Lifted Successor Generation using Query Optimization Techniques

The standard PDDL language for classical planning uses sev eral first-order features, such as schematic actions. Yet, most classical planners ground this first-order representation into a propositional one as a preprocessing step. While this simpli fies the design of other parts of the planner, in several bench- marks the grounding process causes an exponential blowup that puts otherwise solvable tasks out of reach of the planners. In this work, we take a step towards planning with lifted representations . We tackle the successor generation task, a key operation in forward-search planning, directly on the lifted representation using well-known techniques from database theory . We show how computing the variable substitutions that make an action schema applicable in a given state is essentially a query evaluation problem. Interestingly, a large number of the action schemas in the standard benchmarks result in acyclic conjunctive queries, for which query evaluation is tractable. Our empirical results show that our approach is competitive with the standard (grounded) successor generation techniques in a few domains and outperforms them on benchmarks where grounding is challenging or infeasible

The Vadalog System: Datalog-based Reasoning for Knowledge Graphs

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowl\-edge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation.Comment: Extended version of VLDB paper <https://doi.org/10.14778/3213880.3213888

Generating Optimal Decision Functions from Rule Specifications

In this paper we sketch an approach and a tool for rapid evaluation of large systems of weighted decision rules. The tool re-implements the patented miAamics approach, originally devised as a fast technique for multicriterial decision support. The weighted rules are used to express performance critical decision functions. MiAamics optimizes the function and generates its efficient implementation fully automatically. Being declarative, the rules allow experts to define rich sets of complex functions without being familiar with any general purpose programming language. The approach also lends itself to optimize existing decision functions that can be expressed in the form of these rules.The proposed approach first transforms the system of rules into an intermediate representation of Algebraic Decision Diagrams. From this data structure, we generate code in a variety of commonly used target programming languages.We illustrate the principle and tools on a small, easily comprehensible example and present results from experiments with large systems of randomly generated rules. The proposed representation is significantly faster to evaluate and often of smaller size than the original representation. Possible miAamics applications to machine learning concern reducing ensembles of classifiers and allowing for a much faster evaluation of these classification functions. It can also naturally be applied to large scale recommender systems where performance is key

Exploring lifted planning encodings in Essence Prime

This work is supported by UK EPSRC EP/P015638/1 and EP/V027182/1, by the MICINN/FEDER, UE (RTI2018-095609-B-I00), by the French Agence Nationale de la Recherche, reference ANR-19-CHIA-0013-01, and by Archimedes institute, Aix-Marseille University.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.Publisher PD