Partial-Order Planning with Concurrent Interacting Actions

In order to generate plans for agents with multiple actuators, agent teams, or distributed controllers, we must be able to represent and plan using concurrent actions with interacting effects. This has historically been considered a challenging task requiring a temporal planner with the ability to reason explicitly about time. We show that with simple modifications, the STRIPS action representation language can be used to represent interacting actions. Moreover, algorithms for partial-order planning require only small modifications in order to be applied in such multiagent domains. We demonstrate this fact by developing a sound and complete partial-order planner for planning with concurrent interacting actions, POMP, that extends existing partial-order planners in a straightforward way. These results open the way to the use of partial-order planners for the centralized control of cooperative multiagent systems

Planning Technologies for Interactive Storytelling

Since AI planning was first proposed for the task of narrative generation in interactive storytelling (IS), it has emerged as the dominant approach in this field. This chapter traces the use of planning technologies in this area, considers the core issues involved in the application of planning technologies in IS, and identifies some of the remaining challenges

Merging plans with incomplete knowledge about actions and goals through an agent-based reputation system

In This Paper, We Propose And Compare Alternative Ways To Merge Plans Formed Of Sequences Of Actions With Unknown Similarities Between The Goals And Actions. Plans Are Formed Of Actions And Are Executed By Several Operator Agents, Which Cooperate Through Recommendations. The Operator Agents Apply The Plan Actions To Passive Elements (Which We Call Node Agents) That Will Require Additional Future Executions Of Other Plans After Some Time. The Ignorance Of The Similarities Between The Plan Actions And The Goals Justifies The Use Of A Distributed Recommendation System To Produce A Useful Plan For A Given Operator Agent To Apply Towards A Certain Goal. This Plan Is Generated From The Known Results Of Previous Executions Of Various Plans By Other Operator Agents. Here, We Present The General Framework Of Execution (The Agent System) And The Results Of Applying Various Merging Algorithms To This Problem.This work was supported in part by Project MINECO TEC2017-88048-C2-2-

Planning with Concurrent Interacting Actions

In order to generate plans for agents with multiple actuators or agent teams, we must be able to represent and plan using concurrent actions with interacting effects. Historically, this has been considered a challenging task that could require a temporal planner. We show that, with simple modifications, the STRIPS action representation language can be used to represent concurrent interacting actions. Moreover, current algorithms for partial-order planning require only small modifications in order to handle this language and produce coordinated multiagent plans. These results open the way to partial order planners for cooperative multiagent systems. AI [8]—very little research addresses the MAP problem.2