ENAC Seminar Series by Dr C. Andriotis
Event details
| Date | 23.09.2019 |
| Hour | 13:00 › 14:00 |
| Speaker | Dr Charalampos Andriotis |
| Location | |
| Category | Conferences - Seminars |
13:00 – 14:00 – Dr Charalampos Andriotis
Research Assistant, Pennsylvania State University, USA
AI-driven decision-making for intelligent infrastructure: enabling the built environment to reason and adapt
Over the past 5-10 years, we have witnessed groundbreaking advances in data analytics and Artificial Intelligence (AI), which have prompted transformative changes in society, science and engineering. These advances come with tremendous opportunities for civil engineering to expand its impact to the prospects of the fourth industrial revolution, and address the unprecedented challenges posed by an aging, growing and changing built environment. In view of multiple hazards, increased population demands, climate change effects, resource limitations, and big data delivering incomplete information, intelligent infrastructure management and design requirements exceed the limits of conventional approaches, necessitating innovative data-driven decision-making and informatics frameworks, able to support nimble and robust mitigation of the involved socioeconomic and environmental risks.
Such frameworks need to efficiently (i) facilitate learning and reasoning from real-time, noisy observational and/or sensory data; (ii) quantify the value-of-information of monitoring technologies towards improved decision-making; (iii) inform detailed decision plans in complex multi-component networks operating in high-dimensional stochastic domains; (iv) ensure long-term optimality and adaptability over the entire system life-cycle; and (v) be robust to resource and/or sustainability-related constraints. Bridging AI and engineering decision-making, the development of novel methods that address these needs is discussed in this talk, within the contexts of stochastic dynamic programming, dynamic Bayesian networks, partially observable Markov decision processes, deep learning and centralized/decentralized multi-agent reinforcement learning.
The developed approaches target, but are not limited to, structural applications and civil infrastructure management, and are analyzed as per their remarkable strengths in providing solutions to otherwise intractable decision problems. The contributions and future directions of the developed line of research are presented, along with their capabilities in enabling decision-making in high-dimensional state/action spaces, value-of-information assessment, life-cycle optimality, real-time autonomous strategy adaptation in view of new information, flexible policies that effectively and timely avert future risks, and resilient recovery from extreme event catastrophes.
Research Assistant, Pennsylvania State University, USA
AI-driven decision-making for intelligent infrastructure: enabling the built environment to reason and adapt
Over the past 5-10 years, we have witnessed groundbreaking advances in data analytics and Artificial Intelligence (AI), which have prompted transformative changes in society, science and engineering. These advances come with tremendous opportunities for civil engineering to expand its impact to the prospects of the fourth industrial revolution, and address the unprecedented challenges posed by an aging, growing and changing built environment. In view of multiple hazards, increased population demands, climate change effects, resource limitations, and big data delivering incomplete information, intelligent infrastructure management and design requirements exceed the limits of conventional approaches, necessitating innovative data-driven decision-making and informatics frameworks, able to support nimble and robust mitigation of the involved socioeconomic and environmental risks.
Such frameworks need to efficiently (i) facilitate learning and reasoning from real-time, noisy observational and/or sensory data; (ii) quantify the value-of-information of monitoring technologies towards improved decision-making; (iii) inform detailed decision plans in complex multi-component networks operating in high-dimensional stochastic domains; (iv) ensure long-term optimality and adaptability over the entire system life-cycle; and (v) be robust to resource and/or sustainability-related constraints. Bridging AI and engineering decision-making, the development of novel methods that address these needs is discussed in this talk, within the contexts of stochastic dynamic programming, dynamic Bayesian networks, partially observable Markov decision processes, deep learning and centralized/decentralized multi-agent reinforcement learning.
The developed approaches target, but are not limited to, structural applications and civil infrastructure management, and are analyzed as per their remarkable strengths in providing solutions to otherwise intractable decision problems. The contributions and future directions of the developed line of research are presented, along with their capabilities in enabling decision-making in high-dimensional state/action spaces, value-of-information assessment, life-cycle optimality, real-time autonomous strategy adaptation in view of new information, flexible policies that effectively and timely avert future risks, and resilient recovery from extreme event catastrophes.
Practical information
- General public
- Free
Organizer
- ENAC
Contact
- Cristina Perez