Integrating Planning, Execution, and Learning to Improve Plan Execution

Abstract

Algorithms for planning under uncertainty require accurate action models that explicitly capture the uncertainty of the environment. Unfortunately, obtaining these models is usually complex. In environments with uncertainty, actions may produce countless outcomes and hence, specifying them and their probability is a hard task. As a consequence, when implementing agents with planning capabilities, practitioners frequently opt for architectures that interleave classical planning and execution monitoring following a replanning when failure paradigm. Though this approach is more practical, it may produce fragile plans that need continuous replanning episodes or even worse, that result in execution dead-ends. In this paper, we propose a new architecture to relieve these shortcomings. The architecture is based on the integration of a relational learning component and the traditional planning and execution monitoring components. The new component allows the architecture to learn probabilistic rules of the success of actions from the execution of plans and to automatically upgrade the planning model with these rules. The upgraded models can be used by any classical planner that handles metric functions or, alternatively, by any probabilistic planner. This architecture proposal is designed to integrate off-the-shelf interchangeable planning and learning components so it can profit from the last advances in both fields without modifying the architecture.

A Hybrid Approach to Process Planning: The Urban Traffic Controller Example

“…PELEA introduce adaptable modular design that integrates learning with planning and execution. It also incorporates sensing and monitoring for realtime re-planning (Jimnez et al, 2013). We propose the use of Model Predictive Control (MPC) design in continuous planning to create reasoning in controllers that can solve problems in domains which are modelled using variables whose values are changing continuously.…”

A Neural Network Approach to Find The Cumulative Failure Distribution: Modeling and Experimental Evidence

“…The evaluation of the reliability parameters based on the collected field data is a significant problem. In fact, the reliability prediction of a complex system is very important for various purposes, such as production planning, maintenance planning, reliability assessment, fault detection, and spare parts management . In particular, determining the reliability parameters enables applying many possible optimization policies (e.g., an optimal mix of maintenance policies, an effective spare parts management, an optimal production buffer design, the application of total productive maintenance techniques, etc.…”

Using Automated Planning for Traffic Signals Control

“…Complex actions in PDDL can be easily described to control a set of crossings. And, given that PDDL is a declarative language, this file can be autonomically tuned by a learning system, once its behaviour is observed in the real world [38].…”