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A Tale of Two Animats: What Does It Take to Have Goals?

Part of the The Frontiers Collection book series (FRONTCOLL)

Abstract

What does it take for a system, biological or not, to have goals? Here, this question is approached in the context of in silico artificial evolution. By examining the informational and causal properties of artificial organisms (“animats”) controlled by small, adaptive neural networks (Markov Brains), this essay discusses necessary requirements for intrinsic information, autonomy, and meaning.

Keywords

  • Intrinsic Information
  • Artificial Organisms
  • Adaptive Neural Network
  • Cause-effect Structure
  • Intrinsic Perspective

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 2.1

Adapted from [7] with permission

Fig. 2.2

Adapted from [5] with permission

Notes

  1. 1.

    Furthermore, representations of individual environmental features are typically distributed across many elements [6], and thus do no coincide with the Markov Brain’s elementary (micro) logic components.

  2. 2.

    Note that this holds, even if we could evaluate the correlation between internal and external variables in an observer-independent manner, except then the correlations might not even be meaningful for the investigator.

  3. 3.

    If M would not constrain its inputs, its state would just be a source of noise entering the system, not causal information.

  4. 4.

    Sets of elements can constrain their joint inputs and outputs in a way that is irreducible to the constraints of their constituent elements taken individually [13]. The irreducible cause-effect information of a set of elements can be quantified similarly to Eqs. 2.22.3, by partitioning the set and measuring the distance between \(p\left( {\left. {z_{t \pm 1} } \right|m_{t} } \right)\) and the distributions of the partitioned set.

  5. 5.

    By contrast to the uniform, perturbed distribution, the stationary, observed distribution of system Z entails correlations due to the system’s network structure which may occlude or exaggerate the causal constraints of the mechanism itself.

  6. 6.

    Take a neuron that activates, for example, every time a picture of the actress Jennifer Aniston is shown [22]. All it receives as inputs is quasi-binary electrical signals from other neurons. The meaning “Jennifer Aniston” is not in the message to this neuron, or any other neuron.

  7. 7.

    For example, an AND logic gate receiving 2 inputs is what it is, because it switches ON if and only if both inputs were ON. An AND gate in state ON thus constrains the past states of its input to be ON.

  8. 8.

    This notion of causal autonomy applies to deterministic and probabilistic systems, to the extent that their elements constrain each other, above other background inputs, e.g. from the sensors.

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Acknowledgements

I thank Giulio Tononi for his continuing support and comments on this essay, and William Marshall, Graham Findlay, and Gabriel Heck for reading this essay and providing helpful comments. L.A. receives funding from the Templeton World Charities Foundation (Grant#TWCF0196).

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Correspondence to Larissa Albantakis .

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Albantakis, L. (2018). A Tale of Two Animats: What Does It Take to Have Goals?. In: Aguirre, A., Foster, B., Merali, Z. (eds) Wandering Towards a Goal. The Frontiers Collection. Springer, Cham. https://doi.org/10.1007/978-3-319-75726-1_2

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