Reasoning Web. Explainable Artificial Intelligence, Markus Kr?tzsch; Daria Stepanova
Автор: Millington, Ian, Funge, John Название: Artificial Intelligence for Games ISBN: 0123747317 ISBN-13(EAN): 9780123747310 Издательство: Taylor&Francis Рейтинг: Цена: 10870.00 р. Наличие на складе: Поставка под заказ.
Описание: Creating robust artificial intelligence is one of the greatest challenges for game developers, yet the commercial success of a game is often dependent upon the quality of the AI. In this book, Ian Millington brings extensive professional experience to the problem of improving the quality of AI in games. He describes numerous examples from real games and explores the underlying ideas through detailed case studies. He goes further to introduce many techniques little used by developers today. The book's associated web site contains a library of C++ source code and demonstration programs, and a complete commercial source code library of AI algorithms and techniques.<br><br>"Artificial Intelligence for Games - 2nd edition" will be highly useful to academics teaching courses on game AI, in that it includes exercises with each chapter. It will also include new and expanded coverage of the following: AI-oriented gameplay; Behavior driven AI; Casual games (puzzle games). <br><br>* The first comprehensive, professional tutorial and reference to implement true AI in games written by an engineer with extensive industry experience.<br>* Walks through the entire development process from beginning to end.<br>* Includes examples from over 100 real games, 10 in-depth case studies, and web site with sample code.
Описание: This book constitutes revised selected papers from the AIME 2019 workshops KR4HC/ProHealth 2019, the Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, and TEAAM 2019, the Workshop on Transparent, Explainable and Affective AI in Medical Systems.
Описание: With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications.This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making.This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.
Автор: Davide Calvaresi; Amro Najjar; Michael Schumacher; Название: Explainable, Transparent Autonomous Agents and Multi-Agent Systems ISBN: 303030390X ISBN-13(EAN): 9783030303907 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the proceedings of the First International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, EXTRAAMAS 2019, held in Montreal, Canada, in May 2019. The 12 revised and extended papers presented were carefully selected from 23 submissions. explainable agent simulations;
Автор: Wojciech Samek; Gr?goire Montavon; Andrea Vedaldi; Название: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning ISBN: 3030289532 ISBN-13(EAN): 9783030289539 Издательство: Springer Рейтинг: Цена: 10340.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner.The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Автор: Jacques Calmet; Belaid Benhamou; Olga Caprotti; La Название: Artificial Intelligence, Automated Reasoning, and Symbolic Computation ISBN: 3540438653 ISBN-13(EAN): 9783540438656 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the proceedings of the joint International Conferences on Artificial Intelligence and Symbolic Computation, and Calculemus 2002, held in France in 2002. The 24 papers cover automated theorem proving, logical reasoning, mathematical modeling, algebraic computations and more.
Описание: Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems surveys, analyzes, and compares the most effective algorithms for mining all kinds of logical rules. Global academics and professionals in related fields have come together to create this unique knowledge-sharing resources which will serve as a forum for future collaborations.
Автор: Matthias Baaz; Andrei Voronkov Название: Logic for Programming, Artificial Intelligence, and Reasoning ISBN: 3540000100 ISBN-13(EAN): 9783540000105 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Compiled from the proceedings of the 9th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning, this volume contains 30 papers. Among the topics covered are constraint programming, formal software enginering, formal verification, resolution and proof planning.
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning· Explanation Methods in Deep Learning· Learning Functional Causal Models with Generative Neural Networks· Learning Interpreatable Rules for Multi-Label Classification· Structuring Neural Networks for More Explainable Predictions· Generating Post Hoc Rationales of Deep Visual Classification Decisions· Ensembling Visual Explanations· Explainable Deep Driving by Visualizing Causal Attention· Interdisciplinary Perspective on Algorithmic Job Candidate Search· Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
Автор: Iliano Cervesato; Helmut Veith; Andrei Voronkov Название: Logic for Programming, Artificial Intelligence, and Reasoning ISBN: 3540894381 ISBN-13(EAN): 9783540894384 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning, LPAR 2008, which took place in Doha, Qatar, during November 22-27, 2008. This book contains the papers that address issues in automated reasoning, computational logic, and programming languages.
Автор: Ken McMillan; Aart Middeldorp; Andrei Voronkov Название: Logic for Programming, Artificial Intelligence, and Reasoning ISBN: 3642452205 ISBN-13(EAN): 9783642452208 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Algorithm for Enumerating Maximal Models of Horn Theories with an Application to Modal Logics.- May-Happen-in-Parallel Analysis for Priority-Based Scheduling.- The Complexity of Clausal Fragments of LTL.- A Semantic Basis for Proof Queries and Transformations.- Expressive Path Queries on Graphs with Data.- Proving Infinite Satisfiability.- SAT-Based Preprocessing for MaxSAT.- Dynamic and Static Symmetry Breaking in Answer Set Programming.- HOL Based First-Order Modal Logic Provers.- Resourceful Reachability as HORN-LA.- A Seligman-Style Tableau System.- Comparison of LTL to Deterministic Rabin Automata Translators.- Tree Interpolation in Vampire.- Polarizing Double-Negation Translations.- Revisiting the Equivalence of Shininess and Politeness.- Towards Rational Closure for Fuzzy Logic: The Case of Propositional Gцdel Logic.- Multi-objective Discounted Reward Verification in Graphs and MDPs.- Description Logics, Rules and Multi-context Systems.- Complexity Analysis in Presence of Control Operators and Higher-Order Functions.- Zenon Modulo: When Achilles Outruns the Tortoise Using Deduction Modulo.- Long-Distance Resolution: Proof Generation and Strategy Extraction in Search-Based QBF Solving.- Verifying Temporal Properties in Real Models.- A Graphical Language for Proof Strategies.- A Proof of Strong Normalisation of the Typed Atomic Lambda-Calculus.- Relaxing Synchronization Constraints in Behavioral Programs.- Characterizing Subset Spaces as Bi-topological Structures.- Proof-Pattern Recognition and Lemma Discovery in ACL2.- Semantic A-translations and Super-Consistency Entail Classical Cut Elimination.- Blocked Clause Decomposition.- Maximal Falsifiability: Definitions, Algorithms, and Applications.- Solving Geometry Problems Using a Combination of Symbolic and Numerical Reasoning.- On QBF Proofs and Preprocessing.- Partial Backtracking in CDCL Solvers.- Lemma Mining over HOL Light.- On Module-Based Abstraction and Repair of Behavioral Programs.- Prediction and Explanation over DL-Lite Data Streams.- Forgetting Concept and Role Symbols in ALCH-Ontologies.- Simulating Parity Reasoning.- Herbrand Theorems for Substructural Logics.- On Promptness in Parity Games.- Defining Privacy Is Supposed to Be Easy.- Reachability Modules for the Description Logic SRIQ.- An Event Structure Model for Probabilistic Concurrent Kleene Algebra.- Three SCC-Based Emptiness Checks for Generalized Bьchi Automata.- PeRIPLO: A Framework for Producing Effective Interpolants in SAT-Based Software Verification.- Incremental Tabling for Query-Driven Propagation of Logic Program Updates.- Tracking Data-Flow with Open Closure Types.- Putting Newton into Practice: A Solver for Polynomial Equations over Semirings.- System Description: E 1.8.- Formalization of Laplace Transform Using the Multivariable Calculus Theory of HOL-Light.- On Minimality and Integrity Constraints in Probabilistic Abduction.- POLAR: A Framework for Proof Refactoring.
Автор: Walton Название: Goal-based Reasoning for Argumentation ISBN: 1107119049 ISBN-13(EAN): 9781107119048 Издательство: Cambridge Academ Рейтинг: Цена: 12670.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Practical argumentation is intelligent reasoning from an agent`s goals and known circumstances, and from an action selected as a means, to arrive at a decision on what action to take. This book will appeal to a wide audience, from designers of multi-agent and robotics systems to social scientists.
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