Exploring Lifted Planning Encodings in Essence Prime

State-space planning is the de-facto search method of the automated planning community. Planning problems are typically expressed in the Planning Domain Definition Language (PDDL), where action and variable templates describe the sets of actions and variables that occur in the problem. Typically, a planner begins by generating the full set of instantiations of these templates, which in turn are used to derive useful heuristics that guide the search. Thanks to this success, there has been limited research in other directions. We explore a different approach, keeping the compact representation by directly reformulating the problem in PDDL into ESSENCE PRIME, a Constraint Programming language with support for distinct solving technologies including SAT and SMT. In particular, we explore two different encodings from PDDL to ESSENCE PRIME, how they represent action parameters, and their performance. The encodings are able to maintain the compactness of the PDDL representation, and while they differ slightly, they perform quite differently on various instances from the International Planning Competition.

Social shifts in the dynamics of the population of the USSR

The reports made by representatives of the USSR at the International Demographic Congress in Rome and at the International Planning Congress in Amsterdam are of great interest to the general medical community. Knowledge of the social changes that have taken place in our country, as a result of the implementation of the five-year plan for the socialist reconstruction of our economy, will provide each doctor with an understanding of the immediate tasks facing healthcare at this stage.

Walking with Youth in Los Platanitos: Learning Mobilities, Youth Inclusion, and Co-production in International Planning Studios

This article examines the role of mobile spaces to foster trans-disciplinary learning in international planning studios. Drawing from Indigenous ontologies and critical pedagogy, we suggest that walking and learning together between students and partners is critical to situated understandings of power and co-production of knowledge. We reflect on two years of studio courses in Santo Domingo, Dominican Republic, to illustrate three different modalities of walking-learning spaces, each of which have different learning outcomes for students and youth participants. We find that mobile “third” spaces enable students to traverse social, geographic, and epistemological boundaries, while allowing youth’s planning priorities to emerge.

Efficient Lifted Planning with Regression-Based Heuristics

Modern state-of-the-art planners operate by generating a grounded transition system prior to performing search for a solution to a given planning task. Some tasks involve a significant number of objects or entail managing predicates and action schemas with a significant number of arguments. Hence, this instantiation procedure can exhaust all available memory and therefore prevent a planner from performing search to find a solution. This thesis explores this limitation by presenting a benchmark set of problems based on Organic Chemistry Synthesis that was submitted to the latest International Planning Competition (IPC-2018). This benchmark was constructed to gauge the performance of the competing planners given that instantiation is an issue. Furthermore, a novel algorithm, the Regression-Based Heuristic Planner (RBHP), is developed with the aim of averting this issue. RBHP was inspired by the retro-synthetic approach commonly used to solve organic synthesis problems efficiently. RBHP solves planning tasks by applying domain independent heuristics, computed by regression, and performing best-first search. In contrast to most modern planners, RBHP computes heuristics backwards by applying the goal-directed regression operator. However, the best-first search proceeds forward similar to other planners. The proposed planner is evaluated on a set of planning tasks included in previous International Planning Competitions (IPC) against a subset of the top scoring state-of-the-art planners submitted to the IPC-2018.

Efficient Lifted Planning with Regression-Based Heuristics

Modern state-of-the-art planners operate by generating a grounded transition system prior to performing search for a solution to a given planning task. Some tasks involve a significant number of objects or entail managing predicates and action schemas with a significant number of arguments. Hence, this instantiation procedure can exhaust all available memory and therefore prevent a planner from performing search to find a solution. This thesis explores this limitation by presenting a benchmark set of problems based on Organic Chemistry Synthesis that was submitted to the latest International Planning Competition (IPC-2018). This benchmark was constructed to gauge the performance of the competing planners given that instantiation is an issue. Furthermore, a novel algorithm, the Regression-Based Heuristic Planner (RBHP), is developed with the aim of averting this issue. RBHP was inspired by the retro-synthetic approach commonly used to solve organic synthesis problems efficiently. RBHP solves planning tasks by applying domain independent heuristics, computed by regression, and performing best-first search. In contrast to most modern planners, RBHP computes heuristics backwards by applying the goal-directed regression operator. However, the best-first search proceeds forward similar to other planners. The proposed planner is evaluated on a set of planning tasks included in previous International Planning Competitions (IPC) against a subset of the top scoring state-of-the-art planners submitted to the IPC-2018.

Compilation-Based Approaches to Parallel Planning: An Empirical Comparison

Automated planning deals with finding a sequence of actions, a plan, to reach a goal. One of the possible approaches to automated planning is a compilation of a planning problem to a Boolean satisfiability problem or to a constraint satisfaction problem, which takes direct advantage of the advancements of satisfiability and constraint satisfaction solvers. This paper provides a comparison of three encodings proposed for the compilation of planning problems: Transition constraints for parallel planning (TCPP), Relaxed relaxed exist-Step encoding and Reinforced Encoding. We implemented the encodings using the programming language Picat 2.8, we suggested certain modifications, and we compared the performance of the encodings on benchmarks from international planning competitions.

Lilotane: A Lifted SAT-based Approach to Hierarchical Planning

One of the oldest and most popular approaches to automated planning is to encode the problem at hand into a propositional formula and use a Satisfiability (SAT) solver to find a solution. In all established SAT-based approaches for Hierarchical Task Network (HTN) planning, grounding the problem is necessary and oftentimes introduces a combinatorial blowup in terms of the number of actions and reductions to encode. Our contribution named Lilotane (Lifted Logic for Task Networks) eliminates this issue for Totally Ordered HTN planning by directly encoding the lifted representation of the problem at hand. We lazily instantiate the problem hierarchy layer by layer and use a novel SAT encoding which allows us to defer decisions regarding method arguments to the stage of SAT solving. We show the correctness of our encoding and compare it to the best performing prior SAT encoding in a worst-case analysis. Empirical evaluations confirm that Lilotane outperforms established SAT-based approaches, often by orders of magnitude, produces much smaller formulae on average, and compares favorably to other state-of-the-art HTN planners regarding robustness and plan quality. In the International Planning Competition (IPC) 2020, a preliminary version of Lilotane scored the second place. We expect these considerable improvements to SAT-based HTN planning to open up new perspectives for SAT-based approaches in related problem classes.

Evaluating an International Facial Trauma Course for Surgeons: Did We Make a Difference?

Study Design: Retrospective data analysis study. Objective: Attending continuing professional development (CPD) and continuing medical education (CME) activities is a necessity for practicing surgeons in most parts of the world. To enhance best practices in conducting CME/CPD, objective evaluation of these events is crucial. This article aims to evaluate one such international standardized CPD course conducted for facial surgeons across the globe. The Management of Facial Trauma course was developed by an international planning committee of experienced surgeons and has been implemented in all regions of the world. Method: This 2-day course is delivered using a combination of short lectures, small group discussions, and practical hands-on activities. Data collected from pre- and post-course evaluations of 86 Management of Facial Trauma courses conducted worldwide from 2017-2019 were collated and analyzed. Results: Participant demographics and experience levels varied slightly across the regions. Evaluation of the course effectiveness revealed overall high ratings for educational impact, content usefulness, and faculty performance. Conclusion: Our results indicated that this standardized course met the audience needs and enabled participants to plan changes in clinical practice. In addition, it confirmed that the course was relevant across different specialties and across different cultures and countries.