Your cart is empty
This book constitutes the revised selected papers of the 9th Italian Workshop on Advances in Artificial Life and Evolutionary Computation held in Vietri sul Mare, Italy, in May 2014, in conjunction with the 24th Italian Workshop on Neural Networks, WIRN 2014. The 16 papers presented have been thoroughly reviewed and selected from 40 submissions. They cover the following topics: artificial neural networks; fuzzy inference systems; rough set; approximate reasoning; and optimization methods such as evolutionary computation, swarm intelligence, particle swarm optimization.
This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Agents and Artificial Intelligence, ICAART 2013, held in Barcelona, Spain, in February 2013. The 20 revised full papers presented together with one invited paper were carefully reviewed and selected from 269 submissions. The papers are organized in two topical sections on artificial intelligence and on agents.
This book describes the authors' investigations of computational sarcasm based on the notion of incongruity. In addition, it provides a holistic view of past work in computational sarcasm and the challenges and opportunities that lie ahead. Sarcastic text is a peculiar form of sentiment expression and computational sarcasm refers to computational techniques that process sarcastic text. To first understand the phenomenon of sarcasm, three studies are conducted: (a) how is sarcasm annotation impacted when done by non-native annotators? (b) How is sarcasm annotation impacted when the task is to distinguish between sarcasm and irony? And (c) can targets of sarcasm be identified by humans and computers. Following these studies, the book proposes approaches for two research problems: sarcasm detection and sarcasm generation. To detect sarcasm, incongruity is captured in two ways: `intra-textual incongruity' where the authors look at incongruity within the text to be classified (i.e., target text) and `context incongruity' where the authors incorporate information outside the target text. These approaches use machine-learning techniques such as classifiers, topic models, sequence labelling, and word embeddings. These approaches operate at multiple levels: (a) sentiment incongruity (based on sentiment mixtures), (b) semantic incongruity (based on word embedding distance), (c) language model incongruity (based on unexpected language model), (d) author's historical context (based on past text by the author), and (e) conversational context (based on cues from the conversation). In the second part of the book, the authors present the first known technique for sarcasm generation, which uses a template-based approach to generate a sarcastic response to user input. This book will prove to be a valuable resource for researchers working on sentiment analysis, especially as applied to automation in social media.
This book constitutes the refereed proceedings of the 7th International Conference on Knowledge Science, Engineering and Management, KSEM 2014, held in Sibiu, Romania, in October 2014. The 30 revised full papers presented together with 5 short papers and 3 keynotes were carefully selected and reviewed from 77 submissions. The papers are organized in topical sections on formal semantics; content and document analysis; concept and lexical analysis; clustering and classification; metamodeling and conceptual modeling; enterprise knowledge; knowledge discovery and retrieval; formal knowledge processing; ontology engineering and management; knowledge management; and hybrid knowledge systems.
This book constitutes the refereed proceedings of the 8th International Conference on Geographic Information Science, GIScience 2014, held in Vienna, Austria in September 2014. The 23 full papers presented were carefully reviewed and selected from various submissions. The papers are organized in topical sections such as information visualization, spatial analysis, user-generated content, semantic models, wayfinding and navigation, spatial algorithms, and spatial relations.
This book highlights recent advances in natural computing, including biology and its theory, bio-inspired computing, computational aesthetics, computational models and theories, computing with natural media, philosophy of natural computing and educational technology. It presents extended versions of the best papers selected from the symposium "7th International Workshop on Natural Computing" (IWNC7), held in Tokyo, Japan, in 2013. The target audience is not limited to researchers working in natural computing but also those active in biological engineering, fine/media art design, aesthetics and philosophy.
This volume contains the lecture notes of the 10th Reasoning Web Summer School 2014, held in Athens, Greece, in September 2014. In 2014, the lecture program of the Reasoning Web introduces students to recent advances in big data aspects of semantic web and linked data, and the fundamentals of reasoning techniques that can be used to tackle big data applications.
This book brings together philosophical approaches to cooperation and collective agency with research into human-machine interaction and cooperation from engineering, robotics, computer science and AI. Bringing these so far largely unrelated fields of study together leads to a better understanding of collective agency in natural and artificial systems and will help to improve the design and performance of hybrid systems involving human and artificial agents. Modeling collective agency with the help of computer simulations promises also philosophical insights into the emergence of collective agency. The volume consists of four sections. The first section is dedicated to the concept of agency. The second section of the book turns to human-machine cooperation. The focus of the third section is the transition from cooperation to collective agency. The last section concerns the explanatory value of social simulations of collective agency in the broader framework of cultural evolution.
This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.
This book provides an argumentation model for means end-reasoning, a distinctive type of reasoning used for problem-solving and decision-making. Means end-reasoning is modelled as goal-directed argumentation from an agent's goals and known circumstances, and from an action selected as a means, to a decision to carry out the action. Goal-based Reasoning for Argumentation provides an argumentation model of this kind of reasoning showing how it is employed in settings of intelligent deliberation where agents try to collectively arrive at a conclusion on what they should do to move forward in a set of circumstances. The book explains how this argumentation model can help build more realistic computational systems of deliberation and decision-making, and shows how such systems can be applied to solve problems posed by goal-based reasoning in numerous fields, from social psychology and sociology, to law, political science, anthropology, cognitive science, artificial intelligence, multi-agent systems, and robotics.
It's nearly impossible to build a competent Go-playing machine using conventional programming techniques, let alone have it win. By applying advanced AI techniques, in particular deep learning and reinforcement learning, users can train their Go-bot in the rules and tactics of the game. Deep Learning and the Game of Go opens up the world of deep learning and AI by teaching readers to build their own Go-playing machine. Key Features * Getting started with neural networks * Building your Go AI * Improving how your Go-bot plays and reacts Audience No deep learning experience required. All you need is high school level math and basic Python skills. This book even teaches you how to play Go! Author Bio Max Pumperla is a Data Scientist and Engineer specializing in Deep Learning at the artificial intelligence company skymind.ai. He is the cofounder of the Deep Learning platform aetros.com. Kevin Ferguson has 18 years of experience in distributed systems and data science. He is a data scientist at Honor, and has experience at companies such as Google and Meebo. Together, Max and Kevin are co-authors of betago, one of very few open source Go bots, developed in Python.
This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved. Reviews: "this book is easy to read and entertaining, and much can be learned from it. Even if you know just about everything about large-scale and temporal networks, the book is a worthwhile read; you will learn a lot about SNA literature, patents, the US Supreme Court, and European soccer." (Social Networks) "a clear and accessible textbook, balancing symbolic maths, code, and visual explanations. The authors enthusiasm for the subject matter makes it enjoyable to read" (JASSS)
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Learn how to create more challenging and dynamic games with AI and Artificial Life in Video Games. AI, or artificial intelligence, builds better games by directing behaviors inside the games that make them more difficult, while artificial life, or A-Life, adds unpredictability of play and a more lifelike environment to games. This book examines easy and inexpensive methods for implementing AI and A-Life in any video game to not only model behavior in the game but also create tools, generate code, and test the game during development. After introducing the basics of AI and A-Life to use as building blocks, the book delves into more advanced methods and examines possible future uses and techniques. Youall learn how AI can be built up in a game by layering behavioral models on static data to produce behavior that is both intelligent and unpredictable. Examples of several A-Life enhancements in games are presented, and youall investigate the potential pitfalls of using AI and how to troubleshoot, apply A-Life to your own games, test A-Life itself and test virtually using A-Life, implement AI and A-Life in a multiplayer environment, and more. Written for the current and next-generation game developer, AI and Artificial Life in Video Games is a great reference for both game programmers and game designers.
Recent startling successes in machine intelligence using a technique called 'deep learning' seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the 'Surrender'. By dissecting the intricacies of language use and meaning, Collins shows how far we have to go before we cannot distinguish between the social understanding of humans and computers. When the stakes are so high, we need to set the bar higher: to rethink 'intelligence' and recognize its inherent social basis. Only if machine learning succeeds on this count can we congratulate ourselves on having produced artificial intelligence.
With continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike.
Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.
This book constitutes the proceedings of the International Workshop on Vagueness in Communication, VIC 2009, held as part of ESSLLI 2009, in Bordeaux, France, July 20-24, 2009. The 11 contributions presented shed a light on new aspects in the area of vagueness in natural language communication. In contrast to the classical instruments of dealing with vagueness - like multi-valued logics, truth value gaps or gluts, or supervaluations - this volume presents new approaches like context-sensitivity of vagueness, the sharpening of vague predicates in context, and the modeling of precision levels.
Over the coming decades, Artificial Intelligence will profoundly impact the way we live, work, wage war, play, seek a mate, educate our young, and care for our elderly. It is likely to greatly increase our aggregate wealth, but it will also upend our labor markets, reshuffle our social order, and strain our private and public institutions. Eventually it may alter how we see our place in the universe, as machines pursue goals independent of their creators and outperform us in domains previously believed to be the sole dominion of humans. Whether we regard them as conscious or unwitting, revere them as a new form of life or dismiss them as mere clever appliances, is beside the point. They are likely to play an increasingly critical and intimate role in many aspects of our lives. The emergence of systems capable of independent reasoning and action raises serious questions about just whose interests they are permitted to serve, and what limits our society should place on their creation and use. Deep ethical questions that have bedeviled philosophers for ages will suddenly arrive on the steps of our courthouses. Can a machine be held accountable for its actions? Should intelligent systems enjoy independent rights and responsibilities, or are they simple property? Who should be held responsible when a self-driving car kills a pedestrian? Can your personal robot hold your place in line, or be compelled to testify against you? If it turns out to be possible to upload your mind into a machine, is that still you? The answers may surprise you.
The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.
Artificial intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms. It is a critical branch of cognitive science, and its influence is increasingly being felt in other areas, including the humanities. AI applications are transforming the way we interact with each other and with our environment, and work in artificially modeling intelligence is offering new insights into the human mind and revealing new forms mentality can take. This volume of original essays presents the state of the art in AI, surveying the foundations of the discipline, major theories of mental architecture, the principal areas of research, and extensions of AI such as artificial life. With a focus on theory rather than technical and applied issues, the volume will be valuable not only to people working in AI, but also to those in other disciplines wanting an authoritative and up-to-date introduction to the field.
This book describes-in modern computer science terms-the Level II architecture of the meaning and definition of the process referred to as 'thinking'. It applies the basis of early cognitive science research to the creation of autonomous system architectures - connecting philosophical findings of the past with cutting-edge progress in artificial intelligence. Providing an in-depth introduction to the classical, philosophical theories of cognitive scientists like Immanuel Kant, Arthur Schopenhauer, and G.W.F. Hegel, the book examines the Will System, Reason System, Imagination System, and the Communication System.
The book presents selected research papers on current developments in the field of soft computing and signal processing from the International Conference on Soft Computing and Signal Processing (ICSCSP 2018). It includes papers on current topics such as soft sets, rough sets, fuzzy logic, neural networks, genetic algorithms and machine learning, discussing various aspects of these topics, like technological, product implementation, contemporary research as well as application issues.
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
You may like...
Autonomy - The Quest to Build the…
Lawrence D. Burns, Christopher Shulgan Paperback
Novacene - The Coming Age of…
James Lovelock Hardcover (1)
The Creativity Code - How AI is Learning…
Marcus du Sautoy Paperback (1)
We have been harmonised - Life in…
Kai Strittmatter Paperback (1)
AI for Marketing and Product Innovation…
A K Pradeep, Andrew Appel, … Hardcover
The Technology Trap - Capital, Labor…
Carl Benedikt Frey Hardcover
The Elements of Statistical Learning…
Trevor Hastie, Robert Tibshirani, … Hardcover
Superminds - The Surprising Power of…
Thomas W. Malone Paperback (1)
Automated Planning and Acting
Malik Ghallab, Dana Nau, … Hardcover
Superintelligence - Paths, Dangers…
Nick Bostrom Paperback (2)