Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

An Ontological formalization of the planning task

In this paper we propose a generic task ontology, which formalizes the space of planning problems. Although planning is one of the oldest researched areas in Artificial Intelligence and attempts have been made in the past at developing task ontologies for planning, these formalizations suffer from serious limitations: they do not exhibit the required level of formalization and precision and they usually fail to include some of the key concepts required for specifying planning problems. In con-trast with earlier proposals, our task ontology formalizes the nature of the planning task independently of any planning paradigm, specific domains, or applications and provides a fine-grained, precise and comprehensive characterization of the space of planning problems. Finally, in addition to producing a formal specification we have also operationalized the ontology into a set of executable definitions, which provide a concrete reusable resource for knowledge acquisition and system development in planning applications

Learning how to combine sensory-motor functions into a robust behavior

AbstractThis article describes a system, called Robel, for defining a robot controller that learns from experience very robust ways of performing a high-level task such as “navigate to”. The designer specifies a collection of skills, represented as hierarchical tasks networks, whose primitives are sensory-motor functions. The skills provide different ways of combining these sensory-motor functions to achieve the desired task. The specified skills are assumed to be complementary and to cover different situations. The relationship between control states, defined through a set of task-dependent features, and the appropriate skills for pursuing the task is learned as a finite observable Markov decision process (MDP). This MDP provides a general policy for the task; it is independent of the environment and characterizes the abilities of the robot for the task

HTN planning: Overview, comparison, and beyond

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique.<br/

DSHOP: Distributed simple hierarchical ordered planner.

Planning has been an important subject in the area of Artificial Intelligence (AI) for over three decades. Planning is the problem of seeking a series of actions (that is, a plan) that will accomplish a desired goal. Most planning approaches rely on a single processor or a single-agent paradigm. Unfortunately, in a complex world, a single agent may not be sufficient to optimally solve the problem. Distributed Planning is a sub-field of Distributed AI that involves multi-agents working together to solve large planning problems. Distribution may speed up the traditional planning system through parallelism. Hierarchical Task Network (HTN) planning is an AI planning methodology that creates plans by task decomposition. SHOP (Simple Hierarchical Ordered Planner) is a domain-independent HTN planning system designed by Dana Nau et al. that plans for tasks in the same order that they will later be executed. This thesis aims at designing and implementing a distributed version of SHOP (that is, DSHOP) and running it on a high performance distributed system called SHARCNET. The implementation is based upon Message Passing Interface (MPI), that is, a library of functions used to achieve parallelism via message-passing. We investigate two approaches to share work between processors: state-copying and state-recomputation. We implemented a state-copying based DSHOP system (DSHOPC), and a state-recomputation based DSHOP system (DSHOPR). We compared these two implementations of DSHOP with the Java version of SHOP on a set of randomly generated artificial domains. A set of experimental results has been used to evaluate the performance of the DSHOP algorithm.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .L83. Source: Masters Abstracts International, Volume: 43-01, page: 0240. Advisers: Scott Goodwin; Froduald Kabanza. Thesis (M.Sc.)--University of Windsor (Canada), 2004

A Unifying Algorithm for Conditional, Probabilistic Planning

Several recent papers describe algorithms for generating conditional and/or probabilistic plans. In this paper, we synthesize this work, and present a unifying algorithm that incorporates and clarifies the main techniques that have been developed in the previous literature. Our algorithm decouples the search-control strategy for conditional and/or probabilistic planning from the underlying plan-refinement process. A similar decoupling has proven to be very useful in the analysis of classical planning algorithms, and we suspect it can be at least as useful here, where the search-control decisions are even more crucial. We describe an extension of conditional, probabilistic planning, to provide candidates for decision-theoretic assessment, and describe the reasoning about failed branches and side-effects that is needed for this purpose

Plan Projection, Execution, and Learning for Mobile Robot Control

Most state-of-the-art hybrid control systems for mobile robots are decomposed into different layers. While the deliberation layer reasons about the actions required for the robot in order to achieve a given goal, the behavioral layer is designed to enable the robot to quickly react to unforeseen events. This decomposition guarantees a safe operation even in the presence of unforeseen and dynamic obstacles and enables the robot to cope with situations it was not explicitly programmed for. The layered design, however, also leaves us with the problem of plan execution. The problem of plan execution is the problem of arbitrating between the deliberation- and the behavioral layer. Abstract symbolic actions have to be translated into streams of local control commands. Simultaneously, execution failures have to be handled on an appropriate level of abstraction. It is now widely accepted that plan execution should form a third layer of a hybrid robot control system. The resulting layered architectures are called three-tiered architectures, or 3T architectures for short. Although many high level programming frameworks have been proposed to support the implementation of the intermediate layer, there is no generally accepted algorithmic basis for plan execution in three-tiered architectures. In this thesis, we propose to base plan execution on plan projection and learning and present a general framework for the self-supervised improvement of plan execution. This framework has been implemented in APPEAL, an Architecture for Plan Projection, Execution And Learning, which extends the well known RHINO control system by introducing an execution layer. This thesis contributes to the field of plan-based mobile robot control which investigates the interrelation between planning, reasoning, and learning techniques based on an explicit representation of the robot's intended course of action, a plan. In McDermott's terminology, a plan is that part of a robot control program, which the robot cannot only execute, but also reason about and manipulate. According to that broad view, a plan may serve many purposes in a robot control system like reasoning about future behavior, the revision of intended activities, or learning. In this thesis, plan-based control is applied to the self-supervised improvement of mobile robot plan execution