1.10 References and Further Reading

The ideas in this chapter have been derived from many sources. Here, we try to acknowledge those that are explicitly attributable to particular authors. Most of the other ideas are part of AI folklore; trying to attribute them to anyone would be impossible.

Levesque [2012] provides an accessible account of how thinking can be seen in terms of computation. Haugeland [1997] contains a good collection of articles on the philosophy behind artificial intelligence, including that classic paper of Turing [1950] that proposes the Turing test. Grosz [2012] and Cohen [2005] discuss the Turing test from a more recent perspective. Winograd schemas are described by Levesque [2014]. Srivastava et al. [2022] provide a Beyond the Imitation Game benchmark (BIG-bench) consisting of 204 tasks designed to challenge modern learning systems. Grosz [2018] discusses research on what it takes to implement dialog, not just answering one-off questions. Zador et al. [2023] discuss an embodied Turing test, and the role of neuroscience in AI.

Nilsson [2010] and Buchanan [2005] provide accessible histories of AI. Chrisley and Begeer [2000] present many classic papers on AI. Jordan [2019] and the associated commentaries discuss intelligence augmentation.

For discussions on the foundations of AI and the breadth of research in AI, see Kirsh [1991a], Bobrow [1993], and the papers in the corresponding volumes, as well as Schank [1990] and Simon [1995]. The importance of knowledge in AI is discussed in Lenat and Feigenbaum [1991], Sowa [2000], Darwiche [2018], and Brachman and Levesque [2022b].

The physical symbol system hypothesis was posited by Newell and Simon [1976]. Simon [1996] discusses the role of symbol systems in a multidisciplinary context. The distinctions between real, synthetic, and artificial intelligence are discussed by Haugeland [1985], who also provides useful introductory material on interpreted, automatic formal symbol systems and the Church–Turing thesis. Brooks [1990] and Winograd [1990] critique the symbol system hypothesis. Nilsson [2007] evaluates the hypothesis in terms of such criticisms. Shoham [2016] and Marcus and Davis [2019] argue for the importance of symbolic knowledge representation in modern applications.

The use of anytime algorithms is due to Horvitz [1989] and Boddy and Dean [1994]. See Dean and Wellman [1991], Zilberstein [1996], and Russell [1997] for introductions to bounded rationality.

For overviews of cognitive science and the role that AI and other disciplines play in that field, see Gardner [1985], Posner [1989], and Stillings et al. [1987].

Conati et al. [2002] describe a tutoring agent for elementary physics. du Boulay et al. [2023] overview modern tutoring agents. Wellman [2011] overviews research in trading agents. Sandholm [2007] describes how AI can be used for procurement of multiple goods with complex preferences.

A number of AI texts are valuable as reference books complementary to this book, providing a different perspective on AI. In particular, Russell and Norvig [2020] give a more encyclopedic overview of AI. They provide an excellent complementary source for many of the topics covered in this book and also an outstanding review of the scientific literature, which we do not try to duplicate.

The Association for the Advancement of Artificial Intelligence (AAAI) provides introductory material and news at their AI Topics website (https://aitopics.org/). AI Magazine, published by AAAI, often has excellent overview articles and descriptions of particular applications. IEEE Intelligent Systems also provides accessible articles on AI research.

There are many journals that provide in-depth research contributions and conferences where the most up-to-date research is found. These include the journals Artificial Intelligence, the Journal of Artificial Intelligence Research, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Computational Intelligence, as well as more specialized journals. Much of the cutting-edge research is published first in conferences. Those of most interest to a general audience are the International Joint Conference on Artificial Intelligence (IJCAI), the AAAI Annual Conference, the European Conference on AI (ECAI), the Pacific Rim International Conference on AI (PRICAI), various national conferences, and many specialized conferences, which are referred to in the relevant chapters.