@INPROCEEDINGS{Meneguzzi2010b:Representing,
author={Felipe Meneguzzi and Yuqing Tang and Katia Sycara and Simon Parsons},
title={On representing planning domains under uncertainty},
booktitle={The Fourth Annual Conference of the International Technology Alliance},
address={London, UK},
year={2010},
abstract={Planning is an important activity in military coalitions and automated
planning tool support could alleviate cognitive burden of users. Current AI
planning paradigms use two different formalisms to represent the planning
problem. Each of these formalisms entails different inference algorithms
and representation of results. On the one hand plans in non-stochastic
domains are represented using declarative logic-based formalisms, an
example of which is Hierarchical Task Networks (HTNs). In HTNs, domains are
represented in terms of task decompositions of increased detail in relation
to the actions that must be carried out. In general, declarative formalisms
are easier for humans to understand. On the other hand, stochastic planning
is often represented in terms of large probability functions that
exhaustively specify the likelihood of relevant world changes when actions
are executed, as exemplified by Markov Decision Processes (MDPs).
Stochastic domain specifications can easily become challenging to a human
designer as the problem size increases, worse still, solver algorithms
degrade quickly with increased domain size. In order to facilitate domain
modeling for planning under uncertainty, we propose a method of deriving
stochastic domain specifications in the MDP formalism from a description
using the HTN formalism. This method can reduce the resulting MDP
state-space through an intermediate representation using Binary Decision
Diagrams (BDDs). The benefits of the approach are twofold: (a) the
reduction of the state space, and consequent reduction of computational
burden is beneficial since it enables the representation and solving of
realistic planning problems, and (b) starting from a declarative
representation makes planning more comprehensible to humans, while
extending the representation to stochastic domains.}
}