Doctor of Philosophy
Centre for Intelligent Systems and their Applications
School of Informatics
University of Edinburgh
[PDF Format ] (submitted March 2017)
Rational agent behaviour is frequently achieved through the use of plans, particularly within the widely used BDI (Belief-Desire-Intention) model for intelligent agents. As a consequence, preventing or handling failure of planned activity is a vital component in building robust multiagent systems; this is especially true for realistic environments, where unpredictable exogenous change during plan execution may threaten intended activities.
Although reactive approaches can be employed to respond to activity failure through replanning or plan-repair, failure may have debilitative effects that act to stymie recov- ery and, potentially, hinder subsequent activity. A further factor is that BDI agents typ- ically employ deterministic world and plan models, as probabilistic planning methods are typical intractable in realistically complex environments. However, deterministic operator preconditions may fail to represent world states which increase the risk of activity failure.
The primary contribution of this thesis is the algorithmic design of the CAMP-BDI (Capability Aware, Maintaining Plans) approach; a modification of the BDI reason- ing cycle which provides agents with beliefs and introspective reasoning to anticipate increased risk of failure and pro-actively modify intended plans in response.
We define a capability meta-knowledge model, providing information to identify and address threats to activity success using precondition modelling and quantitative quality estimation. This also facilitates semantic-independent communication of capa- bility information for general advertisement and of dependency information - we define use of the latter, within a structured messaging approach, to extend local agent algo- rithms towards decentralized, distributed robustness. Finally, we define a policy based approach for dynamic modification of maintenance behaviour, allowing response to observations made during runtime and with potential to improve re-usability of agents in alternate environments.
An implementation of CAMP-BDI is compared against an equivalent reactive sys- tem through experimentation in multiple perturbation configurations, using a logistics domain. Our empirical evaluation indicates CAMP-BDI has significant benefit if activity failure carries a strong risk of debilitative consequence.