Planning with Inaccurate Temporal Rules

Abstract

We use a temporal pattern model called Temporal Interval Tree Associative Rules (Tita rules). This pattern model has been introduced in a previous work. The model can express uncertainty, temporal inaccuracy, the usual time point operators, synchronicity, incomplete orders, chaining, disjunctive time constraints and temporal negation. This pattern model is initially designed to be used for temporal learning. In this paper, we use Tita rules as world description models for a Planning and Scheduling task. We present an efficient temporal planning algorithm able to deal with uncertainty, temporal inaccuracy, discontinuous (or disjunctive) time constraints and predictable but imprecisely time located exogenous events. We evaluate our technique by joining a learning algorithm and our planning algorithm into a simple reactive cognitive architecture that we apply on with virtual robot.