intelligentsimulaon,( urban(mobility(and(smar-ficaon...
TRANSCRIPT
Intelligent Simula-on, Urban Mobility and Smar-fica-on, Intelligent Transporta-on Systems
through MAS
Rosaldo Rosse< LIACC-‐DEI/FEUP rosse<@fe.up.pt
hJp://www.fe.up.pt/~rosse</
LIACC/DEI
• Laboratório de Inteligência Ar-ficial & Ciência e Computadores
– DAIAS: Distributed Ar/ficial Intelligence & Intelligent Simula/on (FEUP)
– CS: Computer Science (FCUP) – HMIC: Human-‐Machine Intelligent Coopera-on (FEUP & U. Minho)
Intelligent Simula-on, Urban Mobility and Smar-fica-on, & Intelligent Transporta-on Systems
• General Problem & Research Ques/on: How to improve efficiency in the so-‐called socio-‐technical systems?
• Socio-‐technical systems: In short, S-‐T systems present strong interac-ons (symbiosis) between social en--es (humans, social organisa-ons) and the whole bunch of technological components in the system!
• Assump/on: This can be achieved through appropriate mechanisms of social coordina-on (e.g. incen-ves, nego-a-on, argumenta-on), and through adap-ng & persuading humans towards social awareness!
Mul-agent Systems & Models • Composed by mul-ple agents that:
– Exhibit autonomous behavior – Interact with the other agents in the system
• MAS Mo-va-on: – Problem Dimensions – Legacy Systems – Natural Solu-on (distributed problems) – Distributed knowledge or informa-on – Human-‐machine interface – Project Clarity and simplicity – Efficiency – Robustness and Scalability – Problem division – Informa-on privacy
Intelligent Simula-on
• Modelling and Simula-on Theory – Modelling metaphors and methodologies – Test, verifica-on, calibra-on and valida-on – Model-‐driven vs. data-‐driven approaches
• Agent-‐based simula-on – MAS simula-on – MAS-‐directed simula-on – MAS-‐based simulators
Intelligent Simula-on
• Mul--‐resolu-on & mul--‐domain simula-on – Collabora-ve simula-on – Simulator interoperability – HW & SW-‐in-‐the-‐loop simula-on – Parallel worlds
• Social Simula-on – Behavioural modelling and simula-on – Peer-‐designed Agents & Par-cipatory Simula-on – Growing Ar-ficial Socie-es
Urban Smar-fica-on & Mobility
• Various open issues – Open Data Standards – Living Labs (par-cipatory simula-on) – Agent-‐based SOA for smart ci-es – Social issues of smar-fica-on
– Social-‐awareness in smar-fica-on – Public policy-‐making & Incen-ve-‐based design – Energy efficiency & electric mobility – Alterna-ve means of transport
Methodological Approach ex
pert
agen
ts
observes, analyses andsuggests alternatives
off-linedecision support models
on-lineactuation models
replication
P1, P2, P3, ..., Pn(control policies)
virtual domain
UA
TA UA
TA
UATA
UA
Optimisation strategies inductor
TA
CA
EAEA
EA
EA
EA
EA
real world
replicationmixed realities
The MAS-‐Ter Lab Framework
Methodological Approach
• Serious Games and Gamifica-on – Social Awareness – Par-cipatory Modelling & Simula-on – Crowd Sensing
7
Methodological Approach
GIS
Social Media
SAPO Labs
• Management Strategies • Policy-‐making • Incen-ve-‐based Design
9
Intelligent Transporta-on Systems
Intelligent Transporta-on Systems
• Serious Games & Gamifica-on in behaviour modelling and social simula-on
– Behaviour Assimila-on – Behaviour Elicita-on – Behaviour Persuasion
– Peer-‐designed Agents & Par-cipatory Simula-on
Intelligent Transporta-on Systems
Intelligent Transporta-on Systems
SPEED Framework
Intelligent Transporta-on Systems
IC-‐DEEP Framework
• 6 PhD students – L. Passos, on Fault Tolerance in MAS – Z. Kokkinogenis, on Incen/ve-‐based Design and social coordina/on
– J. Almeida, on Serious Games for Behaviour Elicita/on, Assimila/on and Persuasion, in Building Evacua-on and Emergency Scenarios
– D. Perrota, on Electric Mobility for Public Transport in urban areas
– C. Vilarinho, on Intelligent Traffic Control and Op/misa/on
– C. Silva, on Energy Systems and Smart Grids
Some Current Projects
Intelligent Simula-on, Urban Mobility and Smar-fica-on, Intelligent Transporta-on Systems
through MAS
Rosaldo Rosse< LIACC-‐DEI/FEUP rosse<@fe.up.pt
hJp://www.fe.up.pt/~rosse</