A Time Leap Challenge for SAT Solving

We compare the impact of hardware advancement and algorithm advancement for SAT solving over the last two decades. In particular, we compare 20-year-old SAT-solvers on new computer hardware with modern SAT-solvers on 20-year-old hardware. Our findings show that the progress on the algorithmic side has at least as much impact as the progress on the hardware side.Comment: Authors' version of a paper which is to appear in the proceedings of CP'202

Hybrid conditional planning for service robotics

Planning is an indispensable ability for intelligent service robots operating in unstructured environments. Given service robots commonly have incomplete knowledge about and partial observability of handle such uncertainty. Moreover, the plans they compute should be feasible for real-world execution. Conditional planning is concerned with reaching goals from an initial state, in the presence of incomplete knowledge and partial observability; by utilizing sensing actions. Since all contingencies are considered in advance, a conditional plan is essentially a tree of actions where the root represents the initial state, leaves represent goal states, and each branch of the tree from the root to a leaf represents a possible execution of (deterministic) actuation actions and (non-deterministic) sensing actions to reach a goal state. Hybrid conditional planning extends conditional planning further by integrating lowlevel feasibility checks into executability conditions of actuation actions in conditional plans. We introduce a parallel offline algorithm called HCPlan, for computing hybrid conditional plans in robotics applications. HCPlan relies on modeling actuation actions and sensing actions in the causality-based action description language C+, and computation of the branches of a conditional plan in parallel using a SAT solver. In particular, thanks to external atoms, continuous feasibility checks (such as collision and reachability checks) are embedded into causal laws representing actuation actions and sensing actions; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing causal laws that describe iv non-deterministic effects of actions, sensing actions can be explicitly formalized; and thus each branch of a conditional plan can be computed without necessitating an ordering of sensing actions in advance. Furthermore, we introduce two different extensions of our hybrid conditional planner HCPlan: HCPlan-Anytime and HCPlan-Reactive. HCPlan-Anytime computes a partial hybrid conditional plan within a given time, by generating the branches with respect to their probability of execution. HCPlan-Reactive computes a hybrid conditional plan with a receding horizon. These extensions trade-off completeness of hybrid conditional plans for improved computation time, and provide useful important variations towards real-time use of the hybrid conditional planning. We develop comprehensive benchmarks for service robotics domain and evaluate our approach over these benchmarks with extensive experiments in terms of computational efficiency and plan quality. We compare HCPlan with other related conditional planners and approaches. We further demonstrate the usefulness of our approach in service robotics applications through dynamic simulations and physical implementations