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 builds 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.
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