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Title: Flexible autonomy and context in human-agent collectives
Author: Dybalova, Daniela
ISNI:       0000 0004 6352 2156
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2017
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Human-agent collectives (HACs) are collaborative relationships between humans and software agents that are formed to meet the individual and collective goals of their members. In general, different members of a HAC should have differing degrees of autonomy in determining how a goal is to be achieved, and the degree of autonomy that should be enjoyed by each member of the collective varies with context. This thesis explores how norms can be used to achieve context sensitive flexible autonomy in HACs. Norms can be viewed as defining standards of ideal behaviour. In the form of rules and codes, they are widely used to coordinate and regulate activity in human organisations, and more recently they have also been proposed as a coordination mechanism for multi-agent systems (MAS). Norms therefore have the potential to form a common framework for coordination and control in HACs. The thesis develops a novel framework in which group and individual norms are used to specify both the goal to be achieved by a HAC and the degree of autonomy of the HAC and/or of its members in achieving a goal. The framework allows members of a collective to create norms specifying how a goal should (or should not) be achieved, together with sanctions for non-compliance. These norms form part of the decision making context of both the humans and agents in the collective. A prototype implementation of the framework was evaluated using the Colored Trails test-bed in a scenario involving mixed human-agent teams. The experiments confirmed that norms can be used for coordination of HACs and to facilitate context related flexible autonomy.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA 75 Electronic computers. Computer science