Concept of a Reflex Manager to Enhance the Planner Component of an Autonomic / Organic System

Schmitt, Julia; Roth, Michael; Kiefhaber, Rolf; Kluge, Florian; Ungerer, Theo

doi:10.1007/978-3-642-23496-5_2

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Storing full plans has two main drawbacks: the storing of long plans or plan created in complex dynamic environments can lead to increase memory consumption and response times 43 . Furthermore, in dynamic environments with a high level of unexpected situations, the plan stored is rarely fully executed, only the first actions until a new unexpected situation happens.…”

Section: Case-base Representationmentioning

confidence: 99%

On-Line Case-Based Policy Learning for Automated Planning in Probabilistic Environments

Martínez

Garcı́a

Fernández

2018

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

Many robotic control architectures perform a continuous cycle of sensing, reasoning and acting, where that reasoning can be carried out in a reactive or deliberative form. Reactive methods are fast and provide the robot with high interaction and response capabilities. Deliberative reasoning is particularly suitable in robotic systems because it employs some form of forward projection (reasoning in depth about goals, pre-conditions, resources and timing constraints) and provides the robot reasonable responses in situations unforeseen by the designer. However, this reasoning, typically conducted using Artificial Intelligence techniques like Automated Planning (AP), is not effective for controlling autonomous agents which operate in complex and dynamic environments. Deliberative planning, although feasible in stable situations, takes too long in unexpected or changing situations which require re-planning. Therefore, planning cannot be done on-line in many complex robotic problems, where quick responses are frequently required. In this paper, we propose an alternative approach based on case-based policy learning which integrates deliberative reasoning through AP and reactive response time through reactive planning policies. The method is based on learning planning knowledge from actual experiences to obtain a case-based policy. The contribution of this paper is two fold. First, it is shown that the learned case-based policy produces reasonable and timely responses in complex environments. Second, it is also shown how one case-based policy that solves a particular problem can be reused to solve a similar but more complex problem in a transfer learning scope.

show abstract

Section: Case-base Representationmentioning

confidence: 99%