Robust planning with incomplete domain models

Synthesizing Robust Plans under Incomplete Domain Models

Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the d...

Refining Incomplete Planning Domain Models Through Plan Traces

Most existing work on learning planning models assumes that the entire model needs to be learned from scratch. A more realistic situation is that the planning agent has an incomplete model which it needs to refine through learning. In this paper we propose and evaluate a method for doing this. Our method takes as input an incomplete model (with missing preconditions and effects in the actions),...

Towards Model-lite Planning: A Proposal For Learning & Planning with Incomplete Domain Models

Due to the difficulty of defining a complete domain definition, it is hard to apply planners to real-life application domains. Model-lite planning research has been proposed to address this issue (Kambhampati 2007). While it takes time to develop a complete domain definition, it is rather faster and easier to provide a planner with incomplete but close to complete domain definition. In this pap...

Case-Based Task Decomposition with Incomplete Planning Domain Descriptions

Planning with Incomplete Information in the UNIX Domain

l%eal-world software environments, such as UNIX, have a number of attractive features as testbeds for planning research. First, it is relatively easy to develop "embodied agents," which interact directly with a software environment by issuing commands and interpreting the environment’s feedback [6]. Such agents facilitate testing planners experimentally and integrating planning with execution a...