Improving domainindependent intention selection i

Authors: Max Waters Lin Padgham Sebastian Sardina

Publish Date: 2015/03/21

Volume: 29, Issue: 4, Pages: 683-717

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Abstract

The Belief Desire Intention BDI agent paradigm provides a powerful basis for developing complex systems based on autonomous intelligent agents These agents have at any point in time a set of intentions encoding the various tasks the agent is working on Despite its importance the problem of selecting which intention to progress at any point in time has received almost no attention and has been mostly left to the programmer to resolve in an applicationdependent manner In this paper we implement and evaluate two domainindependent intention selection mechanisms based on the ideas of enablement checking and low coverage prioritisation Through a battery of automatically generated synthetic tests and one real program we compare these with the commonly used intention selection mechanisms of FirstInFirstOut FIFO and Round Robin RR We found that enablement checking which is incorporated into low coverage prioritisation is never detrimental and provides substantial benefits when running vulnerable programs in dynamic environments This is a significant finding as such a check can be readily applied to FIFO and RR giving an extremely simple and effective mechanism to be added to existing BDI frameworks In turn low coverage prioritisation provides a significant further benefitWe acknowledge the support of the Australian Research Council under Discovery Project DP1094627 and Agent Oriented Software for providing us with a Jack license We would also like to thank the anonymous reviewers for their useful comments Part of this work was done while the third author was on sabbatical at Sapienza Universita’ di Roma Rome ItalyThe goalplan trees induced by the agent’s plan library can be considered perfect binary trees which have had selected branches “pruned” in order to create coverage gaps We define the depth of a goalplan tree according to the maximum depth to which subgoals are posted The toplevel goal is at depth 0 a plan relevant to a goal at depth d is also at depth d and subgoals posted by plans at depth d are at depth d+1If a goalplan tree has a gap level at depth d then for every subgoal G at depth d there is at least one possible world state where G has no applicable plan These coverage gaps are modelled on peffects ie G is handled by a single plan G phi leftarrow delta and its context condition phi is set to true in the plan which posted G The toplevel goal has no coverage gaps so in a tree with depth d any gap layers must be exist between depths 1 and d So if a goalplan tree has a depth of d and g gap levels the number of possible combinations of gap layers is left beginarraycd gendarrayright = fracddggIn these experiments an agent is tasked with achieving 10 toplevel goals each of which can be decomposed into a binary goalplan tree with a maximum depth of 4 and 2 gap levels There are therefore 6 possible combinations of gap levels available In order to have an even distribution of such structures the gap layers in each of the ten goalplan trees is randomly selected before each test

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