ASIMOV* 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.
ASIMOV has been developed further with the addition of the Feature Association Matrix (FAM) to enable the formation of simple episodic memory. This allows the ASIMOV agent to create spatial maps of its environment to use to maximize the rewards that it obtains in foraging. The FAM is an abstraction of physiological circuits implicated in episodic memory, such as the auto- and hetero-associative circuits of the hippocampus, and it shows how simple episodic memory can emerge from some of the simplest associative learning rules for classical conditioning.
*ASIMOV – short for “Algorithm of Selectivity by Incentive and Motivation for Optimized Valuation"
For more details please see our website and publication:
Gribkova, E. D., Chowdhary, G., & Gillette, R. (2024). Cognitive mapping and episodic memory emerge from simple associative learning rules. Neurocomputing, 595, 127812. https://doi.org/10.1038/s41598-020-66465-0.
To run ASIMOV and inspect the code, the multi-agent modeling program NetLogo must be acquired from https://ccl.northwestern.edu/netlogo/.