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This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems.
Technology: general issues --- belief-desire-intention (BDI) --- jason --- robot operating system (ROS) --- robotic agents --- collective autonomy --- self-organisation --- aggregate computing --- multi-agent systems --- coordination --- robotics --- software engineering --- verification and validation --- human–agent interaction --- Rules of the Road --- Autonomous Vehicles --- agents --- model checking --- self-driving vehicle --- formal verification --- rational agent --- decision-making --- ROS --- n/a --- human-agent interaction
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This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems.
belief-desire-intention (BDI) --- jason --- robot operating system (ROS) --- robotic agents --- collective autonomy --- self-organisation --- aggregate computing --- multi-agent systems --- coordination --- robotics --- software engineering --- verification and validation --- human–agent interaction --- Rules of the Road --- Autonomous Vehicles --- agents --- model checking --- self-driving vehicle --- formal verification --- rational agent --- decision-making --- ROS --- n/a --- human-agent interaction
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This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems.
Technology: general issues --- belief-desire-intention (BDI) --- jason --- robot operating system (ROS) --- robotic agents --- collective autonomy --- self-organisation --- aggregate computing --- multi-agent systems --- coordination --- robotics --- software engineering --- verification and validation --- human-agent interaction --- Rules of the Road --- Autonomous Vehicles --- agents --- model checking --- self-driving vehicle --- formal verification --- rational agent --- decision-making --- ROS
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The scientific study of complex systems has transformed a wide range of disciplines in recent years, enabling researchers in both the natural and social sciences to model and predict phenomena as diverse as earthquakes, global warming, demographic patterns, financial crises, and the failure of materials. In this book, Didier Sornette boldly applies his varied experience in these areas to propose a simple, powerful, and general theory of how, why, and when stock markets crash. Most attempts to explain market failures seek to pinpoint triggering mechanisms that occur hours, days, or weeks before the collapse. Sornette proposes a radically different view: the underlying cause can be sought months and even years before the abrupt, catastrophic event in the build-up of cooperative speculation, which often translates into an accelerating rise of the market price, otherwise known as a "bubble." Anchoring his sophisticated, step-by-step analysis in leading-edge physical and statistical modeling techniques, he unearths remarkable insights and some predictions--among them, that the "end of the growth era" will occur around 2050. Sornette probes major historical precedents, from the decades-long "tulip mania" in the Netherlands that wilted suddenly in 1637 to the South Sea Bubble that ended with the first huge market crash in England in 1720, to the Great Crash of October 1929 and Black Monday in 1987, to cite just a few. He concludes that most explanations other than cooperative self-organization fail to account for the subtle bubbles by which the markets lay the groundwork for catastrophe. Any investor or investment professional who seeks a genuine understanding of looming financial disasters should read this book. Physicists, geologists, biologists, economists, and others will welcome Why Stock Markets Crash as a highly original "scientific tale," as Sornette aptly puts it, of the exciting and sometimes fearsome--but no longer quite so unfathomable--world of stock markets.
Stocks --- Financial crises --- Prices --- History. --- United States. --- Asia. --- Black Monday. --- Dow Jones Industrial Average. --- Hong Kong. --- Latin America. --- Louis Bachelier. --- Nasdaq index. --- Nasdaq. --- Nikkei. --- Russia. --- South Sea bubble. --- anti-imitation. --- antibubble. --- arbitrage opportunities. --- bubble. --- collapse. --- complex systems. --- computational methods. --- cooperative behavior. --- cooperative speculation. --- crash hazard. --- currency crash. --- derivatives. --- discrete scale invariance. --- drawdown. --- efficient market. --- emergent markets. --- extreme events. --- financial crashes. --- finite-time singularity. --- forward prediction. --- fractals. --- free lunch. --- gold. --- hazard rate. --- hedging. --- herding. --- imitation. --- insurance portfolio. --- log-periodicity. --- market failure. --- natural scientists. --- outlier. --- population dynamics. --- positive feedback. --- power law. --- prediction. --- price-driven model. --- random walk. --- rational agent. --- renormalization group. --- returns. --- risk-driven model. --- risk. --- self-organization. --- self-similarity. --- social network. --- social scientists. --- speculative bubble. --- stock market crash. --- stock market indices. --- stock market prices. --- stock market. --- superhumans. --- sustainability. --- tronics boom. --- tulip mania. --- world economy.
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At the heart of human intelligence rests a fundamental puzzle: How are we incredibly smart and stupid at the same time? No existing machine can match the power and flexibility of human perception, language, and reasoning. Yet, we routinely commit errors that reveal the failures of our thought processes. 'What Makes Us Smart' makes sense of this paradox by arguing that our cognitive errors are not haphazard. Rather, they are the inevitable consequences of a brain optimized for efficient inference and decision making within the constraints of time, energy, and memory - in other words, data and resource limitations. Framing human intelligence in terms of these constraints, Samuel Gershman shows how a deeper computational logic underpins the 'stupid' errors of human cognition.
Cognition --- Cognitive psychology. --- Age factors. --- Psychology, Cognitive --- Cognitive science --- Psychology --- Age factors in cognition --- Ability, Influence of age on --- Cognition. --- Intellect. --- Human intelligence --- Intelligence --- Mind --- Ability --- Thought and thinking --- Accuracy and precision. --- Action potential. --- Ad hoc hypothesis. --- Ad hominem. --- Adaptive bias. --- Almost surely. --- Alternative hypothesis. --- Altruism. --- Ambiguity. --- Analogy. --- Anecdote. --- Approximation. --- Attractiveness. --- Bayes' theorem. --- Bayesian inference. --- Bayesian probability. --- Bayesian. --- Behavior. --- Circular reasoning. --- Cognitive flexibility. --- Cognitive style. --- Commitment device. --- Confidence. --- Confirmation bias. --- Conspiracy theory. --- Controllability. --- Counterintuitive. --- Credibility. --- Decision-making. --- Effectiveness. --- Efficacy. --- Efficiency. --- Efficient coding hypothesis. --- Efficient frontier. --- Estimation. --- Expected value. --- Explanation. --- Fair coin. --- Fair market value. --- Gimmick. --- Guessing. --- Heuristic. --- Hot Hand. --- Human intelligence. --- Hypothesis. --- Illusion of control. --- Inductive bias. --- Inference. --- Intelligent design. --- Learnability. --- Lightness (philosophy). --- Likelihood function. --- Logical extreme. --- Logical reasoning. --- Moral hazard. --- Motivated reasoning. --- Mutual exclusivity. --- Natural approach. --- Normative. --- Observation. --- Observational learning. --- Of Miracles. --- Opportunity cost. --- Optimism bias. --- Optimism. --- Our Choice. --- Pairwise comparison. --- Perfect rationality. --- Physical attractiveness. --- Point estimation. --- Politeness. --- Positive feedback. --- Predictability. --- Prediction. --- Predictive coding. --- Predictive power. --- Principle of rationality. --- Prior probability. --- Probability. --- Prosocial behavior. --- Quantity. --- Rational agent. --- Rational choice theory. --- Rationality. --- Reason. --- Reinforcement learning. --- Result. --- Self-control. --- Sophistication. --- Spontaneous recovery. --- Strong inference. --- Suggestion. --- Theory. --- Thought. --- Truth value. --- Uncertainty. --- Utility. --- Value of information. --- With high probability. --- PSYCHOLOGY / Cognitive Psychology & Cognition --- COMPUTERS / Logic Design
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