4 research outputs found
Argumentation-based methods for multi-perspective cooperative planning
Through cooperation, agents can transcend their individual capabilities and achieve
goals that would be unattainable otherwise. Existing multiagent planning work considers
each agent’s action capabilities, but does not account for distributed knowledge
and the incompatible views agents may have of the planning domain. These divergent
views can be a result of faulty sensors, local and incomplete knowledge, and outdated
information, or simply because each agent has conducted different inferences and their
beliefs are not aligned.
This thesis is concerned with Multi-Perspective Cooperative Planning (MPCP), the
problem of synthesising a plan for multiple agents which share a goal but hold different
views about the state of the environment and the specification of the actions they can
perform to affect it. Reaching agreement on a mutually acceptable plan is important,
since cautious autonomous agents will not subscribe to plans that they individually
believe to be inappropriate or even potentially hazardous.
We specify the MPCP problem by adapting standard set-theoretic planning notation.
Based on argumentation theory we define a new notion of plan acceptability, and
introduce a novel formalism that combines defeasible logic programming and situation
calculus that enables the succinct axiomatisation of contradictory planning theories and
allows deductive argumentation-based inference.
Our work bridges research in argumentation, reasoning about action and classical
planning. We present practical methods for reasoning and planning with MPCP
problems that exploit the inherent structure of planning domains and efficient planning
heuristics. Finally, in order to allow distribution of tasks, we introduce a family of
argumentation-based dialogue protocols that enable the agents to reach agreement on
plans in a decentralised manner.
Based on the concrete foundation of deductive argumentation we analytically investigate
important properties of our methods illustrating the correctness of the proposed
planning mechanisms. We also empirically evaluate the efficiency of our algorithms
in benchmark planning domains. Our results illustrate that our methods can
synthesise acceptable plans within reasonable time in large-scale domains, while maintaining
a level of expressiveness comparable to that of modern automated planning