Artificial intelligence i notes on reasoning with uncertainty. Artificial intelligence, spring 20, peter ljunglof. Uncertainty in artificial intelligence sciencedirect. Because we cant see the future, we can never be certain about what exactly is going to happen day to day. Using techniques such as monte carlo simulation, they analyze the resulting uncertainty in the outcome of the model by computing an outcome probability distribution based. Interest and results in uncertainty in ai are growing beyond the capacity of a workshop format.
Student should gain the ability to apply and use these techniques for a range of problems. Is probability theory sufficient for dealing with uncertainty in ai. Reasoning under uncertainty research in ai is focused on uncertainty of truth value,in order to find the values other than true and false. Systematic or bias b uncertainty is the same in both cases, but random or precision p uncertainty is reduced by increased sample size. Immense complexity of modeling and predicting traffic hence a purely logical approach either 1 risks falsehood. In this work we develop tools to obtain practical uncertainty estimates in deep learning, casting recent. Dealing with uncertainty the robot can handle uncertainty in an obstacle position by representing the set of all positions of the obstacle that the robot think possible at each time belief state for example, this set can be a disc whose radius grows 10 linearly with time t 0 t t t 2t the robot must plan to be. In the next two lectures, well look at the question of how to make decisions, to choose actions, when theres uncertainty about what their outcomes will be. But in many domain it is not sufficient to deal only with true facts. Uncertainty objectives introduce a range of uncertainty techniques used in ai systems including. Classical probability theory is insufficiently expressive to serve as the language of uncertainty in ai. It describes the cloud model, its uncertainties of randomness and fuzziness, and the correlation between. Download artificial intelligence with uncertainty pdf ebook.
The aim of these notes is to introduce intelligent agents and reasoning, heuristic. Uncertainty in deep learning university of cambridge. In chapter 4, we encountered problemsolving agents designed to handle uncertainty by monitoring a beliefstate a representation of the set of all possible world states in which. In this post, we introduce a refined version of this method gal et al. How to tolerate uncertainty dealing with uncertainty is an unavoidable part of daily life. The student understands and appreciates the role and need for uncertainty in artificial intelligence systems. Uncertainty in ai and machine learning an overview express corresponding uncertainty. In 2016, gal and ghahramani proposed a method that is both theoretically grounded and practical. Subjective probability here can conceptually not only describe the outcomes of some experiment, but also a set of possible worlds or realities. Chapter uncertainty bilkent university computer engineering. Pdf the fourth uncertainty in artificial intelligence workshop was held 1921 august 1988. Recognition and resolution of comprehension uncertainty in ai 247 and past decision states gleaned from different sources is a setvalued rather than pointvalued feature sicilia, 2006. Quantifying uncertainty differs for single measurements versus sample means. One limitation is that it treats uncertainty as objective risk that is.
Uncertainty artificial intelligence definition,meaning. Uncertainty with respect to consequence c nonlinear preference function f note. Influence diagrams are emerging as a unifying representation, enabling tool development. How to make one decision in the face of uncertainty. The authors focus on the importance of natural languagethe carrier of knowledge and intelligencefor artificial intelligence ai study. Uncertainty in artificial intelligence 1st edition. Utility theory provides a control approach for systems whose uncertain inference is represented in a bayesian framework. The association for uncertainty in artificial intelligence is a nonprofit organization focused on organizing the annual conference on uncertainty in artificial intelligence uai and, more generally, on promoting research in pursuit of advances in knowledge representation, learning and reasoning under uncertainty. This set can be quite large and include anything imaginable. Substantial upside exists for players that can deliver valuecreating solutions and thrive under uncertainty. Representations of uncertainty in artificial intelligence.
Predictive uncertainty estimation via prior networks. It applies to predictions of future events, to physical measurements that are already made, or to the unknown. We use probability to describe the world and existing uncertainty. Every state of the world has a degree of usefulness, or utility, to an agent. Uncertainty agents need to handle uncertainty, whether due to partial observability, nondeterminism, or a combination of the two. By weighing likelihoods of events probabilities we can develop mechanisms. A 90 will get me there on time, or 2 leads to conclusions that are too weak for decision making. While probability is a simple mathematical construction, philosophically it has had at least three di erent meanings. Uncertainty arises in partially observable andor stochastic environments, as well as due to ignorance, indolence, or both. In 27th conference on uncertainty in artificial intelli.
Random uncertainty for a sample mean is estimated from the standard deviation, scaled by the tdistribution and the sample size. Uncertainty in artificial intelligence, volume 4 1st edition. Uncertainty in artificial intelligence contains the proceedings of the ninth conference on uncertainty in artificial intelligence held at the catholic university of america in washington, dc, on july 911, 1993. Dealing with uncertainty is a central challenge for artificial intelligence. In this work we develop tools to obtain practical uncertainty estimates in deep learning, casting recent deep learning tools as bayesian models without changing either the models or the optimisation. Choice under uncertainty jonathan levin october 2006 1 introduction virtually every decision is made in the face of uncertainty. Rather than impose an arbitary cutoff point on this scale, fuzzy logic would rate the subjects on a 0 to 1 scale. Fundamentally, ai software is inherently different from other forms of widely used software, said norvig, who is also a coauthor. Probability, expectation value and uncertainty we have seen that the physically observable properties of a quantum system are represented by hermitean operators also referred to as observables such that the eigenvalues of the operator represents all the possible results that could be obtained if the associated physical. This gives rise to the concept of subjective probability as a general technique to model uncertainty. But we need to be able to evaluate how likely it is that f is true.
Deep learning is not good enough, we need bayesian deep. This is a large and active subfield of ai research. Pdf uncertainty in artificial intelligence researchgate. Probability bayes theory certainty factors fuzzy logic demonstrate the use of these techniques. If youre looking for a free download links of artificial intelligence with uncertainty pdf, epub, docx and torrent then this site is not for you. Healthcare is a dynamic industry with significant opportunity, but cost concerns, uncertainty, and complexity can also make it an unnerving one. Uncertainty and risk the term technological singularity reflects the idea that such change may happen suddenly, and that it is difficult to predict how the resulting new world. Slides are mostly adapted from aima and mit open courseware. Reasoning about uncertainty is a very valuable synthesis of the mathematics of uncertainty as it has developed in a number of related fieldsprobability, statistics, computer science, game theory, artificial intelligence, and philosophy. Recognition and resolution of comprehension uncertainty. Its a little bit like the view we took of probability. Probability, expectation value and uncertainty we have seen that the physically observable properties of a quantum system are represented by hermitean operators also referred to as observables such that the eigenvalues of the operator represents all the possible results that could be. These results show that when we train on less data, or test on data which is significantly different from the training set, then our epistemic uncertainty increases drastically.
Uncertainty refers to epistemic situations involving imperfect or unknown information. Hojjat ghaderi, university of toronto, fall 2006 8 decision making under uncertainty suppose actions dont have deterministic outcomes e. Hojjat ghaderi, university of toronto, fall 2006 1 csc384. How to deal with uncertainty is a subject of much controversy in artificial intelligence. Uncertainty in deep learning phd thesis yarin gal blog. In deep learning, there is no obvious way of obtaining uncertainty estimates. The association for uncertainty in artificial intelligence is a nonprofit. Intro to artificial intelligence decision making under uncertainty. The workshop featured significant developments in application of theories of representation and reasoning. With fol a fact f is only useful if it is known to be true or false. Decision making under uncertainty mit opencourseware. In it i organised the already published results on how to obtain uncertainty in deep learning, and collected lots of bits and pieces of new research i had lying around which i hadnt had the time to publish yet.
Research has found that people vary in their ability to tolerate uncertainty. As with all theoretical models, the expected utility model is not without its limitations. To help reduce the conflict between these two approaches, this paper reexamines expected utility theory from which bayesian probability theory is derived. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.
In the first part of this thesis we develop the theory for such tools, providing applications and illustrative examples. The aim of these notes is to introduce intelligent agents and reasoning, heuristic search techniques, game playing, knowledge. With uncertainty, an agent typically cannot guarantee to satisfy its goals, and even trying to maximize the probability of achieving a goal may not be sensible uncertainty and risk the term technological singularity reflects the idea that such change may happen suddenly, and that it is difficult to predict how the resulting new world would operate. Ex is the mean expected value operator the mean outcomeoutcome of uncertain investment c is of uncertain investment c is ec in example, this was. While we often rely on models of certain information as youve seen in the class so far, many economic problems require that we tackle uncertainty head on. In a reasoning system, there are several types of uncertainty. Idealizations of uncertainty, and lessons from artificial intelligence. This paper provides an introduction to the field of reasoning with uncertainty in artificial intelligence ai, with an emphasis on reasoning with numeric uncertainty. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence ai and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. A 90 will get me there on time if theres no accident.
Researchers in all of these fields will find this a very useful bookboth for its elegant treatment of. Pdf idealizations of uncertainty, and lessons from. Though there are various types of uncertainty in various aspects of a reasoning system, the reasoning with uncertainty or reasoning under uncertainty research in ai has been focused on the uncertainty of truth value, that is, to allow and process truth values other than true and false. Abstract deeplearninghasattractedtremendousattentionfromresearchersinvariousfieldsof informationengineeringsuchasai,computervision,andlanguageprocessingkalch. Uncertainty to act rationally under uncertainty we must be able to evaluate how likely certain things are. The conference on uncertainty in artificial intelligence uai is the premier international conference on research. Shafers theory of belief and the bayesian theory of probability are two alternative and mutually inconsistent approaches toward modelling uncertainty in artificial intelligence. Page 4 one person finds interesting another may not, but for the sake of space we shall not explore this additional factor. We use probability to describe the world and existing uncertainty agents will havebeliefsbased on their current state of knowledge e. Certainty equivalent more generally, consider situation in which have uncertainty with respect to consequence c nonlinear preference function f note. Recognition and resolution of comprehension uncertainty in ai 247 and past decision states gleaned from different sources is a setvalued rather than point valued feature sicilia, 2006. Uncertainty in action outcomes turn key, car doesnt start, etc. May 23, 2017 bayesian deep learning, computer vision, uncertainty. Decision making under uncertainty and reinforcement learning.
The fourth uncertainty in artificial intelligence workshop was held 1921 august 1988. A calculus for decisionmaking under uncertainty decision theory is a calculus for decisionmaking under uncertainty. Artificial intelligence ai is the discipline of computer perception, reasoning, and action. Recognition and resolution of comprehension uncertainty in ai. This book develops a framework that shows how uncertainty in ai expands and generalizes traditional ai. The uncertainty that remains after the best possible analysis has been undertaken is what we call residual uncertaintyfor example, the outcome of an ongoing regulatory debate or the performance attributes of a technology still in development. With uncertainty, an agent typically cannot guarantee to satisfy its goals, and even trying to maximize the probability of achieving a goal may not be sensible. The considered formalisms are probability theory and some of its generalizations, the certainty factor model, dempstershafer theory, and probabilistic networks. Artificial intelligence an overview sciencedirect topics.
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