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Advances in Modern Artificial Intelligence

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Recent Advances in Knowledge-based Paradigms and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 234))

Abstract

This chapter presents a brief overview of advances in modern artificial intelligence. It recognises that society has embraced Artificial Intelligence (AI), even if it is embedded within many of the consumer products being marketed. The reality is that society is already in the throws of digitizing its past and continues progressively moves on-line. The volume and breadth of data being processed is becoming unfathomable. This digital future heralds the dawn of virtual communities, operating a Web of Things (WoT) full of connected devices, many fitted with wireless connectivity 24/7. This pervasiveness increases the demand on researchers to provide more intelligent tools, capable of assisting humans in prosecuting this information, seamlessly, efficiently and immediately. Ultimately AI techniques have been evolving since the 1950s. This evolution began with Good Old-Fashioned Artificial Intelligence (GOFAI) using explicitly coded knowledge, heuristics and axiomatization. This digital analogy of biological systems initially failed to realise its potential, at least until the birth of personal computers. This introduced a paradigm shift towards the Fuzzy/Neural era, which furnished society with computer vision, character recognition and Evolutionary Computing (EC) (among other successes). The value engineering proponents continued to invest in automation, which spurred the growth of Machine Intelligence (MI) research, further increasing expectations for computers to do more with less human interaction. McCarthy recently agreed that it is now more appropriate to reliable AI research as Computational Intelligence (CI), because primitive methodologies have matured and science continues to witness more hybrid solutions. It is true that modern AI techniques typically employ multiple techniques and many now form hybrid systems with flexible problem solving capabilities or increased autonomy. This book contains a series of topics aimed at illustrating advances in modern AI. This book provides discussion on a number of recent innovations that include: classifiers, neural networks, fuzzy logic, Multi-Agent Systems (MASs) and several example applications.

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Notes

  1. 1.

    McCarthy also recently stated it would be more appropriate to use the term CI.

  2. 2.

    AI is commonly being maligned based on expectation promoted through science-fiction movies.

  3. 3.

    In Australian terms this equates to only 0.5% of its total workforce.

  4. 4.

    See www.worldrobotics.org.

  5. 5.

    This is a standard to facilitiate the development of code for both the CPU and GPUs with an emphasis on achieving accelerated computation throughput for portable processing.

  6. 6.

    See http://www.alliedfiresafety.com/page/alarms/.

  7. 7.

    See also Tsukamoto from 1979 and later ‘TSK’ from 1985.

  8. 8.

    Example variables include: X\( _i \), Y\( _i \), \( Z_i \) and X\( _p \), Y\( _p \), Z\( _p \).

  9. 9.

    Examples include a number of simple well defined behaviors; such as: avoidance, reach, follow, align, jump, turn and pass.

  10. 10.

    Agent Oriented Systems—subsumed by CAE International.

  11. 11.

    See http://www.fipa.org/.

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Tweedale, J., Jain, L. (2014). Advances in Modern Artificial Intelligence. In: Tweedale, J., Jain, L. (eds) Recent Advances in Knowledge-based Paradigms and Applications. Advances in Intelligent Systems and Computing, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-319-01649-8_1

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