Imperial College London

Professor Emil Lupu

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems
 
 
 
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Contact

 

e.c.lupu Website

 
 
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Location

 

564Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Felmlee,
author = {Felmlee, D and Lupu, E and McMillan, C and Karafili, E and Bertino, E},
publisher = {IEEE},
title = {Decision-making in policy governed human-autonomous systems teams},
url = {http://hdl.handle.net/10044/1/48241},
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Policies govern choices in the behavior of systems. They are applied to human behavior as well as to the behavior of autonomous systems but are defined differently in each case. Generally humans have the ability to interpret the intent behind the policies, to bring about their desired effects, even occasionally violating them when the need arises. In contrast, policies for automated systems fully define the prescribed behavior without ambiguity, conflicts or omissions. The increasing use of AI techniques and machine learning in autonomous systems such as drones promises to blur these boundaries and allows us to conceive in a similar way more flexible policies for the spectrum of human-autonomous systems collaborations. In coalition environments this spectrum extends across the boundaries of authority in pursuit of a common coalition goal and covers collaborations between human and autonomous systems alike.In social sciences, social exchange theory has been applied successfully to explain human behavior in a variety of contexts. It provides a framework linking the expected rewards, costs, satisfaction and commitment to explain and anticipate the choices that individuals make when confronted with various options. We discuss here how it can be used within coalition environments to explain joint decision making and to help formulate policies re-framing the concepts where appropriate. Social exchange theory is particularly attractive within this context as it provides a theory with “measurable” components that can be readily integrated in machine reasoning processes.
AU - Felmlee,D
AU - Lupu,E
AU - McMillan,C
AU - Karafili,E
AU - Bertino,E
PB - IEEE
TI - Decision-making in policy governed human-autonomous systems teams
UR - http://hdl.handle.net/10044/1/48241
ER -