Temporal credit assignment is crucial for learning and skill development in natural and
artificial intelligence. While computational methods like the TD approach in reinforcement …

A major challenge for research in artificial intelligence is to develop systems that can infer
the goals, beliefs, and intentions of others (ie, systems that have theory of mind, ToM). In this …

Instance-based learning theory (IBLT) is a comprehensive account of how humans make
decisions from experience during dynamic tasks. Since it was first proposed almost two …

Developing effective multi-agent systems (MASs) is critical for many applications requiring
collaboration and coordination with humans. Despite the rapid advance of multi-agent deep …

There is a clear desire to model and comprehend human behavior. Trends in research
covering this topic show a clear assumption that many view human reasoning as the …

Deception, which includes leading cyber-attackers astray with false information, has shown
to be an effective method of thwarting cyber-attacks. There has been little investigation of the …

Computational cognitive modeling is a useful methodology to explore and validate theories
of human cognitive processes. Often cognitive models are used to simulate the process by …

Large Language Models (LLMs) have demonstrated their capabilities across various tasks,
from language translation to complex reasoning. Understanding and predicting human …

Many aspects of humans' dynamic decision-making (DDM) behaviors have been studied
with computer-simulated games called microworlds. However, most microworlds only …

As we discussed in Part I of this topic, there is a clear desire to model and comprehend
human behavior. Given the popular presupposition of human reasoning as the standard for …