Real-World Planning with PDDL+ and Beyond

Real-world applications of AI Planning often require a highly expressive modeling language to accurately capture important intricacies of target systems. Hybrid systems are ubiquitous in the real-world, and PDDL+ is the standardized modeling language for capturing such systems as planning domains. PDDL+ enables accurate encoding of mixed discretecontinuous system dynamics, exogenous activity, and many other interesting features exhibited in realistic scenarios. However, the uptake in usage of PDDL+ has been slow and apprehensive, largely due to a general shortage of PDDL+ planning software, and rigid limitations of the few existing planners. To overcome this chasm, we present Nyx, a novel PDDL+ planner built to emphasize lightness, simplicity, and, most importantly, adaptability. The planner is designed to be effortlessly customizable to expand its capabilities well beyond the scope of PDDL+. As a result, Nyx can be tailored to virtually any potential real-world application requiring some form of AI Planning, paving the way for wider adoption of planning methods for solving real-world problems.

Introduction. Realistic planning problems require an expressive modeling language to accurately capture the innate intricacies of the modeled scenario. Indeed, most existing domainindependent planning languages, such a STRIPS (Fikes and Nilsson 1971), ADL (Pednault 1989), PDDL (McDermott et al. 1998), and even PDDL2.1 (Fox and Long 2003), lack features to describe commonplace elements of real-world systems. As a result, the vast majority of planning models are severely abstracted or limited in scope, often being forced to ignore aspects of the domain that, in the real world, have significant impact on the system’s operations such as environmental phenomena. PDDL+ (Fox and Long 2006) is one of the most expressive modeling languages and was designed to model mixed discrete/continuous (hybrid) systems. PDDL+ is, arguably, the closest AI Planning has come to accurately representing realistic scenarios as planning domains. It has proved useful for capturing a wide range challenging AI domains from traffic control(Vallati et al. 2016) and physicsbased games(Piotrowski et al.

Discussion / Conclusion. Real-world systems are challenging for AI with respect to modeling and solving them. Currently available PDDL+ planners have a steep learning curve and require expert knowledge. Nyx aims to increase the accessibility to AI planning, particularly for realistic feature-rich domains, by focusing on simplicity and adaptability. However, such advantages come at a cost of raw computational performance compared to complex optimized code and low-level languages such as C/C++. For transparency, Table 2 shows comparison with UPMurphi and ENHSP2 (both using blind BFS) in representative speed of state space exploration in nodes per second. Crucially, it should be noted that Nyx addresses a different classes of problems from the other planners. Our planner facilitates rapid prototyping of planner extensions and novel heuristics, solving emerging classes of domains which cannot be handled by any established planners, and reasoning with composite PDDL+ models with external functions & features.