Abstract for: Cognitive Artificial Intelligence: Building Human-Like Artificial Agents

One original goal of Artificial Intelligence (AI) was to create algorithms that would replicate human behavior. Today, AI has diverged, often aiming to excel in tasks inspired by human capabilities and outperform humans, rather than replicating human actions. In this talk, I will focus on Cognitive Science’s contribution to AI by developing computational algorithms that mimic human decision making. Specifically, I will delve into the realm of dynamic decision making, approaching the question from the point of a comprehensive cognitive algorithm rooted in Instance-Based Learning Theory (IBLT), a cognitive theory of decisions from experience in dynamic environments. I will summarize evidence of IBL’s human-like performance across tasks ranging from simple to complex in various domains and the critical research gaps for improving IBL’s cognitive fidelity in AI algorithms. Finally, I will also attempt to identify ways in which System Dynamics can play a role in the creation of system models of human cognitive processes, such as the one represented by IBLT and the potential applications that such System Dynamics model may have to study human dynamic decision making.