Diverse planning is the problem of generating plans with distinct
characteristics. This is valuable for many real-world scenarios, including
applications related to plan recognition and business process automation. In
this work, we introduce \emph{Behaviour Planning}, a diverse planning toolkit
that can characterise and generate diverse plans based on modular diversity
models. We present a qualitative framework for describing diversity models, a
planning approach for generating plans aligned with any given diversity model,
and provide a practical implementation of an SMT-based behaviour planner. We
showcase how the qualitative approach offered by Behaviour Planning allows it
to overcome various challenges faced by previous approaches. Finally, the
experimental evaluation shows the effectiveness of Behaviour Planning in
generating diverse plans compared to state-of-the-art approaches.

行为规划是一个多样化规划工具包，它可以基于模块化的多样性模型来表征和生成多样化的计划。与现有方法相比，行为规划通过定性方法克服了各种挑战，并在生成多样化计划方面取得了有效性的实验评估结果。

行为规划：多样化规划的工具包

Behaviour Planning: A Toolkit for Diverse Planning

Diverse planning is the problem of finding multiple plans for a given problem
specification, which is at the core of many real-world applications. For
example, diverse planning is a critical piece for the efficiency of plan
recognition systems when dealing with noisy and missing observations. Providing
diverse solutions can also benefit situations where constraints are too
expensive or impossible to model. Current diverse planners operate by
generating multiple plans and then applying a selection procedure to extract
diverse solutions using a similarity metric. Generally, current similarity
metrics only consider the structural properties of the given plans. We argue
that this approach is a limitation that sometimes prevents such metrics from
capturing why two plans differ. In this work, we propose two new
domain-independent metrics which are able to capture relevant information on
the difference between two given plans from a domain-dependent viewpoint. We
showcase their utility in various situations where the currently used metrics
fail to capture the similarity between plans, failing to capture some
structural symmetries.

多样化规划是寻找给定问题规范的多个解决方案的问题，它在许多实际应用程序中至关重要。本文提出了两种新的领域无关度量指标，能够从领域相关的角度捕捉两个给定方案之间的差异，并展示了它们在当前使用的度量指标无法捕捉到方案之间相似性和结构对称性的各种情况下的实用性。

结构与语义相似度在多元规划中的桥梁

Bridging the Gap between Structural and Semantic Similarity in Diverse  Planning

Diverse, top-k, and top-quality planning are concerned with the generation of
sets of solutions to sequential decision problems. Previously this area has
been the domain of classical planners that require a symbolic model of the
problem instance. This paper proposes a novel alternative approach that uses
Monte Carlo Tree Search (MCTS), enabling application to problems for which only
a black-box simulation model is available. We present a procedure for
extracting bounded sets of plans from pre-generated search trees in best-first
order, and a metric for evaluating the relative quality of paths through a
search tree. We demonstrate this approach on a path-planning problem with
hidden information, and suggest adaptations to the MCTS algorithm to increase
the diversity of generated plans. Our results show that our method can generate
diverse and high-quality plan sets in domains where classical planners are not
applicable.

使用蒙特卡洛树搜索方法来解决无法使用经典规划器解决的问题，并在路径规划问题中演示了该方法可以生成多样化和高质量的计划集合。