timeanswering

Timeanswering is the practice of producing time-oriented responses to questions or requests, including estimates of duration, deadlines, or timing forecasts. It arises in any setting where scheduling, planning, or coordination depends on how long tasks will take. Timeanswering can be explicit, for example "about 15 minutes," "ETA 2–3 days," or "delivery by tomorrow," and may also appear as implicit timing embedded in a response.

Techniques used in timeanswering include interval estimates, probabilistic forecasting, and calibration methods that align predicted times with observed durations. In human–computer interaction, timeanswering often involves communicating uncertainty clearly, using phrases that reflect ranges or confidence levels, and updating estimates as new information becomes available. Data sources include historical task durations, real-time system state, and expert judgment.

Applications span customer service chatbots providing ETAs, project management software estimating time-to-completion, logistics and delivery services, and personal assistants that schedule events or warn of delays. In AI systems, timeanswering may be generated by models trained on timing data and refined through user feedback to reduce overconfidence.

Evaluation of timeanswering performance uses metrics such as mean absolute error, root mean square error, and calibration measures, as well as user satisfaction and perceived reliability. Limitations include inherent uncertainty, variability across tasks, and the risk of misleading precision when communicating broad ranges.

See also: ETA, estimated time of arrival; time perception; forecasting; estimation theory; reliability calibration.