Workshop on Heuristics and Search for Domain-independent Planning (HSDIP)

Collocated with ICAPS 2019 in Berkeley, USA.

Heuristics and search algorithms are the two key components of heuristic search, one of the main approaches to many variations of domain-independent planning, including classical planning, temporal planning, planning under uncertainty and adversarial planning. This workshop seeks to understand the underlying principles of current heuristics and search methods, their limitations, ways for overcoming those limitations, as well as the synergy between heuristics and search. The workshop proceedings are available for download here. The workshop will take place in Room 242 on Thursday, July 11. All presentations are given 20 minutes, including 5 minutes for questions.

Session 1: Probabilistic, Temporal, and Online Planning
Opening Remarks
Width-Based Lookaheads Augmented with Base Policies for Stochastic Shortest Paths
Guiding MCTS with Generalized Policies for Probabilistic Planning
Beyond Cost-to-go Estimates in Situated Temporal Planning
Simultaneous Re-Planning and Plan Execution for Online Job Arrival
10:30Coffee Break
Session 2: Classical Planning
Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases
Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning
Merge-and-Shrink Task Reformulation for Classical Planning
Learning How to Ground a Plan - Partial Grounding in Classical Planning
12:30Lunch Break
Session 3: Task Reformulations, Goal Recognition
A* Search and Bound-Sensitive Heuristics for Oversubscription Planning
Information Shaping for Enhanced Goal Recognition of Partially-Informed Agents
Reshaping Diverse Planning: Let There Be Light!
Top-Quality: Finding Practically Useful Sets of Best Plans

Search guided by heuristics, automatically derived from a declarative formulation of action effects, preconditions and goals, has been a successful approach to domain-independent planning. From the initial success of heuristics based on syntactic relaxations and abstractions, the theory and practice of developing novel heuristics have become more diverse, often borrowing concepts and tools from Optimisation and Satisfiability, and bolder, tackling more expressive planning languages.

In parallel to the increasing maturity of the methods and tools used to derive heuristic methods, important theoretical results have brought around a more clear image of how heuristic methods relate to each other. For instance, it has been shown that classic frameworks for heuristic search as planning can be encoded symbolically and their execution simulated via off-the-shelf satisfiability solvers. Groundbreaking theoretical work has shown how heuristic methods can be grouped into distinct families, depending on whether they can or cannot be shown to dominate or be compiled into each other.

As a result, the formulation of heuristics for domain-independent planning is increasingly being less about describing procedures that exploit specific features in declarative information, and more about describing auxiliary constraints that make apparent those features to off-the-shelf solvers that operate over a logical or algebraic theory that over-approximate the set of valid plans and compute the heuristic estimator.

Last, but not least, there is a growing realization that the search algorithm used can significantly amplify or reduce the utility of specific heuristics. Recent work that highlights the pitfalls latent in well-known search algorithms, also suggests opportunities to exploit synergies between the heuristic calculation and the search control.

The workshop on Heuristics and Search for Domain-Independent Planning (HSDIP) is the 11th workshop in a series that started with the "Heuristics for Domain-Independent Planning" (HDIP) workshops at ICAPS 2007. At ICAPS 2012, the workshop was changed to its current name and scope to explicitly encourage work on search for domain-independent planning.

Examples of typical topics for submissions to this workshop are: The HSDIP workshop has always been welcoming of multidisciplinary work, for example, drawing inspiration from operations research (like row and column generation algorithms), convex optimization (like gradient optimization for hybrid planning), constraint programming or satisfiability, or applications of machine learning in heuristic search (e.g. learning heuristics, adaptive search strategies, or heuristic selection). We will keep this stance, particularly as ICAPS 2019 will continue the special track on planning & learning. Please format submissions in AAAI style (see instructions in the Author Kit at and keep them to at most 9 pages including references. Authors considering submitting to the workshop papers rejected from the main conference, please ensure you do your utmost to address the comments given by ICAPS reviewers. Please do not submit papers that are already accepted for the main conference to the workshop.

Submissions will be made through OpenReview. The following conditions apply: Every submission will be reviewed by two members of the organizing committee according to the usual criteria such as relevance to the workshop, significance of the contribution, and technical quality.

Submissions sent to other conferences are allowed. It is the responsibility of the authors to ensure that those venues allow for papers submitted to be already published in "informal" ways (e.g. on proceedings or websites without associated ISSN/ISBN).

The workshop is meant to be an open and inclusive forum, and we encourage papers that report on work in progress or that do not fit the mold of a typical conference paper. Non-trivial negative results are welcome to the workshop, but we expect the authors to argue for the significance of the presented results to alternative lines of research on the topic of choice.

At least one author of each accepted paper must attend the workshop in order to present the paper. Authors must register for the ICAPS main conference in order to attend the workshop. There will be no separate workshop-only registration.