Skip to main content
SpringerLink
Log in

Introduction to the Autonomous Artificial Intelligence Systems

  • Chapter
  • First Online:
Judgement-Proof Robots and Artificial Intelligence

  • 661 Accesses

Abstract

This chapter attempts to explain the main concepts, definitions, and developments of the field of artificial intelligence. It addresses the issues of logic, probability, perception, learning, and action. This chapter examines the current “state of the art” of the artificial intelligence systems and its recent developments. Moreover, this chapter presents the artificial intelligence’s conceptual foundations and discusses the issues of machine learning, uncertainty, reasoning, learning, and robotics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 69.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  • Armando, Alessandro, Roberto Carbone, Luca Compagna, Jorge Cuellar, and Llanos Tobarra. 2008. Formal Analysis of SAML 2.0 Web Browser Single Sign-on for Google Apps. FMSE ’08: Proceedings 6th ACM Workshop on Formal Methods in Security Engineering, 1–10.

    Google Scholar 

  • Athey, Susan. 2018. The Impact of Machine Learning on Economics. In The Economics of Artificial Intelligence: An Agenda, National Bureau of Economic Research.

    Google Scholar 

  • Bar-Shalom, Yaakov (ed.). 1992. Multitarget-Multisensor Tracking: Advanced Application. Miami: Artech House.

    Google Scholar 

  • Beal, Jacob, and Patrick H. Winston. 2009. Guest Editors’ Introduction: The New Frontier of Human-Level Artificial Intelligence. IEEE Intelligent Systems 24 (4): 21–23.

    Google Scholar 

  • Bekey, George. 2008. Robotics: State of the Art and Future Challenges. London: Imperial College Press.

    Google Scholar 

  • Bertsekas, P. Dimitri, and John N. Tsitsiklis. 2008. Introduction to Probability, 2nd ed. Cambridge: Athena Scientific.

    Google Scholar 

  • Bishop, Christopher. 1995. Neural Networks for Pattern Recognition. Oxford: Oxford University Press.

    Google Scholar 

  • Bishop, Christopher. 2007. Pattern Recognition and Machine Learning. New York: Springer.

    Google Scholar 

  • Blei, M. David, Y. Ng. Andrew, and Michael I. Jordan. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research 3: 993–1022.

    Google Scholar 

  • Bridle, S. John. 1990. Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition. In Neurocomputing: Algorithms, Architectures and Applications, ed. Soulie Fogelman and Jean Herault. New York: Springer.

    Google Scholar 

  • Bryson, E. Arthur, and Yu-Chi Ho. 1975. Applied Optimal Control, Optimization, Estimation, and Control. New York: Wiley.

    Google Scholar 

  • Buchanan, G. Bruce, Tom M. Mitchell, Reid G. Smith, and C.R. Johnson. 1978. Models of Learning Systems. In Encyclopedia of Computer Science and Technology, ed. J. Belzer, A.G. Holzman, and A. Kent, vol. 11. New York: Marcel Decker.

    Google Scholar 

  • Calo, Ryan. 2015. Robotics and the Lessons of Cyberlaw. California Law Review 103: 513–563.

    Google Scholar 

  • Calo, Ryan. 2016. Robots as Legal Metaphors. Harvard Journal of Law & Technology 30: 209–237.

    Google Scholar 

  • Calo, Ryan. 2017. Artificial Intelligence Policy: A Primer and Roadmap. UC Davis Law Review 51: 399–435.

    Google Scholar 

  • Cheeseman, Peter. 1985. In Defense of Probability. Proceedings of the International Joint Conference on Artificial Intelligence.

    Google Scholar 

  • Cowan, D. Jack, and David H. Sharp. 1988. Neural Nets. Quarterly Review of Biophysics 21: 365–427.

    Google Scholar 

  • Cross, E. Stephen, and Edward Walker. 1994. DART: Applying Knowledge Based Planning and Scheduling to Crisis Action Planning. In Intelligent Scheduling, ed. Monte Zweben and Mark S. Fox, 711–729. San Francisco: Morgan Kaufmann.

    Google Scholar 

  • de Donbal, F. Tom, David J. Leaper, Jane C. Horrocks, and John R. Staniland. 1974. Human and Computer-Aided Diagnosis of Abdominal Pain: Further Report with Emphasis on Performance of Clinicians. British Medical Journal 1 (2): 376–380.

    Google Scholar 

  • Dudek, Gregory, and Michael Jenkin. 2000. Computational Principles of Mobile Robotics. Cambridge: Cambridge University Press.

    Google Scholar 

  • Goertzel, Ben, and Cassio Pennachin. 2007. Artificial General Intelligence. New York: Springer.

    Google Scholar 

  • Goodman, Joshua, and David Heckerman. 2004. Fighting Spam with Statistics. Significance, the Magazine of the Royal Statistical Society 1: 69–72.

    Google Scholar 

  • Gopalan, Prem, Matthew J. Hoffman and D.M. Blei. 2015. Scalable Recommendation with Hierarchical Poisson Factorization. In UAI: 326–335.

    Google Scholar 

  • Gorry, G. Anthony, Jerome P. Kassirer, Alvin Essig, and William B. Schwartz. 1973. Decision Analysis as the Basis for Computer-Aided Management of Acute Renal Failure. American Journal of Medicine 55 (4): 473–484.

    Google Scholar 

  • Haugeland, John (ed.). 1985. Artificial Intelligence: The Very Idea. Cambridge: MIT Press.

    Google Scholar 

  • Hopfield, J. John. 1982. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS 79: 2554–2558.

    Google Scholar 

  • Horwitz, Eric, John S. Breese, and Max Henrion. 1988. Decision Theory in Expert Systems and Artificial Intelligence. International Journal of Approximate Reasoning 2 (3): 247–302.

    Google Scholar 

  • Iyenegar, Vinod. 2016. Why AI Consolidation Will Create the Worst Monopoly in U.S. History, TechCrunch.

    Google Scholar 

  • Jensen Verner, Finn. 2007. Bayesian Networks and Decision Graphs. New York: Springer.

    Google Scholar 

  • Jonsson, K. Ari, Paul H. Morris, Nicola Muscettola, Kanna Rajan, and Ben Smith. 2000. Planning in Interplanetary Space: Theory and Practice. AIPS-00: 177–186.

    Google Scholar 

  • Kephart, O. Jeffrey, and David M. Chess. 2003. The Vision of Automatic Computing. IEEE Computer 36 (1): 41–50.

    Google Scholar 

  • Kirk, E. Donald. 2004. Optimal Control Theory: An Introduction. London: Dover Books.

    Google Scholar 

  • Lucas, J. Peter, Linda C. van der Gaag, and Ameen Abu-Hanna. 2004. Bayesian Networks in Biomedicine and Healthcare. Artificial Intelligence in Medicine 30 (3): 201–214.

    Google Scholar 

  • Mason, T. Matthew. 2001. Mechanics of Robotic Manipulation. Cambridge: MIT Press.

    Google Scholar 

  • McCarthy, John. 2007. From Here to Human-Level AI. Artificial Intelligence 171 (18): 1174–1182.

    Google Scholar 

  • Metz, Cade. 2016. In a Huge Breakthrough, Google’s AI Beats a Top Player at the Game of Go. Wired.

    Google Scholar 

  • Minsky, Marvin. 1952. A Neural-Analogue Calculator Based upon a Probability Model of Reinforcement. Cambridge, MA: Harvard University Psychological Laboratories.

    Google Scholar 

  • Minsky, Marvin. 1969. Basic Mechanisms of the Epilepsies. New York: Little, Brown.

    Google Scholar 

  • Minsky, Marvin. 2007. The Emotion Machine: Commonsense Thinking, Artificial Intelligence and the Future of the Human Mind. New York: Simon & Schuster.

    Google Scholar 

  • Mitchell, M. Tom. 1997. Machine Learning. New York: McGraw-Hill.

    Google Scholar 

  • Mullainathan, Sendhil, and Jann Spiess. 2017. Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives 31 (2): 87–106.

    Google Scholar 

  • Murphy, R. Robin. 2000. Introduction to AI Robotics. Cambridge: MIT Press.

    Google Scholar 

  • Newell, Allen. 1994. Unified Theories of Cognition. Cambridge: Harvard University Press.

    Google Scholar 

  • Newell, Allen, and Herbert A. Simon. 1972. Human Problem Solving. New York: Prentice-Hall.

    Google Scholar 

  • Nilsson, J. Nils. 1998. Artificial Intelligence: A New Synthesis. San Francisco: Morgan Kaufman.

    Google Scholar 

  • Nilsson, J. Nils. 2009. The Quest for Artificial Intelligence: A History of Ideas and Achievement. Cambridge: Cambridge University Press.

    Google Scholar 

  • Nilsson, J. Nils. 2010. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge: Cambridge University Press.

    Google Scholar 

  • Nowick, M. Steven, Mark E. Dean, David Dill, and Mark Horowitz. 1993. The Design of a High-performance Cache Controller: A Case Study in Asynchronous Synthesis. Integration: The VLSI Journal 15 (3): 241–262.

    Google Scholar 

  • Oh, Songhwai, Stuart Russell, and Shankar Sastry. 2009. Markov Chain Monte Carlo Data Association for Multi-target Tracking. IEEE Transactions on Automatic Control 54 (3): 481–497.

    Google Scholar 

  • Pearl, Judea. 1988. Probabilistic Reasoning in Intelligent Systems. San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Pearson, Jordan. 2017. Uber’s AI Hub in Pittsburgh Gutted a University Lab—Now It’s in Toronto, Vice Motherboard. Available at https://www.vice.com/en_us/article/3dxkej/ubers-ai-hub-in-pittsburgh-gutted-a-university-lab-now-its-in-toronto.

  • Poole, David, Alan K. Mackworth, and Randy Goebel. 1998. Computational Intelligence: A Logical Approach. Oxford: Oxford University Press.

    Google Scholar 

  • Puterman, L. Martin. 1994. Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: Wiley.

    Google Scholar 

  • Rumelhart, E. David, and James L. McClelland. 1986. Parallel Distributed Processing, Volume 1 Explorations in the Microstructure of Cognition: Foundations. Cambridge: MIT Press.

    Google Scholar 

  • Russell, Stuart. 2019. Human Compatible: Artificial Intelligence and the Problem of Control. London: Allen Lane.

    Google Scholar 

  • Russell, Stuart, and Peter Norvig. 2016. Artificial Intelligence: A Modern Approach, 3rd ed. Harlow: Pearson.

    Google Scholar 

  • Stone, Peter, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee (Anno) Saxenian, Julie Shah, Milind Tambe, and Astro Teller. 2016. Artificial Intelligence and Life in 2030. Report of the 2015 study panel 50, Stanford University.

    Google Scholar 

  • Stone, Peter, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee (Anno) Saxenian, Julie Shah, Milind Tambe, and Astro Teller. 2018. Artificial Intelligence and Life in 2030. Report of the 2015 study panel 50, Stanford University.

    Google Scholar 

  • Surden, Harry. 2014. Machine Learning and Law. Washington Law Review 89 (1): 87–115.

    Google Scholar 

  • Tambe, Milind, Lewis W. Johnson, Randolph M. Jones, Frank Ross, John E. Laird, Paul S. Rosenbloom, and Karl Schwab. 1995. Intelligent Agents for Interactive Simulation Environments. AI Magazine, 16 (1).

    Google Scholar 

  • Turing, M. Alan. 1936. On Computable Numbers, with Application to the Entscheidungsproblem, or Decision Problem. Proceedings of the London Mathematical Society, 2nd ser., 42: 230–265.

    Google Scholar 

  • Turing, M. Alan. 1950. Computing Machinery and Intelligence. Mind, New Series 59 (236): 433–460.

    Google Scholar 

  • Vapnik, N. Vladimir. 1998. Statistical Learning Theory. New York: Wiley.

    Google Scholar 

  • Varian, R. Hall. 2014. Big Data: New Tricks for Econometrics. The Journal of Economic Perspectives 28 (3): 3–27.

    Google Scholar 

  • Winston, H. Patrick. 1992. Artificial Intelligence, 3rd ed. New York: Addison-Wesley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Economics and Business, University of Ljubljana, Ljubljana, Slovenia

    Mitja Kovač

Authors
  1. Mitja Kovač
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mitja Kovač .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kovač, M. (2020). Introduction to the Autonomous Artificial Intelligence Systems. In: Judgement-Proof Robots and Artificial Intelligence. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-53644-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-53644-2_4

  • Published:

  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-53643-5

  • Online ISBN: 978-3-030-53644-2

  • eBook Packages: Economics and FinanceEconomics and Finance (R0)

Publish with us

Policies and ethics

Search

Navigation