Towards an architectural framework for intelligent virtual agents using probabilistic programming

TL;DR

KorraAI is a probabilistic programming framework for creating embodied conversational agents that can adapt, show proactive behavior, and model internal states for more natural interactions.

Contribution

The paper introduces KorraAI, a novel framework leveraging probabilistic programming to model ECAs' behavior, internal states, and interaction dynamics, enabling more natural and adaptable agents.

Findings

KorraAI allows ECAs to adapt to user preferences.

Agents can exhibit proactive and context-aware behaviors.

Models can evolve over time without user interaction.

Abstract

We present a new framework called KorraAI for conceiving and building embodied conversational agents (ECAs). Our framework models ECAs' behavior considering contextual information, for example, about environment and interaction time, and uncertain information provided by the human interaction partner. Moreover, agents built with KorraAI can show proactive behavior, as they can initiate interactions with human partners. For these purposes, KorraAI exploits probabilistic programming. Probabilistic models in KorraAI are used to model its behavior and interactions with the user. They enable adaptation to the user's preferences and a certain degree of indeterminism in the ECAs to achieve more natural behavior. Human-like internal states, such as moods, preferences, and emotions (e.g., surprise), can be modeled in KorraAI with distributions and Bayesian networks. These models can evolve over time, even without interaction with the user. ECA models are implemented as plugins and share a common interface. This enables ECA designers to focus more on the character they are modeling and less on the technical details, as well as to store and exchange ECA models. Several applications of KorraAI ECAs are possible, such as virtual sales agents, customer service agents, virtual companions, entertainers, or tutors.