induction | inductive | deduction | deductive | he - historical and philosophical studies | v500 | philosophical studies | V000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Different Types of Arguments

Description

The second of six lectures dealing with critical reasoning. In this lecture you will learn about the different types of arguments, in particular deductive and inductive arguments.

Subjects

philosophy | arguments | critical reasoning | argument | reasoning | deduction | induction | deductive | inductive | v500 | ukoer | philosophical studies | V000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Different Types of Arguments (slides)

Description

The second of six lectures dealing with critical reasoning. In this lecture you will learn about the different types of arguments, in particular deductive and inductive arguments.

Subjects

philosophy | arguments | critical reasoning | argument | reasoning | deduction | induction | deductive | inductive | v500 | ukoer | philosophical studies | V000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Critical Reasoning for Beginners

Description

Are you confident you can reason clearly? Are you able to convince others of your point of view? Are you able to give plausible reasons for believing what you believe? Do you sometimes read arguments in the newspapers, hear them on the television, or in the pub and wish you knew how to confidently evaluate them? In this six-part course, you will learn all about arguments, how to identify them, how to evaluate them, and how not to mistake bad arguments for good. Such skills are invaluable if you are concerned about the truth of your beliefs, and the cogency of your arguments.

Subjects

simple-podcasting | tpi3 | philosophy | arguments | critical reasoning | argument | reasoning | checked1 | induction | inductive | deduction | deductive | he - historical and philosophical studies | v500 | philosophical studies | V000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Different Types of Arguments

Description

The second of six lectures dealing with critical reasoning. In this lecture you will learn about the different types of arguments, in particular deductive and inductive arguments.

Subjects

philosophy | arguments | critical reasoning | argument | reasoning | deduction | induction | deductive | inductive | v500 | ukoer | philosophical studies | V000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Different Types of Arguments (slides)

Description

The second of six lectures dealing with critical reasoning. In this lecture you will learn about the different types of arguments, in particular deductive and inductive arguments.

Subjects

philosophy | arguments | critical reasoning | argument | reasoning | deduction | induction | deductive | inductive | v500 | ukoer | philosophical studies | V000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Deduction and speculation

Description

An introduction to deduction and speculation, comprising a learning context and associated assessment

Subjects

deduction | speculation | efl | english language | Linguistics | AREA STUDIES / CULTURAL STUDIES / LANGUAGES / LITERATURE | Learning | Students | UK EL04 = SCQF 4 | Foundational Level | NICAT 1 | CQFW 1 | Foundation | GCSE D-G | NVQ 1 | Intermediate 1 | | related subjects | Q000 | F

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Research Skills 1

Description

Session looking at arguement, evidence and reasoning, deduction and induction, Epistemology and Knowledge. These resources have been developed through the Learning from WOeRK project and seek to support learning in the work place. For an overview of all related modules and resources please visit http://cpdoer.net/collections.

Subjects

ukoer | lfwoer | uopcpdrm | learning from woerk | argument | evidence | reasoning | deduction | induction | Education | X000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Logic and Critical Thinking

Description

This course provides an introduction to critical thinking, informal logic, and a small amount of formal logic; its purpose is to provide students with the basic tools of analytical reasoning. This free course may be completed online at any time. See course site for detailed overview and learning outcomes. (Philosophy 102)

Subjects

philosophy | meaning | argument | validity | fallacies | induction | deduction | logic | truth tables | formalization | venn diagrams | syllogism | scientific reasoning | causality | correlation | strategic reasoning | creative thinking | morality | relativism | reflective equilibrium | philosophical studies | V000

License

Attribution 2.0 UK: England & Wales Attribution 2.0 UK: England & Wales http://creativecommons.org/licenses/by/2.0/uk/ http://creativecommons.org/licenses/by/2.0/uk/

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16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | decision | decision-making | reasoning | optimization | autonomous | autonomous systems | decision support | algorithms | artificial intelligence | a.i. | operations | operations research | logic | deduction | heuristic search | constraint-based search | model-based reasoning | planning | execution | uncertainty | machine learning | linear programming | dynamic programming | integer programming | network optimization | decision analysis | decision theoretic planning | Markov decision process | scheme | propositional logic | constraints | Markov processes | computational performance | satisfaction | learning algorithms | system state | state | search treees | plan spaces | model theory | decision trees | function approximators | optimization algorithms | limitations | tradeoffs | search and reasoning | game tree search | local stochastic search | stochastic | genetic algorithms | constraint satisfaction | propositional inference | rule-based systems | rule-based | model-based diagnosis | neural nets | reinforcement learning | web-based | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | decision | decision-making | reasoning | optimization | autonomous | autonomous systems | decision support | algorithms | artificial intelligence | a.i. | operations | operations research | logic | deduction | heuristic search | constraint-based search | model-based reasoning | planning | execution | uncertainty | machine learning | linear programming | dynamic programming | integer programming | network optimization | decision analysis | decision theoretic planning | Markov decision process | scheme | propositional logic | constraints | Markov processes | computational performance | satisfaction | learning algorithms | system state | state | search treees | plan spaces | model theory | decision trees | function approximators | optimization algorithms | limitations | tradeoffs | search and reasoning | game tree search | local stochastic search | stochastic | genetic algorithms | constraint satisfaction | propositional inference | rule-based systems | rule-based | model-based diagnosis | neural nets | reinforcement learning | web-based | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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16.412J Cognitive Robotics (MIT)

Description

Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.Technical RequirementsAny text editor can be used to view the .ascii, .binary, .map, and .pddl files found on this course site. Any number of development tools can be used to compile and run the .c and .h files found on this course site.

Subjects

cognitive robotics | robotic systems | intelligence algorithms | robustness algorithms | intelligence paradigms | robustness paradigms | autonomous robots | mars explorers | cooperative air vehicles | embedded devices | real-time deduction | real-time search | temporal planning | decision-theoretic planning | contingency planning | dynamic execution | dynamics re-planning | reasoning | path planning | reasoning under uncertainty | mapping | localization | cooperative robotics | distributed robotics | mars exploration rover | nursebot | museum tourguide | human-interaction systems | navigation | state-aware robots | fast planning | cooperative planning | vision-based exploration | preplanning | 16.412 | 6.834

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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12.864 Inference from Data and Models (MIT)

Description

The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themeslinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc.

Subjects

observation | kinematical models | dynamical models | basic statistics | linear algebra | inverse methods | singular value decompositions | control theory | sequential estimation | Kalman filters | smoothing algorithms | adjoint/Pontryagin principle methods | model testing | stationary processes | Fourier methods | z-transforms | sampling theorems | spectra | multi-taper methods | coherences | filtering | quantitative combinations | realistic observations | data assimilations | deduction | regression | objective mapping | time series analysis | inference

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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