Itult

Itult is a theoretical framework in linguistics and artificial intelligence that describes how interlocutors converge on shared meanings through iterative updates to a common representation during conversation. It posits that successful communication emerges when participants optimize a joint utility function that balances accuracy, brevity, and cooperative intent.

The term is used as a shorthand for the iterative utilization and linguistic tuning process; it is applied to analyze both human-human and human-agent dialogue.

Core ideas include a dynamic, shared mental model; iterative negotiation of meaning through discourse acts; utility-weighted selection of utterances; Bayesian-style updates to beliefs about intended meanings; and feedback signaling to correct misalignments.

In practice, itult has been used to study task-oriented dialogue, design and evaluation of conversational agents, and assessment of alignment between agent behavior and user goals.

Critics note that the framework relies on assumptions of rationality and complete information, can be computationally demanding, and may underrepresent nonverbal cues, cultural differences, or power dynamics in conversation.

The framework emerged in the early 21st century as part of broader efforts to model pragmatic language use and cooperative communication, and it has since been integrated into experimental studies and the development of adaptive dialogue systems.

Related topics include pragmatics, information theory in linguistics, dialogue systems, and negotiation theory.