Planning as an Iterative Process

Activity planning for missions such as the Mars Exploration Rover mission presents many technical challenges, including oversubscription, consideration of time, concurrency, resources, preferences, and uncertainty. These challenges have all been addressed by the research community to varying degrees, but significant technical hurdles still remain. In addition, the integration of these capabilities into a single planning engine remains largely unaddressed. However, I argue that there is a deeper set of issues that needs to be considered namely the integration of planning into an iterative process that begins before the goals, objectives, and preferences are fully defined. This introduces a number of technical challenges for planning, including the ability to more naturally specify and utilize constraints on the planning process, the ability to generate multiple qualitatively different plans, and the ability to provide deep explanation of plans

I-P2 - Intelligent Process Panels to Support Coalition Operations

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.I-X is a research programme with a number of different aspects intended to create a well-founded approach to allow humans and computer systems to cooperate in the creation or modification of some product or products such as documents, plans or designs. I-X may also be used to support more general collaborative activity.The I-X research draws on earlier work on O-Plan (Tate et.al., 1998; Tate et.al., 2000; Tate et.al., 2002), (Tate, 1996), the Enterprise Project (Fraser and Tate, 1995; Stader, 1996); Uschold, et.al., 1998) and the TBPM project (Stader, 2000) but seeks to make the framework generic and to clarify terminology, simplify the approach taken, and increase re-usability and applicability of the core ideas.I-X Applications are being studied in a variety of areas. These currently include:· Coalition Operations (CoAX: I-LEED, I-DEEL)· Emergency and Unusual Procedure Assistance (I-Rescue)· Help Desk Support (I-Help)· Multi-Perspective Knowledge Modelling and Management (I-AKT)· Contextualised Presentations of Procedures and Plans (I-Tell)· Collaborative Meeting and Task Support (I-Room, I-Space)An application of I-X Process Panels within a military Coalition context - part of the Coalition Agents eXperiment - CoAX (Allsopp et.al., 2001; Allsopp et.al., 2002) will be described in this paper

Coalition Task Support using I-X and <I-N-C-A>

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the authorâ s and shouldnâ t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.I-X is a research programme with a number of different aspects in-tended to create a well-founded approach to allow humans and computer systems to cooperate in the creation or modification of some product such as a design, physical entity or plan - i.e. it supports cooperative synthesis tasks. The I-X approach involves the use of shared models for task-directed cooperation between human and computer agents who are jointly exploring (via some, perhaps dynamically determined, process) a range of alternative options for the synthesis of an artifact such as a design or a plan (termed a product). The (Issues - Nodes - Constraints - Annotations) ontology is used to represent a specific artifact as a set of constraints on the space of all possible artifacts in an application domain. It can be used to describe the requirements or specification to be achieved and the emerging description of the artifact itself. It can also describe the (perhaps dynamically generated) processes involved. I-X and | have been applied to Coalition Task Support

Activity-oriented Instant Messaging for Coalition Operations

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.I-X Process Panels are used to support users who are carrying out processes and responding to events in a cooperative working environment. The panels support the tracking of personal or group issues, the planning and execution of activities and the checking of constraints. Panels can be connected to other panels, and also to a range of services, agents and other cooperative working support tools to form part of a framework for activity and process support in an organization. The dynamically changing context in which a user operates is reflected in the options presented. Actual usage in a multi-national coalition operations setting indicated the value of adopting an “instant messaging” style of use. An augmented activity-orientated “intelligent messaging” approach is taken in which artificial intelligence planning technology can be deployed in a natural way

I-Ex: Extreme Expedition Support

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.The aim of the I-X research programme is to provide a general framework for performing mixed-initiative synthesis tasks, along with a set of tools that supports its use. This framework arises from and builds upon seminal work at the University of Edinburgh in the field of Artificial Intelligence planning. In this paper we describe the framework and tools, before describing the application of I-X to the task of planning and coordinating expeditions to remote locations – such as an attempt on Everest. We call this application I-Ex

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/

Knowledge-Based Task Structure Planning for an Information Gathering Agent

An effective solution to model and apply planning domain knowledge for deliberation and action in probabilistic, agent-oriented control is presented. Specifically, the addition of a task structure planning component and supporting components to an agent-oriented architecture and agent implementation is described. For agent control in risky or uncertain environments, an approach and method of goal reduction to task plan sets and schedules of action is presented. Additionally, some issues related to component-wise, situation-dependent control of a task planning agent that schedules its tasks separately from planning them are motivated and discussed