Use this URL to cite or link to this record in EThOS:
Title: Intelligent agent support for policy authoring and refinement
Author: Aphale, Mukta S.
ISNI:       0000 0004 5353 660X
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2015
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
A policy (or norm) can be defined as a guideline stating what is allowed, what is forbidden and what is obligated for an entity, in a certain situation, so that an acceptable outcome is achieved. Policies occur in many types of scenarios, whether they are loose social networks of individuals or highly structured institutions. It is important, however, for policies to be consistent and to support the goals of organisations they govern. This requires a thorough understanding of the implications of introducing specific policies and how they interact. It is difficult, even for experts, to write consistent, unambiguous and accurate policies, and conflicts are practically unavoidable. At the same time conflicts may vary in significance. For example, some conflicts are most likely to occur, some conflicts may lead to high cost for goal achievement while some conflicts may lead to severe obstacles in the achievement of goals. Such conflicts are the most significant for the domain and goals of organisation. Resolution of conflicts that will clear the obstacles in the goal achievement and will maximize the benefits received must be prioritised. In order to resolve conflicts and refine policies; it is crucial to understand the implications of policies, conflicts and resolutions in terms of goal achievement and benefits to organisation. There exist huge number of policies and conflicts occurring within any organisation. Human decision makers are most likely to be cognitively overloaded. Making is difficult for them to decide which conflicts to prioritise in order to successfully achieve goals while maximizing benefits. Automated reasoning mechanisms can effectively support human decision makers in this process. In this thesis, we have addressed the problem of developing effective automated reasoning support for the detection and resolution of conflicts between plans (to achieve a given goal) and policies. We also present an empirical evaluation of a model of conflict detection and prioritisation through experiments with human users. Our empirical evaluations prove that providing guidance to users regarding what conflicts to prioritise and highlighting related conflicts lead to higher quality outcomes, thereby achieving goals successfully and rapidly.
Supervisor: Not available Sponsor: RCUK Digital Economy Programme through the dot.rural Digital Hub
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Decision making ; Computer-assisted instruction