Treewidth-Aware Cycle Breaking for Algebraic Answer Set Counting

Probabilistic reasoning, parameter learning, and most probable explanation inference for answer set programming have recently received growing attention. They are only some of the problems that can be formulated as Algebraic Answer Set Counting (AASC) problems. The latter are however hard to solve, and efficient evaluation techniques are needed. Inspired by Vlasser et al.'s Tp-compilation (JAR, 2016), we introduce Tp-unfolding, which employs forward reasoning to break the cycles in the positive dependency graph of a program by unfolding them. Tp-unfolding is defined for any normal answer set program and unfolds programs with respect to unfolding sequences, which are akin to elimination orders in SAT-solving. Using "good" unfolding sequences, we can ensure that the increase of the treewidth of the unfolded program is small. Treewidth is a measure adhering to a program's tree-likeness, which gives performance guarantees for AASC. We give sufficient conditions for the existence of good unfolding sequences based on the novel notion of component-boosted backdoor size, which measures the cyclicity of the positive dependencies in a program. The experimental evaluation of a prototype implementation, the AASC solver aspmc, shows promising results.

Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs

State-of-the-art inference approaches in probabilistic logic programming typically start by computing the relevant ground program with respect to the queries of interest, and then use this program for probabilistic inference using knowledge compilation and weighted model counting. We propose an alternative approach that uses efficient Datalog techniques to integrate knowledge compilation with forward reasoning with a non-ground program. This effectively eliminates the grounding bottleneck that so far has prohibited the application of probabilistic logic programming in query answering scenarios over knowledge graphs, while also providing fast approximations on classical benchmarks in the field.

Sistem pakar mendiagnosa penyakit kolesterol berbasis database dan web

Technological advancements, increasing various aspects of life in the modern era have changed the style and pattern of people's lives. Unhealthy lifestyles are clearly seen in big cities, seen from food that can be found in every fast food restaurant or fast food. If the diet is not controlled, a person will be more susceptible to diseases that are difficult to cure, namely cholesterol. In this study the authors aim to design an expert system application that is used to diagnose cholesterol based and web- based cholesterol diseases based on the symptoms of the disease felt by patients who become a decision in the form of a level of confidence and the solution by using the forward chaining method to get forward reasoning and calculate the value of trust of the symptoms chosen by the patient.