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Max Waters

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3 papers

2 author rows

IJCAI Conference 2020 Conference Paper

Optimising Partial-Order Plans Via Action Reinstantiation

Max Waters
Lin Padgham
Sebastian Sardina

This work investigates the problem of optimising a partial-order plan’s (POP) flexibility through the simultaneous transformation of its action ordering and variable binding constraints. While the former has been extensively studied through the notions of deordering and reordering, the latter has received much less attention. We show that a plan’s variable bindings are often related to resource usage and their reinstantiation can yield more flexible plans. To do so, we extend existing POP optimality criteria to support variable reinstantiation, and prove that checking if a plan can be optimised further is NP-complete. We also propose a MaxSAT-based technique for increasing plan flexibility and provide a thorough experimental evaluation that suggests that there are benefits in action reinstantiation.

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ICAPS Conference 2018 Conference Paper

Plan Relaxation via Action Debinding and Deordering

Max Waters
Bernhard Nebel
Lin Padgham
Sebastian Sardiña

While seminal work has studied the problem of relaxing the ordering of a plan’s actions, less attention has been given to the problem of relaxing and modifying a plan’s variable bindings. This paper studies the problem of relaxing a plan into a partial plan which specifies which operators must be executed, but need not completely specify their order or variable bindings. While partial plans can provide an agent with additional flexibility and robustness at execution time, many operations over partial plans are intractable. This paper tackles this problem by proposing and empirically evaluating a fixed-parameter tractable algorithm which searches for tractable, flexible partial plans.

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JAAMAS Journal 2015 Journal Article

Improving domain-independent intention selection in BDI systems

Max Waters
Lin Padgham
Sebastian Sardina

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 application-dependent manner. In this paper, we implement and evaluate two domain-independent 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 First-In-First-Out ( 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 benefit.

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