Merging plans with incomplete knowledge about actions and goals through an agent-based reputation system
This work addresses plan management for people with cognitive disabilities, such as autism, but appears incremental as it focuses on comparing merging algorithms within a specific framework.
The paper tackles the problem of generating transition plans for people with cognitive disabilities by proposing an agent-based system to merge plans with incomplete knowledge about actions and goals, using a distributed recommendation system to provide useful plans based on previous executions.
Managing transition plans is one of the major problems of people with cognitive disabilities. Therefore, finding an automated way to generate such plans would be a helpful tool for this community. In this paper we have specifically proposed and compared different alternative ways to merge plans formed by sequences of actions of unknown similarities between goals and actions executed by several operator agents which cooperate between them applying such actions over some passive elements (node agents) that require additional executions of another plan after some time of use. Such ignorance of the similarities between plan actions and goals would justify the use of a distributed recommendation system that would provide an useful plan to be applied for a certain goal to a given operator agent, generated from the known results of previous executions of different plans by other operator agents. Here we provide the general framework of execution (agent system), and the different merging algorithms applied to this problem. The proposed agent system would act as an useful cognitive assistant for people with intelectual disabilities such as autism.