Marvin: A Heuristic Search Planner with Online Macro-Action Learning

This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate macro-actions, which are then used during search for a solution plan. We provide an overview of its architecture and search behaviour, detailing the algorithms used. We also empirically demonstrate the effectiveness of its features in various planning domains; in particular, the effects on performance due to the use of macro-actions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates

Using Kernel Perceptrons to Learn Action Effects for Planning

Abstract — We investigate the problem of learning action effects in STRIPS and ADL planning domains. Our approach is based on a kernel perceptron learning model, where action and state information is encoded in a compact vector representation as input to the learning mechanism, and resulting state changes are produced as output. Empirical results of our approach indicate efficient training and prediction times, with low average error rates (< 3%) when tested on STRIPS and ADL versions of an object manipulation scenario. This work is part of a project to integrate machine learning techniques with a planning system, as part of a larger cognitive architecture linking a highlevel reasoning component with a low-level robot/vision system. I

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

EmergencyGrid:Planning in Convergence Environments

Government agencies are often responsible for event handling, planning, coordination, and status reporting during emergency response in natural disaster events such as floods, tsunamis and earthquakes. Across such a range of emergency response scenarios, there is a common set of requirements that distributed intelligent computer systems generally address. To support the implementation of these requirements, some researchers are proposing the creation of grids, where final interface and processing nodes perform joint work supported by a network infrastructure. The aim of this project is to extend the concepts of emergency response grids, using a convergence scenario between web and other computational platforms. Our initial work focuses on the Interactive Digital TV platform, where we intend to transform individual TV devices into active final nodes, using a hierarchical planning structure. We describe the architecture of this approach and an initial prototype specification that is being developed to validate some concepts and illustrate the advantages of this convergence planning environment