A stochastic action language A

In this paper we present a new stochastic nondeterministic high-level action language SAA which is a stochastic extension of Action Language A. We describe the syntax and semantics of SAA and show it has an equivalent expressive power to Hidden Markov Models (HMMs). The main advantage of SAA is its smooth conversion of propositions and probability, and use of a well-established stochastic model. We show two simple examples in the nuclear reactor domain and propose a normalisation technique for declarative probability assignments which match our intuition

Improving numerical reasoning capabilities of inductive logic programming systems

Inductive Logic Programming (ILP) systems have been largely applied to classification problems with a considerable success. The use of ILP systems in problems requiring numerical reasoning capabilities has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the range of domains where the ILP paradigm may be applied. This paper proposes improvements in numerical reasoning capabilities of ILP systems. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAG method to evaluate learning performance. We have found these extensions essential to improve on results mer statistical-based algorithms for time series forecasting used in the empirical evaluation study

On avoiding redundancy in inductive logic programming

ILP systems induce rst-order clausal theories performing asearch through very large hypotheses spaces containing redundant hypotheses.The generation of redundant hypotheses may prevent the systemsfrom nding good models and increases the time to induce them.In this paper we propose a classication of hypotheses redundancy andshow how expert knowledge can be provided to an ILP system to avoidit. Experimental results show that the number of hypotheses generatedand execution time are reduced when expert knowledge is used to avoidredundancy

Human Comprehensible Active Learning of Genome-Scale Metabolic Networks

An important application of Synthetic Biology is the engineering of the host cell system to yield useful products. However, an increase in the scale of the host system leads to huge design space and requires a large number of validation trials with high experimental costs. A comprehensible machine learning approach that efficiently explores the hypothesis space and guides experimental design is urgently needed for the Design-Build-Test-Learn (DBTL) cycle of the host cell system. We introduce a novel machine learning framework ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive logical reasoning and actively learns from training examples. In contrast to numerical models, ILP-iML1515 is built on comprehensible logical representations of a genome-scale metabolic model and can update the model by learning new logical structures from auxotrophic mutant trials. The ILP-iML1515 framework 1) allows high-throughput simulations and 2) actively selects experiments that reduce the experimental cost of learning gene functions in comparison to randomly selected experiments.Comment: Invited presentation for AAAI Spring Symposium Series 2023 on Computational Scientific Discover

Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results

Strategies to parallelize ILP systems

It is well known by Inductive Logic Programming (ILP) practionersthat ILP systems usually take a long time to nd valuable models(theories). The problem is specially critical for large datasets, preventingILP systems to scale up to larger applications. One approach to reducethe execution time has been the parallelization of ILP systems. In thispaper we overview the state-of-the-art on parallel ILP implementationsand present work on the evaluation of some major parallelization strategiesfor ILP. Conclusions about the applicability of each strategy arepresented

Distributed generative data mining

A process of Knowledge Discovery in Databases (KDD) involving large amounts of data requires a considerable amount of computational power. The process may be done on a dedicated and expensive machinery or, for some tasks, one can use distributed computing techniques on a network of affordable machines. In either approach it is usual the user to specify the workflow of the sub-tasks composing the whole KDD process before execution starts.In this paper we propose a technique that we call Distributed Generative Data Mining. The generative feature of the technique is due to its capability of generating new sub-tasks of the Data Mining analysis process at execution time. The workflow of sub-tasks of the DM is, therefore, dynamic.To deploy the proposed technique we extended the Distributed Data Mining system HARVARD and adapted an Inductive Logic Programming system (IndLog) used in a Relational Data Ming task.As a proof-of-concept, the extended system was used to analyse an artificialdataset of a credit scoring problem with eighty million records