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Benjamin Krarup

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4 papers

2 author rows

HAXP Workshop 2025 Workshop Paper

How Humans Explain the Difference in the Quality of Plans -- A User Study

Benjamin Krarup
Amanda Jane Coles
Dancheng Gao
Derek Long
David E. Smith

Recent advances in plan explanation have used abstractions to produce explanations. We consider the task of explaining why there is a difference in the quality of plans produced for a planning problem, $\Pi$, and the same problem constrained in some way, $\Pi + c$. The method involves abstracting away details of the planning problems until the difference in the quality of plans they support is minimised. It is not known whether humans use abstractions to explain these differences, and if so, what types of properties these abstractions have. We present the results of a qualitative user study investigating this. We tasked participants with explaining the difference in the quality of plans and found that users do indeed use abstractions to explain differences. We extract a set of properties that these abstractions satisfy, which can be used in automatic abstraction for explanation generation.

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ICAPS Conference 2024 Conference Paper

Explaining Plan Quality Differences

Benjamin Krarup
Amanda Jane Coles
Derek Long
David E. Smith 0001

We describe a method for explaining the differences between the quality of plans produced for similar planning problems. The method exploits a process of abstracting away details of the planning problems until the difference in solution quality they support has been minimised. We give a general definition of a valid abstraction of a planning problem. We then give the details of the implementation of a number of useful abstractions. Finally, we present a breadth-first search algorithm for finding suitable abstractions for explanations; and detail the results of an evaluation of the approach.

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JAIR Journal 2021 Journal Article

Contrastive Explanations of Plans through Model Restrictions

Benjamin Krarup
Senka Krivic
Daniele Magazzeni
Derek Long
Michael Cashmore
David E. Smith

In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user’s expectation. We frame Explainable AI Planning as an iterative plan exploration process, in which the user asks a succession of contrastive questions that lead to the generation and solution of hypothetical planning problems that are restrictions of the original problem. The object of the exploration is for the user to understand the constraints that govern the original plan and, ultimately, to arrive at a satisfactory plan. We present the results of a user study that demonstrates that when users ask questions about plans, those questions are usually contrastive, i.e. “why A rather than B?”. We use the data from this study to construct a taxonomy of user questions that often arise during plan exploration. Our approach to iterative plan exploration is a process of successive model restriction. Each contrastive user question imposes a set of constraints on the planning problem, leading to the construction of a new hypothetical planning problem as a restriction of the original. Solving this restricted problem results in a plan that can be compared with the original plan, admitting a contrastive explanation. We formally define model-based compilations in PDDL2.1 for each type of constraint derived from a contrastive user question in the taxonomy, and empirically evaluate the compilations in terms of computational complexity. The compilations were implemented as part of an explanation framework supporting iterative model restriction. We demonstrate its benefits in a second user study.

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IROS Conference 2018 Conference Paper

Strategic-Tactical Planning for Autonomous Underwater Vehicles over Long Horizons

Dorian Buksz
Michael Cashmore
Benjamin Krarup
Daniele Magazzeni
Bram Ridder

In challenging environments where human intervention is expensive, robust and persistent autonomy is a key requirement. AI Planners can efficiently construct plans to achieve this long-term autonomous behaviour. However, in plans which are expected to last over days, or even weeks, the size of the state-space becomes too large for current planners to solve as a single problem. These problems are well-suited to decomposition and abstraction planning techniques. We present a novel approach in the context of persistent autonomy in autonomous underwater vehicles, in which tasks are complex and diverse and plans cannot be precomputed. Our approach performs a decomposition into a two-level hierarchical structure, which dynamically constructs planning problems at the upper level of the hierarchy using solution plans from the lower level. Solution plans are then executed and monitored simultaneously at both levels. We evaluate the approach, showing that compared to strictly top-down hierarchical decompositions, our approach leads to more robust solution plans of higher quality.

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