Normative systems have been proposed as a useful abstraction to represent ideals of behaviour for autonomous agents in a social context. They specify constraints that agents ought to follow, but may sometimes be violated. Norms can increase the predictability of a system and make undesired situations less likely. When designing normative systems, it is important to anticipate the effects of possible violations and understand how robust these systems are to violations. Previous research on robustness analysis of normative systems builds upon simplistic norm formalisms, lacking support for the speciļ¬cation of complex norms that are often found in real world scenarios. Furthermore, existing approaches do not consider the fact that compliance with different norms may be more or less important in preserving some desirable properties of a system; that is, norm violations may vary in severity. In this thesis we propose models and algorithms to represent and reason about complex norms, where their violation may vary in severity. We build upon existing preference-based deontic logics and propose mechanisms to rank the possible states of a system according to what norms they violate, and their severity. Further, we propose mechanisms to analyse the properties of the system under different compliance assumptions, taking into account the severity of norm violations. Our norm formalism supports the speciļ¬cation of norms that regulate temporally extended behaviour and those that regulate situations where other norms have been violated. We then focus on algorithms that allow coalitions of agents to coordinate their actions in order to minimise the risk of severe violations. We propose offline algorithms and heuristics for pre-mission planning in stochastic scenarios where there is uncertainty about the current state of the system. We then develop online algorithms that allow agents to maintain a certain degree of coordination and to use communication to improve their performance.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:715478 |
| Date | January 2017 |
| Creators | Gasparini, Luca |
| Publisher | University of Aberdeen |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=231873 |
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