Computing and Information Systems - Theses
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ItemImprovised coordination in agent organisations( 2018)This thesis investigates coordination between intelligent software agents operating within agent organisations. Motivated by the prospect of agents working with humans in real world complex domains, the thesis focuses on flexible behaviour and improvisation in agent organisations. Methods used to design organisations of software agents are explored with particular consideration given to problem situations that cannot be defined with a detailed pre-scripted solution for coordinated action. A conceptual model that describes the components that are needed in an agent based model in a multi-agent system is referred to in this thesis as a meta-model. A number of agent organisation-based meta-models and frameworks for coordination of agents have been proposed such as OperA, OMACS and SharedPlans. There is however, no specific meta-model or approach that addresses agent improvisation and unscripted coordination. The reality of complex coordination in people's behaviour is analysed and used to develop requirements for agents' behaviour. A meta-model is proposed to include components to address these requirements. A process outlining how to design and implement such organisations is presented. The meta-model draws on features in existing models in the literature and describes components to guide agents to behave with flexibility at run time. The thesis argues that coordinated agents benefit from an explicit representation of an organisational model and policies to guide agents' run time behaviour. Policies are proposed to maintain consistent knowledge and mutual plans between team members. Coordination is explicit and some flexibility is given to agents to improvise beyond the solution anticipated at design-time. Agents can mutually adjust individual plans to fit in with others so the multi-agent organisation is able to dynamically adapt to a changing environment. The meta-model and design approach is successfully demonstrated and validated using an implementation of a simulation system. In this demonstration system, agents in multiple organisations collaborate and coordinate to resolve a problem within an artificial simulation world.
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ItemOptimizing projection in the situation calculus( 2018)Among the most frequent reasoning tasks in the situation calculus are projection queries that query the truth of conditions in a future state of affairs. However, in long running action sequences involving thousands or millions of independent actions, solving the projection problem is complex. Existing approaches require either syntactically rewriting queries through each action that has occurred via a mechanism called regression or producing and maintaining an updated representation of the knowledge base via progression. This latter approach is often infeasible, as updating a knowledge base without loss of relevant information is not possible for many domains. This thesis introduces a new technique which allows the length of the action sequences to be reduced by reordering independent actions and removing dominated actions; maintaining semantic equivalence with respect to the original action theory. This transformation allows for the removal of actions that are problematic with respect to progression, allowing for periodic update of the action theory to reflect the current state of affairs. We provide the logical framework for the general case and give specific methods for important classes of action theories. We also show how more expressive cases may be handled, such as the reordering of sensing actions in order to delay progression. We investigate mechanisms for deciding which actions should be removed or reordered to improve the efficiency via a guided search and introduce appropriate heuristics. The end result is a method that allows long-running situation calculus based agents to reason more efficiently about their current and future situations.