A Hybrid Approach to Process Planning: The Urban Traffic Controller Example
| Autor: | Thomas Leo McCluskey, Simon Parkinson, Jimoh Falilat Olaitan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
QA75
0209 industrial biotechnology State variable Computer Networks and Communications Process (engineering) Computer science Control (management) Business system planning 02 engineering and technology Machine learning computer.software_genre Q1 Domain (software engineering) QA76 020901 industrial engineering & automation Artificial Intelligence Control theory Automated planning and scheduling 0202 electrical engineering electronic engineering information engineering QA business.industry Industrial engineering Model predictive control TA 020201 artificial intelligence & image processing Artificial intelligence business computer Software |
| ISSN: | 1549-3636 |
| Popis: | Automated planning and scheduling are increasingly utilised in solving evsery day planning task. Planning in domains with continuous numeric changes present certain limitations and tremendous challenges to advanced planning algorithms. There are many pertinent examples to the engineering community; however, a case study is provided through the urban traffic controller domain. This paper introduce a novel hybrid approach to state-space planning systems involving a continuous process which can be utilised in several applications. We explore Model Predictive Control (MPC) and explain how it can be introduce into planning with domains containing mixed discrete and continuous state variables. This preserves the numerous benefits of AI Planning approach by the use of explicit reasoning and declarative modelling. It also leverages on the capability of MPC to manage numeric computation and control of continuous processes. The hybrid approach was tested on an urban traffic control network to ascertain it practicability on a continuous domain; the results show its potential to control and optimise heavy volumes of traffic. |
| Databáze: | OpenAIRE |
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