The last seven years have seen the field of artificial intelligence (AI) transformed. This transformation is not simple, nor has it yet run its course. The transformation has been generated by the emergence of expert systems. Whatever exactly these are or turn out to be, they first arose during the 1970s, with a triple claim: to be AI systems that used large bodies of heuristic knowledge, to be AI systems that could be applied, and to be the wave of the future. The exact status of these claims (or even whether my statement of them is anywhere close to the mark) is not important. The thrust of these systems was strong enough and the surface evidence impressive enough to initiate the transformation. This transformation has at least two components.

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It is no pure coincidence that an organic chemist rather than a computer specialist is writing this foreword.

The nature of the business doesn't matter; in every business computers have made numerous changes in record keeping, process control, and decision-making. And there will be more. One of the most important trends in computing is making computers behave intelligently. The software underneath this intelligent behavior is called an expert system, sometimes also called a knowledgebased system, or knowledge system. An expert system is a computer program that reasons about a problem in much the same way, and with about the same performance, as specialists. This chapter is about the trend toward using expert systems: what it means, how it's possible, and how to think about it. There have been lead articles about this in Fortune, Business Week, and Newsweek; most Fortune-SOO companies are using expert systems; many are establishing research and development groups for them; even staid IBM is marketing expert systems tools and using them internally. Bruce G. Buchanan I 129 There are many reasons why companies want to build an expert system. Most of them are based on the premise that: Expertise is a scarce resource. And the corollary (by Murphy's Law): Even when there is enough expertise, it is never close enough to the person who needs it in a hurry. Because this is true, almost by definition, an expert system containing some of the knowledge of a company's specialists may have several benefits.. There are several examples of expert systems working in various problem areas. At present, they are used more as "intelligent assistants" than as replacements for technicians or experts. That is, they help people think through difficult problems and may provide suggestions about what to do, without taking over every aspect of the task. Although the problems are quite different they can be categorized into two major classes problems of interpretation and problems of construction. Interpretive problem examples include Schlumberger's Dipmeter Advisor, which replicates the expertise of some of their company-wide specialists who interpret data from clients' oil wells and then sell the expert system's interpretations around the world.

Given a data sample with its feature vector, SC tries to learn a codebook with some codeworks, and approximate the data sample as the linear combination of the codewords. SC assume that only a few codewords in the codebook are enough to represent the data sample, thus the combination coefficients should be sparse, i.e. most of the coefficients are zeros, leaving only a few of them non-zeros. The linear combination coefficients of the data sample could be its new representation. Because they are sparse, the coefficient vector is often referred to as the sparse code. To solve the sparse code, one usually minimizes the approximation error with regard to the codebook and the sparse code, and at the same time seeks the sparsity of the sparse code. Although SC has been used in many pattern recognition applications, such as palmprint recognition [24], dynamic texture recognition [25], human action recognition [26], [27], [28], speech recognition [29], digit recognition [30], image annotation [31], [32], [33], and face recognition [34], in most cases, SC is used as an unsupervised learning method. When SC is performed to the training data set, it is assumed that the class labels of the training samples are unavailable. Then after the sparse codes are learned, they will be used to learn a classifier. Thus the class labels are ignored during the sparse coding procedure.

This literature review covers AI techniques used for real-time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision-making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academia and industry, finding the academic research heavily focused on creating game-winning agents, while the indus- try aims to maximise player enjoyment. It finds the industry adoption of academic research is low because it is either in- applicable or too time-consuming and risky to implement in a new game, which highlights an area for potential investi- gation: bridging the gap between academia and industry. Fi- nally, the areas of spatial reasoning, multi-scale AI, and co- operation are found to require future work, and standardised evaluation methods are proposed to produce comparable re- sults between studies.

OWL 2 EL is a popular ontology language that supports role inclusions---that is, axioms that capture compositional properties of roles. Role inclusions closely correspond to context-free grammars, which was used to show that answering conjunctive queries (CQs) over OWL 2 EL knowledge bases with unrestricted role inclusions is undecidable. However, OWL 2 EL inherits from OWL 2 DL the syntactic regularity restriction on role inclusions, which ensures that role chains implying a particular role can be described using a finite automaton (FA). This is sufficient to ensure decidability of CQ answering; however, the FAs can be worst-case exponential in size so the known approaches do not provide a tight upper complexity bound. In this paper, we solve this open problem and show that answering CQs over OWL 2 EL knowledge bases is PSPACE-complete in combined complexity (i.e., the complexity measured in the total size of the input). To this end, we use a novel encoding of regular role inclusions using bounded-stack pushdown automata---that is, FAs extended with a stack of bounded size. Apart from theoretical interest, our encoding can be used in practical tableau algorithms to avoid the exponential blowup due to role inclusions. In addition, we sharpen the lower complexity bound and show that the problem is PSPACE-hard even if we consider only role inclusions as part of the input (i.e., the query and all other parts of the knowledge base are fixed). Finally, we turn our attention to navigational queries over OWL 2 EL knowledge bases, and we show that answering positive, converse-free conjunctive graph XPath queries is PSPACE-complete as well; this is interesting since allowing the converse operator in queries is known to make the problem EXPTIME-hard. Thus, in this paper we present several important contributions to the landscape of the complexity of answering expressive queries over description logic knowledge bases.

In this paper, we highlight the usage of AI in software development process for Robotic systems, in general and HRI systems, in particular. The software as well as the software development methodology and associated tools are knowledge-based systems. The key challenge is to represent domain knowledge that enables the process and model evolution to built complex software intensive HRI systems.

Can brain-mapping data be used to reverse engineer a brain Noam Chomsky discusses the evolution of the field of system in silico? This is actually the question of whether artificial intelligence from 1956, when John McCarthy consciousness is fully contained within the physical defined the science, until today (Ramsay, 2012). The goal structure that is the brain. Do the brain and its supporting of AI was to study intelligence by implementing its systems fully account for consciousness or are there other essential features using man-made technology. This goal components that transcend the body that are also at play? If has resulted in several practical applications people use metaphysical components play a role, then the answer is every day. The field has produced significant advances in negative, since mapping just the anatomical aspects of the search engines, data mining, speech recognition, image consciousness system would leave a critical component processing, and expert systems, to name a few.