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Learning robots interacting with humans: from epistemic risk to responsibility

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Abstract

The import of computational learning theories and techniques on the ethics of human-robot interaction is explored in the context of recent developments of personal robotics. An epistemological reflection enables one to isolate a variety of background hypotheses that are needed to achieve successful learning from experience in autonomous personal robots. The conjectural character of these background hypotheses brings out theoretical and practical limitations in our ability to predict and control the behaviour of learning robots in their interactions with humans. Responsibility ascription problems, which concern damages caused by learning robot actions, are analyzed in the light of these epistemic limitations. Finally, a broad framework is outlined for ethically motivated scientific inquiries, which aim at improving our capability to understand, anticipate, and selectively cope with harmful errors by learning robots.

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Notes

  1. Failures of this safety policy in industrial environments are witnessed by a significant number of accidents involving robots in factories and plants. Useful information about robots and safety of human beings is provided in the 8 Jun 2006 issue of The Economist-Technology Quarterly. An electronic copy of the article is available on line at: http://www.economist.com/displaystory.cfm?story_id = 7001829.

  2. The overall rule governing the behaviour of an unsociable robot is relatively easy to state, but its actual implementation raises non-trivial theoretical and technological problems, which include the need for real-time reactivity and motion planning in high-dimensional configuration spaces. Furthermore, the reliability of the proposed solutions usually declines sharply when the environment becomes more and more cluttered, dense, and complex. A survey of effective methods and solutions to such problems can be found in Minguez and Montano (2004); Brock and Khatib (2002); and Kohout (2000).

  3. This sweeping claim is clearly stated and motivated in Cucker and Smale (2001). Mitchell (1997) (p. 42ff.) is also a valid source for a discussion of inductive biases needed by computational learning agents.

  4. Several proposals for relaxing some of these constraints have been advanced, but the modified learning algorithms are usually intractable for all but the smallest problems. Even though a few algorithms (see, e.g., Pineau and Gordon 2005) solved some such computational problems and demonstrated competitive performances in limited tasks, their use is still far from being widespread in robotics.

  5. Background conjectural assumptions about the environment play a crucial role here too, insofar as successful application of RL learning depends on the correctness of these assumptions about the environment. A detailed argument to this effect is provided, in connection with RL algorithms for adapting navigation control strategies in behaviour-based robotic architectures, in Datteri et al. (2006).

  6. The first influential work is due to Vapnik and Chervonenckis (1971), while a comprehensive overview of the resulting theory (known as VC theory or statistical learning theory) was provided by Vapnik (1999a, b).

  7. A solution for a class of learning problems is the specification of a learning algorithm that can be trained by means of a suitable set of training examples.

  8. From the standpoint of computational complexity theory this is usually taken to mean that the learning problem belongs to the class P.

  9. The connections between PAC models and (the theory of) empirical processes were first exploited by Blumer and colleagues (1989); thereafter, many efforts have been produced to achieve a better understanding of these connections (see, e.g., Vidyasagar 1996).

  10. Clearly, in this toy example, computational aspects are difficult to appreciate; however, these aspects are crucial in the real case. The more the parameters are the more expensive it is to explore the parameter space in order to find the “best” solution. Of course, a line can be represented by only two parameters while a curve requires (in general) more parameters.

  11. For discussion, see Tamburrini (2006); for an analysis of early cybernetic reflections on the use of learning machines, see Cordeschi and Tamburrini (2005).

  12. This error is referred to as false negative.

  13. More details about the effectiveness of ROC curves are found in Zweig and Campbell (1993), while some practical issues are more extensively discussed in Fawcett (2004).

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Acknowledgements

The financial support of the VI Framework Programme of the European Union (Science and Society, contract number 017759) is gratefully acknowledged.

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Authors and Affiliations

  1. Dipartimento di Scienze Fisiche, Università di Napoli Federico II, Complesso Universitario Monte S. Angelo, Via Cintia, 80126, Napoli, Italy

    Matteo Santoro & Guglielmo Tamburrini

  2. Istituto Statale di Istruzione Superiore “Francesco De Sanctis”, Napoli, Italy

    Dante Marino

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  1. Matteo Santoro
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  2. Dante Marino
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  3. Guglielmo Tamburrini
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Correspondence to Guglielmo Tamburrini.

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Santoro, M., Marino, D. & Tamburrini, G. Learning robots interacting with humans: from epistemic risk to responsibility. AI & Soc 22, 301–314 (2008). https://doi.org/10.1007/s00146-007-0155-9

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