Fuzzy Logic


Convergent Actor-Critic Algorithms Under Off-Policy Training and Function Approximation

arXiv.org Artificial Intelligence

We present the first class of policy-gradient algorithms that work with both state-value and policy function-approximation, and are guaranteed to converge under off-policy training. Our solution targets problems in reinforcement learning where the action representation adds to the-curse-of-dimensionality; that is, with continuous or large action sets, thus making it infeasible to estimate state-action value functions (Q functions). Using state-value functions helps to lift the curse and as a result naturally turn our policy-gradient solution into classical Actor-Critic architecture whose Actor uses state-value function for the update. Our algorithms, Gradient Actor-Critic and Emphatic Actor-Critic, are derived based on the exact gradient of averaged state-value function objective and thus are guaranteed to converge to its optimal solution, while maintaining all the desirable properties of classical Actor-Critic methods with no additional hyper-parameters. To our knowledge, this is the first time that convergent off-policy learning methods have been extended to classical Actor-Critic methods with function approximation.


The First International Workshop on Rough Sets

AI Magazine

The First International Workshop on Rough Sets: State of the Art and Perspectives was held on 2-4 September 1992 in Kiekrz, Poland. To stimulate the discussion, the participation was limited to 40 researchers who are involved in fundamental research in rough set theory and its extensions, logic for approximate reasoning, machine learning, knowledge representation and transfer, and applications of rough set methodology. The workshop focused primarily on applications of the basic idea of the approximate definition of a set and its consequences in other areas of science and engineering. Applications discussed at the workshop included machine learning, medical diagnosis, fault detection, medical image processing, neural net training, database organization, drug research, and digital circuit design. The workshop was the first international meeting of researchers working in this relatively new area. The approximate definition of a set in terms of lower and upper bounds, as introduced in the ...


A Fuzzy logic Production System Language ancl Shell

AI Magazine

In fact, we have a knowledge infrastructure already, and it is already immense. AI Mugaztine 7(l): 34- served the most successful work on expert systems: that (today) knowledge comes (mostly) from people. Editor: Mark Stefik Xerox PARC 3333 Coyote Hill Road Palo Alto, California 94304 Workshop on the Foundations of Al: An On-The-Spot Report The NSF and AAAI sponsored Workshop on the Foundations of AI (6-8 February 1986, Las Cruces, New Mexico) is over and, from my perspective at least, it was a very worthwhile event. I am preparing a report that I will send to you in due course. In addition, I noticed that John McCarthy was snapping freely with his camera at the workshop.


Intelligent Multiobjective Optimization of Distribution System Operations

AI Magazine

A hybrid fuzzy knowledge-based system with crisp and fuzzy rules as well as numerical methods was developed for multiobjective optimization of power distribution system operation. The development process and knowledge-acquisition process for the fuzzy knowledge-based system are described in detail. Fuzzy sets are defined for recent temperature trend, line section loading, transformer aging, voltage-level guidelines, and the degree of desirability of a proposed switching combination. After a heuristic preprocessor proposes a list of switch openings that would seem to reduce system losses, network radiality rules consider whether to open a particular switch and find a corresponding switch that can be closed to maintain radiality. Network parameter rules determine whether the proposed switching combination will violate network integrity.


Applied Al News

AI Magazine

Foremost Manufacturing Inc. (Union, NJ), a manufacturer of reflectors for lighting fixtures, has adopted a fuzzy logic-based application to produce quotations for customers in less time. The company is using a fuzzy system to produce bids in about 1.5 minutes, compared to an industry average of two weeks. Carnegie Group Inc. (Pittsburgh, PA) has developed a hybrid neural network/expert system for diagnostic situations where signal data and symbolic data must be combined to perform a definitive diagnosis and repair procedure. This technology was developed with funding from the National Science Foundation. Working with experts from Armco Steel (Middletown, OH), Carnegie Group developed a prototype system to diagnose chatter in a coldrolling mill.


A New Direction in AI

AI Magazine

Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familiar examples are parking a car, driving in city traffic, playing golf, cooking a meal, and summarizing a story. In performing such tasks, humans use perceptions of time, direction, speed, shape, possibility, likelihood, truth, and other attributes of physical and mental objects. Reflecting the bounded ability of the human brain to resolve detail, perceptions are intrinsically imprecise. In more concrete terms, perceptions are f-granular, meaning that (1) the boundaries of perceived classes are unsharp and (2) the values of attributes are granulated, with a granule being a clump of values (points, objects) drawn together by indistinguishability, similarity, proximity, and function.


A novel improved fuzzy support vector machine based stock price trend forecast model

arXiv.org Machine Learning

Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has become a hot research topic in the field of machine learning, and it has been successfully applied in many fields. However, as a new technology, there are many limitations to support vector machines. There is a large amount of fuzzy information in the objective world. If the training of support vector machine contains noise and fuzzy information, the performance of the support vector machine will become very weak and powerless. As the complexity of many factors influence the stock price prediction, the prediction results of traditional support vector machine cannot meet people with precision, this study improved the traditional support vector machine fuzzy prediction algorithm is proposed to improve the new model precision. NASDAQ Stock Market, Standard & Poor's (S&P) Stock market are considered. Novel advanced- fuzzy support vector machine (NA-FSVM) is the proposed methodology.


Tutorial: Introduction to Reinforcement Learning with Function Approximation

#artificialintelligence

Reinforcement learning is a body of theory and techniques for optimal sequential decision making developed in the last thirty years primarily within the machine learning and operations research communities, and which has separately become important in psychology and neuroscience. This tutorial will develop an intuitive understanding of the underlying formal problem (Markov decision processes) and its core solution methods, including dynamic programming, Monte Carlo methods, and temporal-difference learning. It will focus on how these methods have been combined with parametric function approximation, including deep learning, to find good approximate solutions to problems that are otherwise too large to be addressed at all.


Finite Sample Analyses for TD(0) with Function Approximation

arXiv.org Artificial Intelligence

TD(0) is one of the most commonly used algorithms in reinforcement learning. Despite this, there is no existing finite sample analysis for TD(0) with function approximation, even for the linear case. Our work is the first to provide such results. Existing convergence rates for Temporal Difference (TD) methods apply only to somewhat modified versions, e.g., projected variants or ones where stepsizes depend on unknown problem parameters. Our analyses obviate these artificial alterations by exploiting strong properties of TD(0). We provide convergence rates both in expectation and with high-probability. The two are obtained via different approaches that use relatively unknown, recently developed stochastic approximation techniques.


A primer on universal function approximation with deep learning (in Torch and R)

@machinelearnbot

Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts.