Top-Quality Planning: Finding Practically Useful Sets of Best Plans

Abstract

The need for finding a set of plans rather than one has been motivated by a variety of planning applications. The problem is studied in the context of both diverse and top-k planning: while diverse planning focuses on the difference between pairs of plans, the focus of top-k planning is on the quality of each individual plan. Recent work in diverse planning introduced additionally restrictions on solution quality. Naturally, there are application domains where diversity plays the major role and domains where quality is the predominant feature. In both cases, however, the amount of produced plans is often an artificial constraint, and therefore the actual number has little meaning. Inspired by the recent work in diverse planning, we propose a new family of computational problems called top-quality planning, where solution validity is defined through plan quality bound rather than an arbitrary number of plans. Switching to bounding plan quality allows us to implicitly represent sets of plans. In particular, it makes it possible to represent sets of plans that correspond to valid plan reorderings with a single plan. We formally define the unordered top-quality planning computational problem and present the first planner for that problem. We empirically demonstrate the superior performance of our approach compared to a top-k planner-based baseline, ranging from 41% increase in coverage for finding all optimal plans to 69% increase in coverage for finding all plans of quality up to 120% of optimal plan cost. Finally, complementing the new approach by a complete procedure for generating all valid reorderings of a given plan, we derive a top-quality planner. We show the planner to be competitive with a top-k planner based baseline.

Reshaping Diverse Planning

“…Thus, in principle LPG-d might not be able to find a solution to the diversitybounded diverse planning problem when a solution exists. For the search problem bQ-k, note that it can be solved by post-processing solutions to the top-quality planning problem (Katz, Sohrabi, and Udrea 2020).…”

“…Recently, Vadlamudi and Kambhampati (2016) suggested "cost-sensitive" diverse planners, first finding all cost sensitive plans and then finding a diverse set of plans among these. Top-k planners (e.g., Katz et al 2018b) or top-quality planners (Katz, Sohrabi, and Udrea 2020) can also be viewed as diverse planners, purely addressing the quality metric.…”

From Robotic Process Automation to Intelligent Process Automation: Emerging Trends

“…Generative model-based approaches such as planning are uniquely situated to do this, finding applications in the robustification and adaptation of processes to failures [27], validation, verification, and monitoring of processes [37], and so on. A particular useful tool towards achieving this is referred to as top-k and diverse planning [30] where a set of solutions are computed instead of a single one thereby allowing one to anticipate likely ways a process may evolve. Such approaches have found many applications 1 in enterprise risk management and scenario planning recently.…”

Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems

“…This allows decision makers to see different options not quantified as part of the formulated objectives and see a wide range of option of achieving a desired goal. Recent investigations into this direction have gained some attention in the artificial intelligence community with respect to planning problems Katz, Sohrabi, and Udrea 2020). In the case of topk planning, the goal is to obtain a set of solutions such that no better solution outside that set exists (Katz et al 2018).…”