Some application domains highlight the importance of distributed continual planning concepts; coordinating teams of unmanned ground vehicles in dynamic environments is an example of such a domain. In this article, I illustrate the ideas in, and promises of, distributed continual planning by showing how acquiring and distributing operator intent among multiple semiautonomous vehicles supports ongoing, cooperative mission elaboration and revision. It is this longer-term view that motivates the use of planning such that an agent should decide between alternative anticipated sequences of activities; otherwise, the application might be better served with simpler reactive agents that only decide on their very next actions. Second, what the agent knows about the application domain, or what the agent's objectives are, or both, can change over time. Information about the domain could be revealed incrementally or could dynamically change in ways outside the agent's control, and thus, the agent should continually reevaluate its ongoing plans and revise or elaborate them to accommodate the changes.

Complex, real-world domains require rethinking traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment implies a continual approach in which planning and execution are interleaved, uncertainty in the current and projected world state is recognized and handled appropriately, and replanning can be performed when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, which we term distributed, continual planning (DCP). We argue that developing DCP systems will be necessary for planning applications to be successful in these environments.

To solve ever more complex and longer tasks, mobile robots need to generate more elaborate plans and must handle dynamic environments and incomplete knowledge. We address this challenge by integrating two seemingly different approaches — PDDL-based planning for efficient plan generation and Golog for highly expressive behavior specification — in a coherent framework that supports continual planning. The latter allows to interleave plan generation and execution through assertions, which are placeholder actions that are dynamically expanded into conditional sub-plans (using classical planners) once a replanning condition is satisfied. We formalize and implement continual planning in Golog which was so far only supported in PDDL-based systems. This enables combining the execution of generated plans with regular Golog programs and execution monitoring. Experiments on autonomous mobile robots show that the approach supports expressive behavior specification combined with efficient sub-plan generation to handle dynamic environments and incomplete knowledge in a unified way.

Web Service (WS) domains constitute an application field where automated planning can significantly contribute towards achieving customisable and adaptable compositions. Following the vision of using domain-independent planning and declarative complex goals to generate compositions based on atomic service descriptions, we apply a planning framework based on Constraint Satisfaction techniques to a domain consisting of WSs with diverse functionalities. One of the key requirements of such domains is the ability to address the incomplete knowledge problem, as well as recovering from failures that may occur during execution. We propose an algorithm for interleaving planning, monitoring and execution, where continual planning via altering the CSP is performed, under the light of the feedback acquired at runtime. The system is evaluated against a number of scenarios including real WSs, demonstrating the leverage of situations that can be effectively tackled with respect to previous approaches.

Planning and executing the resulting plans in a dynamic environment implies a continual approach in which planning and execution are interleaved, uncertainty in the current and projected world state is recognized and handled appropriately, and replanning can be performed when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, which we term distributed, continual planning (DCP). We give a historical overview of research leading to the current state of the art in DCP and describe research in distributed and continual planning.

Some application domains highlight the importance of distributed continual planning concepts; coordinating teams of unmanned ground vehicles in dynamic environments is an example of such a domain. In this article, I illustrate the ideas in, and promises of, distributed continual planning by showing how acquiring and distributing operator intent among multiple semiautonomous vehicles supports ongoing, cooperative mission elaboration and revision.

Some application domains highlight the importance of distributed continual planning concepts; coordinating teams of unmanned ground vehicles in dynamic environments is an example of such a domain. In this article, I illustrate the ideas in, and promises of, distributed continual planning by showing how acquiring and distributing operator intent among multiple semiautonomous vehicles supports ongoing, cooperative mission elaboration and revision.

Complex, real-world domains require rethinking traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment implies a continual approach in which planning and execution are interleaved, uncertainty in the current and projected world state is recognized and handled appropriately, and replanning can be performed when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, which we term distributed, continual planning (DCP). We argue that developing DCP systems will be necessary for planning applications to be successful in these environments. We give a historical overview of research leading to the current state of the art in DCP and describe research in distributed and continual planning.