Problem: Classical AI planning systems lack the flexible, affect-modulated decision-making that allows agents to prioritize and adapt under uncertainty.
What we built: Developed an affective agent architecture that uses emotion-inspired variables to modulate goal selection and planning behavior in multi-agent systems. Research was conducted in partnership with DePaul University.
Outcome: Presented at the Seventh Conference on Artificial General Intelligence (AGI-14). Published in Springer Lecture Notes in Computer Science.
Tags: AGI · Affective Computing · Multi-Agent Systems · Planning · Published Research