Ioannis Tsamardinos, Martha E. Pollack and John F. Horty, Merging plans with quantitative temporal constraints, temporally extended actions and conditional branches, Proceedings of the International Conference on Arti cial Intelligence Planning and Scheduling (AIPS), 2000.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....which has only one step take the medicine with a default duration of 1 minute. 4. Specify an interstep constraint to ensure that the medicine taking occurs within one hour of finishing breakfast. As each pre constructed plan fragment or constraint is added, the PM performs step merging [26,30], that is, it checks to ensure the consistency of the daily plan being constructed and resolves any conflicts. To do this, it uses the same techniques for consistency checking that are used during plan execution; these techniques are described in the next subsection. The same GUI can be used ....

Tsamardinos, I.; Pollack, M. E.; and Horty, J. F. 2000. Merging plans with quantitative temporal constraints, temporally extended actions, and conditional branches. 5th International Conference on Artificial Intelligence Planning and Scheduling, 264--272.

....we must know the observations at the start of execution. We now suggest a possible use of Weak Consistency for planning purposes, namely in planning architectures that are based on planmerging. Examples of such architectures are Workflow Management Systems [6] PRS [11] the Plan Management Agent [25], and Autorainder [16] to name a few. In these systems, there is a library of plan (or workflow) schemata, and whenever a new goal arrives, a plan from the plan library is selected and subsequently merged with the system s existing commitment structure, i.e. the set of (partially) instantJared ....

Tsamardinos, I., M. E. Pollack, and J. F. Horty: 2000, 'Merging Plans with Quantitative Temporal Constraints, Temporally Extended Actions, and Conditional Branches'. In: Proceedings of the 5th International Conference on Artificial Intelligence Planning and Scheduling.

....we must know the observations at the start of execution. We now suggest a possible use of Weak Consistency for planning purposes, namely in planning architectures that are based on planmerging. Examples of such architectures are Work ow Management Systems [6] PRS [11] the Plan Management Agent [25], and Autominder [16] to name a few. In these systems, there is a library of plan (or work ow) schemata, and whenever a new goal arrives, a plan from the plan library is selected and subsequently merged with the system s existing commitment structure, i.e. the set of (partially) instantiated ....

Tsamardinos, I., M. E. Pollack, and J. F. Horty: 2000, `Merging Plans with Quantitative Temporal Constraints, Temporally Extended Actions, and Conditional Branches'. In: Proceedings of the 5th International Conference on Arti cial Intelligence Planning and Scheduling.

....the PM. This plan may then be changed in one of three ways: i) by the addition of new activities 3 ; ii) by the modi cation or deletion of (constraints on) activities already in the plan; iii) by the execution of one of the planned activities. In the rst two cases, PM performs plan merging [21, 8, 20, 19]: to ensure that the change does not introduce a con ict. In the third case, it propagates the constraints a ected by activity execution, as described in the example above. To adequately represent the client plans, it is essential to support a rich set of temporal constraints: for example, we may ....

Ioannis Tsamardinos, Martha E. Pollack, and John F. Horty. Merging plans with quantitative temporal constraints, temporally extended actions, and conditional branches. In Proceedings of the 5th International Conference on Articial Intelligence Planning and Scheduling, 2000.

....be formulated as a Constraint Satisfaction Problem or CSP, with temporal features. The process must consider temporal constraints, resource usage, and causal links (preconditions and effects) There has been a great deal of research done on similar problems by the Artificial Intelligence community [14, 17, 20]. A number of formalizations have been developed for variations with more or less expressivity. The two that most closely match our problem are the Disjunctive Temporal Problem (DTP) and the Conditional Disjunctive Temporal Problem (CDTP) For solving DTPs we have developed and implemented a new ....

Tsamardinos I., M. E. Pollack, et al. Merging Plans with Quantitative Temporal Constraints, Temporally Extended Actions, and Conditional Branches. Artificial Intelligence Planning and Scheduling (AIPS'00), Breckenridge, Colorado, USA, 2000

....for P in the context of C. 4 Reasoning procedures We now present some algorithms for the reasoning processes described above. As explained earlier, the first step of the process involves determining whether P is, in fact, strongly compatible with the context C. In work reported elsewhere [26], we have recently developed a new 18 algorithm to compute consistency for plans that have quantitative temporal constraints and steps with extended duration, as well as observation actions and conditional branches. The algorithm uses a CSP based approach to find alternative sets of constraints ....

....to the theory as we have developed it in the paper. Most notably, we have dealt here only with complete, primitive plans, all of whose actions are instantaneous. In related work, we have been directly concerned with plans that include rich temporal constraints such as deadlines and durations [26], and we believe that the techniques developed there can be adapted to the problem of step merging in a more realistic temporal framework. Handling incomplete plans, however, requires extending the algorithms for cost computation, because the cost of an incomplete plan depends not just on possible ....

Ioannis Tsamardinos, Martha Pollack, and John Horty. Merging plans with quantitative temporal constraints, temporally extended actions, and conditional branches. In Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling. AAAI Press, 2000.

No context found.

Ioannis Tsamardinos, Martha E. Pollack and John F. Horty, Merging plans with quantitative temporal constraints, temporally extended actions and conditional branches, Proceedings of the International Conference on Arti cial Intelligence Planning and Scheduling (AIPS), 2000.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC