Abstract Industrial processes often involve the generation—and the analysis—of multivariate time series data, which poses several challenges from the anomaly detection perspective. In addition to the need to detect previously unseen anomalies, the high dimensionality of industrial datasets introduces the complexity of simultaneously analyzing multiple features and their interactions. Finally, industrial datasets are typically highly imbalanced, with minimal information on anomalous processes. To address these issues, we propose a novel anomaly detection framework that introduces two embedding models, based on Time2Vec and Discrete Wavelet Transforms, leveraging their capabilities to represent multivariate time series as vectors while capturing and preserving temporal dependencies and combining them with several classifiers to enhance the overall performance of anomaly detection. We tested our solution using a publicly available benchmark dataset and a real industrial use case, particularly data collected from a Bonfiglioli gear manufacturing plant. The results demonstrate that, unlike traditional reconstruction-based autoencoders, which often struggle with sporadic noise, our embedding-based solutions maintain high performance across various noise conditions.

The manufacturing process of frozen dairy desserts, such as ice cream and gelato, is very sensitive to human errors in ingredient preparation: even minor variations in the ingredient mix can lead to quality issues and material waste. To become more sustainable, next generation ice cream making machines need to implement intelligent and adaptive processes that are both efficient and forgiving of human mistakes in mixture preparations. Toward that goal, we developed Hard-O-Tronic AI-driven (HOT-AI), a novel edge AI solution specifically designed for Carpigiani’s ice cream making machines. Leveraging the innovative multimilestone classification methodology, HOT-AI performs inference at multiple stages—or milestones—during the ice cream making process, with increasing accuracy over time. This enables HOT-AI to take corrective actions by adapting the preparation process accordingly, thus improving batch-to-batch uniformity, minimizing ingredient waste, and enhancing production efficiency, cost-effectiveness, and sustainability. HOT-AI has been successfully validated under real production conditions, and its large-scale implementation is planned across Carpigiani Group machines.

The growing demand for deployment of Artificial Intelligence (AI) on resource-constrained edge devices has motivated extensive research on the design of efficient edge-compatible AI hardware accelerators. One of the most promising solutions are the self-adaptive AI accelerators, capable of optimizing in real time their performance and energy consumption according to application requirements. This work introduces the EU-funded project Twinning for Excellence in Adaptive Edge Artificial Intelligence (AIDA4Edge), aimed to advance the state-of-the-art in the design of adaptive neural network accelerators for edge applications. The main goal is to develop a novel hybrid self-adaptive neural network architecture combining spiking and artificial neural networks, and supporting runtime adaptation of network functionality, precision and reliability. Furthermore, we aim to enhance the neural network training by incorporating hardware and quantization constraints in an automated tuning engine.

Abstract Solving a decision theory problem usually involves finding the actions, among a set of possible ones, which optimize the expected reward, while possibly accounting for the uncertainty of the environment. In this paper, we introduce the possibility to encode decision theory problems with Probabilistic Answer Set Programming under the credal semantics via decision atoms and utility attributes. To solve the task, we propose an algorithm based on three layers of Algebraic Model Counting, that we test on several synthetic datasets against an algorithm that adopts answer set enumeration. Empirical results show that our algorithm can manage non-trivial instances of programs in a reasonable amount of time.

Abstract The aim of the Semantic Web is making information and resources from the Web automatically processable by machines. Usually, the uncertainty characterizing much of this information is addressed by means of a probabilistic semantics. Following the vision of a “Probabilistic Semantic Web”, a plethora of probabilistic semantics have been proposed: some of them change the syntax and/or the semantics itself of the knowledge representation language, others allow one to annotate axioms of a knowledge base with a probability value. Among the latter, the DISPONTE semantics exploits probabilistic annotations to extend query answering with the capability of returning the probability of a query being true in a domain. In order to promote the adoption of Probabilistic Semantic Web we first developed BUNDLE, a framework that can exploit different underlying (probabilistic and non‐probabilistic) reasoners to perform probabilistic inference under the DISPONTE semantics. In this paper we present a web application for BUNDLE, to show how DISPONTE is easily usable even in already established applications and systems. It allows users to query a DISPONTE knowledge base written or uploaded directly in the application interface by using just a web browser, without the need to install any software on their machine. It is accessible on the web at https://bundle.ml.unife.it/ and also provides some examples for familiarizing with the application. The results of a usability evaluation involving human participants are also reported, showing the relevance and the practical impact of the tool and possible ways for improvement.

Abstract Probabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic programming usually the aim is to learn these kinds of models to predict specific atoms or predicates of the domain, called target atoms/predicates. However, it might also be useful to learn classifiers for interpretations as a whole: to this end, we consider the models produced by the inductive constraint logic system, represented by sets ofintegrity constraints, and we propose a probabilistic version of them. Each integrity constraint is annotated with a probability, and the resulting probabilistic logical constraint model assigns a probability of being positive to interpretations. To learn both the structure and the parameters of such probabilistic models we propose the system PASCAL for “probabilistic inductive constraint logic”. Parameter learning can be performed using gradient descent or L-BFGS. PASCAL has been tested on 11 datasets and compared with a few statistical relational systems and a system that builds relational decision trees (TILDE): we demonstrate that this system achieves better or comparable results in terms of area under the precision–recall and receiver operating characteristic curves, in a comparable execution time.

Abstract Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are a suitable framework to handle uncertain information, and in the last decade many probabilistic logical languages have been proposed, as well as inference and learning systems for them. In the realm of Abductive Logic Programming (ALP), a variety of proof procedures have been defined as well. In this paper, we consider a richer logic language, coping with probabilistic abduction with variables. In particular, we consider an ALP program enriched with integrity constraints à la IFF, possibly annotated with a probability value. We first present the overall abductive language and its semantics according to the Distribution Semantics. We then introduce a proof procedure, obtained by extending one previously presented, and prove its soundness and completeness.

Abstract In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given evidence on other variables, and the Most Probable Explanation (MPE) task, the instance of MAP where the query variables are the complement of the evidence variables. We present a novel algorithm, included in the PITA reasoner, which tackles these tasks by representing each problem as a Binary Decision Diagram and applying a dynamic programming procedure on it. We compare our algorithm with the version of ProbLog that admits annotated disjunctions and can perform MAP and MPE inference. Experiments on several synthetic datasets show that PITA outperforms ProbLog in many cases.

Although recent advances of significant subgraph mining enable us to find subgraphs that are statistically significantly associated with the class variable from graph databases, it is challenging to interpret the resulting subgraphs due to their massive number and their propositional representation . Here we represent graphs by probabilistic logic programming and solve the problem of summarizing significant subgraphs by structure learning of probabilistic logic programs. Learning probabilistic logical models leads to a much more interpretable, expressive and succinct representation of significant subgraphs. We empirically demonstrate that our approach can effectively summarize significant subgraphs with keeping high accuracy.

Abstract When modeling real-world domains, we have to deal with information that is incomplete or that comes from sources with different trust levels. This motivates the need for managing uncertainty in the Semantic Web. To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics (DLs) with probability theory. The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD). The set of explanations can be found using thetableau algorithm, which has to handle non-determinism. Prolog, with its efficient handling of non-determinism, is suitable for implementing the tableau algorithm. TRILL and TRILLPare systems offering a Prolog implementation of the tableau algorithm. TRILLPbuilds apinpointing formulathat compactly represents the set of explanations and can be directly translated into a BDD. Both reasoners were shown to outperform state-of-the-art DL reasoners. In this paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is directly built during the construction of the tableau, further speeding up the overall inference process. An experimental comparison shows the effectiveness of TORNADO. All systems can be tried online in the TRILL on SWISH web application at http://trill.ml.unife.it/ .

Ontologies form the basis of the Semantic Web. Description Logics (DLs) are often the languages of choice for modeling ontologies. Integration of DLs with rules and rule-based reasoning is crucial in the so-called Semantic Web stack vision - a complete stack of recommendations and languages each ba sed on and/or exploiting the underlying layers - which adds new features to the standards used in theWeb. The growing importance of the integration between DLs and rules is proved by the definition of the profile OWL 2 RL1 and the definition of languages such as RIF2 and SWRL3. Datalog± is an extension of Datalog which can be used for representing lightweight ontologies and expressing some languages of the DL-Lite family, with tractable query answering under certain language restrictions. In particular, it is able to express the DL-Lite version defined in OWL. In this work, we show that Abductive Logic Programming (ALP) can be used to represent Datalog± ontologies, supporting query answering through an abductive proof procedure, and smoothly achieving the integration of ontologies and rule-based reasoning. Often, reasoning with DLs means finding explanations for the truth of queries, that are useful when debugging ontologies and to understand answers given by the reasoning process. We show that reasoning under existential rules can be expressed by ALP languages and we present a solving system, which is experimentally proved to be competitive with DL reasoning systems. In particular, we consider an ALP framework named 𝒮CIFF derived from the IFF abductive framework. Forward and backward reasoning is naturally supported in this ALP framework. The 𝒮CIFF language smoothly supports the integration of rules, expressed in a Logic Programming language, with Datalog± ontologies, mapped into 𝒮CIFF (forward) integrity constraints. The main advantage is that this integration is achieved within a single language, grounded on abduction in computational logic, and able to model existential rules.

We present the web application Tableau Reasoner for descrIption Logics in proLog on SWI-Prolog for SHaring (TRILL on SWISH) which allows the user to write probabilistic description logic (DL) theories and compute the probability of queries with just a web browser. Various probabilistic extensions of DLs have been proposed in the recent past, because uncertainty is a fundamental component of the Semantic Web. We consider probabilistic DL theories following our distribution semantics for probabilistic ontologies (DISPONTE) semantics. Axioms of a DISPONTE knowledge base can be annotated with a probability, and the probability of queries can be computed with inference algorithms. TRILL is a probabilistic reasoner for DISPONTE knowledge base that is implemented in Prolog and exploits its backtracking facilities for handling the non-determinism of the tableau algorithm. TRILL on SWISH is based on SWISH, a recently proposed web framework for logic programming, based on various features and packages of SWI-Prolog (e.g., a web server and a library for creating remote Prolog engines and posing queries to them). TRILL on SWISH also allows users to cooperate in writing a probabilistic DL theory. It is free, open, and accessible on the Web at the url: http://trill.lamping.unife.it; it includes a number of examples that cover a wide range of domains and provide interesting Probabilistic Semantic Web applications. By building a web-based system, we allow users to experiment with probabilistic DLs without the need to install a complex software stack. In this way, we aim to reach out to a wider audience and popularize the Probabilistic Semantic Web. Copyright © 2016 John Wiley & Sons, Ltd.

Probabilistic Logic Programming is a promising formalism for dealing with uncertainty. Learning probabilistic logic programs has been receiving an increasing attention in Inductive Logic Programming: for instancethe system SLIPCOVER learns high quality theories in a variety of domains. HoweverSLIPCOVER is computationally expensivewith a running time of the order of hours. In order to apply SLIPCOVER to Big Data, we present SEMPRE, for “Structure lEarning by MaPREduce”, that scales SLIPCOVER by following a MapReduce strategy, directly implemented with the Message Passing Interface.

The management of business processes can support efficiency improvements in organizations. One of the most interesting problems is the mining and representation of process models in a declarative language. Various recently proposed knowledge-based languages showed advantages over graph-based proced ural notations. Moreover, rapid changes of the environment require organizations to check how compliant are new process instances with the deployed models. We present a Statistical Relational Learning approach to Workflow Mining that takes into account both flexibility and uncertainty in real environments. It performs automatic discovery of process models expressed in a probabilistic logic. It uses the existing DPML algorithm for extracting first-order logic constraints from process logs. The constraints are then translated into Markov Logic to learn their weights. Inference on the resulting Markov Logic model allows a probabilistic classification of test traces, by assigning them the probability of being compliant to the model. We applied this approach to three datasets and compared it with DPML alone, five Petri net- and EPC-based process mining algorithms and Tilde. The technique is able to better classify new execution traces, showing higher accuracy and areas under the PR/ROC curves in most cases.

Summary We present the web application ‘cplinton SWI‐Prolog for SHaring that allows the user to write (SWISH)' Probabilistic Logic Programs and submit the computation of the probability of queries with a web browser. The application is based on SWISH, a web framework for Logic Programming. SWISH is based on various features and packages of SWI‐Prolog, in particular, its web server and its Pengine library, that allow to create remote Prolog engines and to pose queries to them. In order to develop the web application, we started from the PITA system, which is included incplint, a suite of programs for reasoning over Logic Programs with Annotated Disjunctions, by porting PITA to SWI‐Prolog. Moreover, we modified the PITA library so that it can be executed in a multi‐threading environment. Developing ‘cplinton SWISH’ also required modification of the JavaScript SWISH code that creates and queries Pengines. ‘cplinton SWISH’ includes a number of examples that cover a wide range of domains and provide interesting applications of Probabilistic Logic Programming. By providing a web interface tocplint, we allow users to experiment with Probabilistic Logic Programming without the need to install a system, a procedure that is often complex, error prone, and limited mainly to the Linux platform. In this way, we aim to reach out to a wider audience and popularize Probabilistic Logic Programming. Copyright © 2015 John Wiley & Sons, Ltd.

Repetitive laboratory testing has become a well-recognized problem in the practice of medicine, especially in the hospital inpatient setting, since it increases costs and causes patient discomfort. Among the interventions proposed to reduce unnecessary testing, Clinical Decision Support Systems (CDSS) have been shown to be effective. We present the project of a CDSS recommending professionals in real time regarding the appropriateness for repeating laboratory exams, embedded in a Computerized Physician Order Entry at the Azienda Ospedaliero-Universitaria and Azienda Unità Sanitaria Locale of Ferrara, Italy. Appropriateness is encoded in test-specific formal rules which are applied against the laboratory results done in the past for a patient, and which eventually trigger an alert meaning that a test repetition is redundant. Both the previous result's validation date and quantitative value are considered during rule application. The rules-set implemented concerns: clinical chemistry, hematology, coagulation, infectious diseases serology, microbiology, inflammation, cardiac and tumor markers, hormones, autoimmunity, allergology, molecular biology and drug monitoring testing.

Abstract Learning probabilistic logic programming languages is receiving an increasing attention, and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both structure and parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for “Structure LearnIng of Probabilistic logic programs by searChing OVER the clause space.” It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood, SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and receiver operating characteristic curves in most cases.

Abstract Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP2for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.

Recently much work in Machine Learning has concentrated on using expressive representation languages that combine aspects of logic and probability. A whole field has emerged, called Statistical Relational Learning, rich of successful applications in a variety of domains. In this paper we present a Machine Learning technique targeted to Probabilistic Logic Programs, a family of formalisms where uncertainty is represented using Logic Programming tools. Among various proposals for Probabilistic Logic Programming, the one based on the distribution semantics is gaining popularity and is the basis for languages such as ICL, PRISM, ProbLog and Logic Programs with Annotated Disjunctions. This paper proposes a technique for learning parameters of these languages. Since their equivalent Bayesian networks contain hidden variables, an Expectation Maximization (EM) algorithm is adopted. In order to speed the computation up, expectations are computed directly on the Binary Decision Diagrams that are built for inference. The resulting system, called EMBLEM for “EM over Bdds for probabilistic Logic programs Efficient Mining”, has been applied to a number of datasets and showed good performances both in terms of speed and memory usage. In particular its speed allows the execution of a high number of restarts, resulting in good quality of the solutions.