Safe LTL Assumption-Based Planning

Planning for partially observable, nondeterministic domains is a very signi cant and computationally hard problem. Often, reasonable assumptions can be drawn over expected/nominal dynamics of the domain; using them to constrain the search may lead to dramatically improve the ef ciency in plan generation. In turn, the execution of assumption-based plans must be monitored to prevent runtime failures that may happen if assumptions turn out to be untrue, and to replan in that case. In this paper, we use an expressive temporal logic, LTL, to describe assumptions, and we provide two main contributions. First, we describe an effective, symbolic forward-chaining mechanism to build (conditional) assumption-based plans for partially observable, nondeterministic domains. Second, we constrain the algorithm to generate safe plans, i.e. plans guaranteeing that, during their execution, the monitor will be able to univocally distinguish whether the domain behavior is one of those planned for or not. ...

• Type: Conference
• Year: 2006
• Where: AIPS
• Authors: Alexandre Albore, Piergiorgio Bertoli
• Keywords: AIPS 2006, Artificial Intelligence, Assumption-based Plans, Nondeterministic Domains, Plan Generation