ASIMOV (short for “Algorithm of Selectivity by Incentive and Motivation for Optimized Valuation”) is an agent-based simulation of decision-making processes. It provides a fundamental framework for developing artificial animal behavior and cognition through step-wise modifications, where pre-existing circuitry is plausibly modified for changing function and tested, as in natural evolutionary exaptation. ASIMOV contains a cognitive architecture that is based on the behaviors and neuronal circuitry of the simple predatory sea slug Pleurobranchaea californica, and has since been expanded to include more complex behaviors, such as simple aesthetics and addiction dynamics (Gribkova et al., 2020), episodic memory, and spatial navigation. Code for ASIMOV is available at: https://github.com/Entience/ASIMOV.
Origin in Cyberslug
ASIMOV was built on an earlier simulation, Cyberslug (Brown et al., 2018), itself based on neuronal circuitry of cost-benefit decision of the predatory sea-slug Pleurobranchaea californica. In Cyberslug, a forager affectively integrates sensation, motivation (hunger), and learning to make cost-benefit decisions for approach or avoidance of prey.
Reward Experience in ASIMOV
ASIMOV builds on this with the explicit implementation of reward experience, pain, and homeostatic plasticity to provide realistic valuation of stimuli, and demonstrates how the nature and course of the addiction process emerges as an extreme expression of aesthetic preference. Here is a marvelous illustration of ASIMOV by Rebecca Purchase with a summary by Jared Adelman (Science in Pictures). Be sure to check out and support their amazing work!
A demonstration of ASIMOV’s agent with FAM learning spatial maps:
Brown JW, Caetano-Anollés D, Catanho M, Gribkova E, Ryckman N, Tian K, Voloshin M, Gillette R (2018) Implementing goal-directed foraging decisions of a simpler nervous system in simulation. eNeuro, 5(1). DOI: https://doi.org/10.1523/ENEURO.0400-17.2018.
Gribkova ED, Catanho M, Gillette R (2020) Simple aesthetic sense and addiction emerge in neural relations of cost-benefit decision in foraging. Scientific Reports, 10(1), 11-1. DOI: https://doi.org/10.1038/s41598-020-66465-0