2 research outputs found
CAMP-BDI: an approach for multiagent systems robustness through capability-aware agents maintaining plans
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 in 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 recovery
and, potentially, hinder subsequent activity. A further factor is that BDI agents typically
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 reasoning
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 capability
information for general advertisement and of dependency information - we define
use of the latter, within a structured messaging approach, to extend local agent algorithms
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 system
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