Imperial College London
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ProfessorAlessioLomuscio

Faculty of Engineering, Department of Computing

Professor of Safe Artificial Intelligence
 
 
 
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Contact

 

+44 (0)20 7594 8414a.lomuscio Website

 
 
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Location

 

Imperial-XTranslation & Innovation Hub BuildingWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Lomuscio:2018,
author = {Lomuscio, AR and akitunde, M and maganti, L and Pirovano, E},
publisher = {Association for the Advancement of Artificial Intelligence},
title = {Reachability analysis for neural agent-environment systems},
url = {https://aaai.org/ocs/index.php/KR/KR18/paper/view/17991},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We  develop  a  novel  model  for  studying  agent-environmentsystems, where the agents are implemented via feed-forwardReLU neural networks. We provide a semantics and developa method to verify automatically that no unwanted states arereached by the system during its evolution. We study severalreachability problems for the system, ranging from one-stepreachability,  to  fixed  multi-step  and  arbitrary-step  to  studythe system evolution. We also study the decision problem ofwhether an agent, realised via feed-forward ReLU networkswill perform an action in a system run. Whenever possible,we give tight complexity bounds to decision problems intro-duced. We automate the various reachability problems stud-ied by recasting them as mixed-integer linear programmingproblems. We present an implementation and discuss the ex-perimental results obtained on a range of test cases.
AU  - Lomuscio,AR
AU  - akitunde,M
AU  - maganti,L
AU  - Pirovano,E
PB  - Association for the Advancement of Artificial Intelligence
PY  - 2018///
TI  - Reachability analysis for neural agent-environment systems
UR  - https://aaai.org/ocs/index.php/KR/KR18/paper/view/17991
UR  - http://hdl.handle.net/10044/1/63044
ER  -
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