On Different Strategies for Eliminating Redundant Actions from Plans

Satisficing planning engines are often able to generate plans in a reasonable time, however, plans are often far from optimal. Such plans often contain a high number of redundant actions, that are actions, which can be removed without affecting the validity of the plans. Existing approaches for determining and eliminating redundant actions work in polynomial time, however, do not guarantee eliminating the "best" set of redundant actions, since such a problem is NP-complete. We introduce an approach which encodes the problem of determining the "best" set of redundant actions (i.e. having the maximum total-cost) as a weighted MaxSAT problem. Moreover, we adapt the existing polynomial technique which greedily tries to eliminate an action and its dependants from the plan in order to eliminate more expensive redundant actions. The proposed approaches are empirically compared to existing approaches on plans generated by state-of-the-art planning engines on standard planning benchmark

Unit propagation with stable watches

Unit propagation is the hottest path in CDCL SAT solvers, therefore the related data-structures, algorithms and implementation details are well studied and highly optimized. State-of-the-art implementations are based on reduced occurrence tracking with two watched literals per clause and one blocking literal per watcher in order to further reduce the number of clause accesses. In this paper, we show that using runtime statistics for watched literal selection can improve the performance of state-of-the-art SAT solvers. We present a method for efficiently keeping track of spans during which literals are satisfied and using this statistic to improve watcher selection. An implementation of our method in the SAT solver CaDiCaL can solve more instances of the SAT Competition 2019 and 2020 benchmark sets and is specifically strong on satisfiable cryptographic instances

Explainable AI for Constraint-Based Expert Systems

The need to derive explanations from machine learning (ML)-based AI systems has been addressed in recent research due to the opaqueness of their processing.However, a significant amount of productive AI systems are not based on ML but are expert systems including strong opaqueness.A resulting lack of understanding causes massive inefficiencies in business processes that involve opaque expert systems. This work uses recent research interest in explainable AI (XAI) to generate knowledge for the design of explanations in constraint-based expert systems.Following the Design Science Research paradigm, we develop design requirements and design principles. Subsequently, we design an artifact and evaluate the artifact in two experiments. We observe the following phenomena. First, global explanations in a textual format were well-received. Second, abstract local explanations improved comprehensibility. Third, contrastive explanations successfully assisted in the resolution of contradictions. Finally, a local tree-based explanation was perceived as challenging to understand

REINFORCED ENCODING FOR PLANNING AS SAT

Solving planning problems via translation to satisfiability (SAT) is one of the most successful approaches to automated planning. We propose a new encoding scheme, called Reinforced Encoding, which encodes a planning problem represented in the SAS+ formalism into SAT. The Reinforced Encoding is a combination of the transition-based SASE encoding with the classical propositional encoding. In our experiments we compare our new encoding to other known SAS+ based encodings. The results indicate, that he Reinforced encoding performs well on the benchmark problems of the 2011 International Planning Competition and can outperform all the other known encodings for several domains

Modelling and Solving Problems Using SAT Techniques

Řešení problémů plánování prostřednictvím překladů do splnitelnosti (SAT) je jedním z nejúspěšnějších přístupů k automatickému plánování. V této práci popíšeme několik způsobů jak přeložit problém plánování reprezentovaný v SAS+ formalismu do SAT. Přezkoumáme a přizpůsobíme stávající kódování a také zavedeme nové vlastní způsoby kódování. Porovnáme jednotlivá kódování pomocí výpočtu horních odhadů na velikosti formulí, které produkují, a pomocí spuštění rozsáhlých experimentů na referenčních problémech z Mezinárodní plánovací soutěže 2011. V experimentální části také porovnáme své kódování s nejmodernejšími kódováními z plánovače Madagascar. Experimenty ukazují, že naše techniky dokažou překonat tato kódování. V předložené práci také řešíme speciální případ optimalizace plánů -- odstranění redundantních akcí. Odstranění všech redundantních akcí je NP- úplný problém. Prostudujeme existující polynomialní heuristické přístupy a navrhneme vlastní heuristický přístup, který dokaže eliminovat vyšší počet a dražší redundantní akce než stávající techniky. Také navrhneme způsob kódování problému redundance plánů do SAT, který nám za použití MaxSAT řešičů umožní optimálně vyřešit problém eliminace redundantních akcí. Naše experimenty provedené s plány od nejmodernejších satisficing plánovačů pro referenční problémy...Solving planning problems via translation to satisfiability (SAT) is one of the most successful approaches to automated planning. In this thesis we describe several ways of encoding a planning problem represented in the SAS+ formalism into SAT. We review and adapt existing encoding schemes as well as introduce new original encodings. We compare the encodings by calculating upper bounds on the size of the formulas they produce as well as by running extensive experiments on benchmark problems from the 2011 International Planning Competition (IPC). In the experimental section we also compare our encodings with the state-of-the-art encodings of the planner Madagascar. The experiments show, that our techniques can outperform these state-of-the-art encodings. In the presented thesis we also deal with a special case of post-planning optimization -- elimination of redundant actions. The elimination of all redundant actions is NP-complete. We review the existing polynomial heuristic approaches and propose our own heuristic approach which can eliminate a higher number and more costly redundant actions than the existing techniques. We also propose a SAT encoding for the problem of plan redundancy which together with MaxSAT solvers allows us to solve the problem of action elimination optimally. Experiments done with...Katedra teoretické informatiky a matematické logikyDepartment of Theoretical Computer Science and Mathematical LogicMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Fast approximate calculation of valid domains in a satisfiability-based product configurator

Calculating valid domains is an important feature of an interactive product configurator. Since it is an NP hard problem, it is necessary (for large real-world instances) to calculate valid domains only approximately in order to keep the response time low. In this paper, we present a new fast and accurate approximation algorithm to calculate the valid domains in a satisfiability based interactive product configurator. The algorithm is based on building a full implication graph during unit propagation and performing a search in that implication graph in order to approximate whether a domain value is valid. We experimentally compared our new algorithm to the algorithm used by the commercial SAT-based configurator CAS Merlin and measured speedups of up to 18-fold while maintaining the same accuracy

SAT Competition 2016 : Recent Developments

Non peer reviewe