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2016 | Book

Rule Technologies. Research, Tools, and Applications

10th International Symposium, RuleML 2016, Stony Brook, NY, USA, July 6-9, 2016. Proceedings

Editors: Jose Julio Alferes, Leopoldo Bertossi, Guido Governatori, Paul Fodor, Dumitru Roman

Publisher: Springer International Publishing

Book Series : Lecture Notes in Computer Science


About this book

This book constitutes the refereed proceedings of the 10th International RuleML Symposium, RuleML 2016, held in New York, NY, USA during July 2016.

The 19 full papers, 1 short paper, 2 keynote abstracts, 2 invited tutorial papers, 1 invited standard paper, presented were carefully reviewed and selected from 36 submissions.

RuleML is a leading conference aiming to build bridges between academia and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rule systems, logic programming rule engines, and business rule engines and business rule management systems, Semantic Web rule languages and rule standards and technologies, and research on inference rules, transformation rules, decision rules, and ECA rules.

Table of Contents


Invited Papers

Programming in Picat
Picat ( is a logic-based multi-paradigm programming language that integrates logic programming, functional programming, constraint programming, and scripting. Picat takes many features from other languages, including logic variables, unification, backtracking, pattern-matching rules, functions, list/array comprehensions, loops, assignments, tabling for dynamic programming and planning, and constraint solving with CP (constraint programming), SAT (satisfiability), and MIP (mixed integer programming). These features make Picat more convenient than Prolog for scripting and modeling, and more suitable than functional languages (such as Haskell and F#) and scripting languages (such as Python and Ruby) for symbolic computations. This article provides a quick introduction to Picat using examples from Google Code Jam (GCJ).
Neng-Fa Zhou
The RuleML Knowledge-Interoperation Hub
The RuleML knowledge-interoperation hub provides for syntactic/semantic representation and internal/external transformation of formal knowledge. The representation system permits the configuration of textbook and enriched Relax NG syntax as well as the association of syntax with semantics. The transformation tool suite includes serialized formatters (normalizers and compactifiers), polarized parsers and generators (the RuleML\(\leftrightarrow \)POSL tool and the RuleML\(\rightarrow \)PSOA/PS generator and PSOA/PS\(\rightarrow \)AST parser), as well as importers and exporters (the importer from Dexlog to Naf Datalog RuleML and the exporter from FOL RuleML languages to TPTP). An N3-PSOA-Flora knowledge-interoperation use case is introduced for illustration.
Harold Boley

General RuleML Track

Handling Complex Process Models Conditions Using First-Order Horn Clauses
WorkFlow Management Systems provide automatic support to learn process models or to check compliance of process enactment to correct models. The expressive power of the adopted formalism for representing process models is fundamental to determine the effectiveness or even feasibility of a correct model. In particular, a desirable feature is the possibility of expressing complex conditions on some elements of the model. The formalism used in the WoMan framework for workflow management, based on First-Order Logic, is more expressive than standard formalisms adopted in the literature. It allows tight integration between the activity flow and the conditions, and it allows one to express conditions that take into account contextual information and various kinds of relationships among the involved entities. This paper discusses such a formalism, especially concerning conditions, and provides an explicative example of how this can be applied in practice.
Stefano Ferilli
Business Rules Uncertainty Management with Probabilistic Relational Models
Object-oriented Business Rules Management Systems (OO-BRMS) are a complex applications platform that provide tools for automating day-to-day business decisions. To allow more sophisticated and realistic decision-making, these tools must enable Business Rules (BRs) to handle uncertainties in the domain. For this purpose, several approaches have been proposed, but most of them rely on heuristic models that unfortunately have shortcomings and limitations. In this paper we present a solution allowing modern OO-BRMS to effectively integrate probabilistic reasoning for uncertainty management. This solution has a coupling approach with Probabilistic Relational Models (PRMs) and facilitates the inter-operability, hence, the separation between business and probabilistic logic. We apply our approach to an existing BRMS and discuss implications of the knowledge base dynamicity on the probabilistic inference.
Hamza Agli, Philippe Bonnard, Christophe Gonzales, Pierre-Henri Wuillemin
A Declarative Semantics for a Fuzzy Logic Language Managing Similarities and Truth Degrees
This work proposes a declarative semantics based on a fuzzy variant of the classical notion of least Herbrand model for the so-called FASILL language (acronym of “Fuzzy Aggregators and Similarity Into a Logic Language”) which has been recently designed and implemented in our research group for coping with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity.
Pascual Julián-Iranzo, Ginés Moreno, Jaime Penabad, Carlos Vázquez
Controlling the Average Behavior of Business Rules Programs
Business Rules are a programming paradigm for non-programmer business users. They are designed to encode empirical knowledge of a business unit by means of “if-then” constructs. The classic example is that of a bank deciding whether to open a line of credit to a customer, depending on how the customer answers a list of questions. These questions are formulated by bank managers on the basis of the bank strategy and their own experience. Banks often have goals about target percentages of allowed loans. A natural question then arises: can the Business Rules be changed so as to meet that target on average? We tackle the question using “machine learning constrained” mathematical programs, which we solve using standard off-the-shelf solvers. We then generalize this to arbitrary decision problems.
Olivier Wang, Leo Liberti, Claudia D’Ambrosio, Christian de Sainte Marie, Changhai Ke
Bridge Rules for Reasoning in Component-Based Heterogeneous Environments
Multi-Context Systems (MCS) model in Computational Logic distributed systems composed of heterogeneous sources, or “contexts”, interacting via special rules called “bridge rules”. In this paper we consider how to enhance flexibility and generality of such systems; in particular, we discuss aspects that might be improved to increase practical applicability.
Stefania Costantini, Giovanni De Gasperis
Choreographic Compilation of Decentralized Comprehension Patterns
We develop an approach to compiling high-level specifications of distributed applications into code that is executable on individual computing nodes. The high-level language is a form of multiset rewriting augmented with comprehension patterns. It enables a programmer to describe the behavior of a distributed system as a whole rather than from the perspective of the individual nodes, thus dramatically reducing opportunities for programmer errors. It abstracts away the mechanics of communication and synchronization, resulting in concise and declarative specifications. Compilation generates low-level code in a syntactic fragment of this same formalism. This code forces the point of view of each node, and standard state-of-the-art execution techniques are applicable. It is relatively simple to show the correctness of this compilation scheme.
Iliano Cervesato, Edmund Soon Lee Lam, Ali Elgazar
Minimal Objectification and Maximal Unnesting in PSOA RuleML
The paper introduces two connected advancements of Positional-Slotted, Object-Applicative RuleML: (1) a model-theoretic semantics, realized transformationally, that directly handles atoms (i.e., predicate applications) without object identifiers (e.g., relationships as in Prolog) and (2) a transformational semantics that handles nested atomic formulas (e.g., nested frames as in Flora-2/F-logic). For (1), the model theory is extended to atoms with optional OIDs, the transformation is developed from static to dynamic objectification, and the correctness of the realization is proved. For (2), the unnesting transformation is defined to decompose nested atomic formulas into equivalent conjunctions.
Gen Zou, Harold Boley

Smart Contracts, Blockchain and Rules

Setting Standards for Altering and Undoing Smart Contracts
Often, we wish to let parties alter or undo a contract that has been made. Toward this end, contract law has developed a set of traditional tools for altering and undoing contracts. Unfortunately, these tools often fail when applied to smart contracts. It is therefore necessary to define a new set of standards for the altering and undoing of smart contracts. These standards might ensure that the tools we use to alter and undo smart contracts achieve their original (contract law) goals when applied to this new technology. This paper develops such a set of standards and, then, to prove their worth as a framework, applies to them to an existing smart contract platform (Ethereum).
Bill Marino, Ari Juels
Evaluation of Logic-Based Smart Contracts for Blockchain Systems
While procedural languages are commonly used to program smart contracts in blockchain systems, logic-based languages may be interesting alternatives. In this paper, we inspect what are the possible legal and technical (dis)advantages of logic-based smart contracts in light of common activities featuring ordinary contracts, then we provide insights on how to use such logic-based smart contracts in combination with blockchain systems. These insights lead us to emphasize a fundamental challenge - algorithms for logic approaches have to be efficient, but they also need to be literally cheap as measured within the environment where they are deployed and according to its economic rules. We illustrate this with different algorithms from defeasible logic-based frameworks.
Florian Idelberger, Guido Governatori, Régis Riveret, Giovanni Sartor
Blockchain Temporality: Smart Contract Time Specifiability with Blocktime
The aims of this paper are to (1) provide a conceptual context for smart contracts, (2) argue that blockchains are a next-generation technology enabling much larger-scale and more complex computing projects, and (3) posit blocktime as a new mode of conceiving time. Blockchains are the distributed ledger technology underlying Bitcoin and other cryptocurrencies; the payments layer the Internet never had; a mechanism for updating truth states in distributed network computing through consensus trust; and overall, a new form of general computational substrate. Blocktime is the time over which a certain number of blocks will have confirmed; and this creates an alternative event trajectory in time which can be offset against human-time or other computing clocktime regimes for arbitrage or complementary purposes. The result of this effort is to show that blocktime allows the contingency of future events to be more robustly orchestrated through temporality as a selectable smart contract feature.
Melanie Swan

Constraint Handling Rules

A Numerical Optimisation Based Characterisation of Spatial Reasoning
We present a novel numerical optimisation based characterisation of spatial reasoning in the context of constraint logic programming (CLP). The approach —formalised and implemented within CLP— is developed as an extension to CLP(QS), a declarative spatial reasoning framework providing a range of mixed quantitative-qualitative spatial representation and reasoning capabilities. We demonstrate the manner in which the numerical optimisation based extensions further enhance the declarative spatial reasoning capabilities of CLP(QS).
Carl Schultz, Mehul Bhatt
Why Can’t You Behave? Non-termination Analysis of Direct Recursive Rules with Constraints
This paper is concerned with rule-based programs that go wrong. The unwanted behavior of rule applications is non-termination or failure of a computation. We propose a static program analysis of the non-termination problem for recursion in the Constraint Handling Rules (CHR) language.
CHR is an advanced concurrent declarative language involving constraint reasoning. It has been closely related to many other rule-based approaches, so the results are of a more general interest. In such languages, non-termination is due to infinite applications of recursive rules. Failure is due to accumulation of contradicting constraints during the computation.
We give theorems with so-called misbehavior conditions for potential non-termination and failure (as well as definite termination) of linear direct recursive simplification rules. Logical relationships between the constraints in a recursive rule play a crucial role in this kind of program analysis. We think that our approach can be extended to other types of recursion and to a more general class of rules. Therefore this paper can serve as a basic reference and a starting point for further research.
Thom Frühwirth
Translation of Cognitive Models from ACT-R to Constraint Handling Rules
Cognitive architectures are used to abstract and simplify the process of computational cognitive modeling. The popular cognitive architecture ACT-R has a well-defined psychological theory, but lacks a formalization of its computational system. This inhibits computational analysis of cognitive models, e.g. confluence or complexity analysis. In this paper we present a source to source transformation of ACT-R models to Constraint Handling Rules (CHR) programs enabling the use of analysis tools for CHR to analyze computational cognitive models. This translation is the first that matches the current abstract operational semantics of ACT-R.
Daniel Gall, Thom Frühwirth

Legal Rules and Reasoning

Enabling Reasoning with LegalRuleML
This paper presents an approach for the specification and implementation of translating legal norms represented using LegalRuleML to a variant of Modal Defeasible Logic. From its logical form, legal norms will be transformed into a machine readable format and eventually implemented as executable semantics that can be reasoned about depending upon the client’s preference.
Ho-Pun Lam, Mustafa Hashmi, Brendan Scofield
SBVR to OWL 2 Mapping in the Domain of Legal Rules
The Semantics of Business Vocabulary and Business Rules (SBVR) is a specification created by the Object Management Group (OMG) to provide a way to semantically describe business concepts and specify business rules. However, reasoning with SBVR is still an open subject, and current efforts to provide reasoning are done through the Web Ontology Language (OWL), by providing a mapping between SBVR and OWL. In this paper we focus on the problem of mapping SBVR vocabulary and rulebook to OWL 2, but unlike previous mappings described in the literature, we provide a novel and unorthodox mapping that allows to describe legal rules which have their own intricate anatomy.
Firas Al Khalil, Marcello Ceci, Kosala Yapa, Leona O’Brien

Rule- and Ontology-Based Data Access and Transformation

OBDA Constraints for Effective Query Answering
In Ontology Based Data Access (OBDA) users pose SPARQL queries over an ontology that lies on top of relational datasources. These queries are translated on-the-fly into SQL queries by OBDA systems. Standard SPARQL-to-SQL translation techniques in OBDA often produce SQL queries containing redundant joins and unions, even after a number of semantic and structural optimizations. These redundancies are detrimental to the performance of query answering, especially in complex industrial OBDA scenarios with large enterprise databases. To address this issue, we introduce two novel notions of OBDA constraints and show how to exploit them for efficient query answering. We conduct an extensive set of experiments on large datasets using real world data and queries, showing that these techniques strongly improve the performance of query answering up to orders of magnitude.
Dag Hovland, Davide Lanti, Martin Rezk, Guohui Xiao
A Framework Enhancing the User Search Activity Through Data Posting
Due to the increasing availability of huge amounts of data, traditional data management techniques result inadequate in many real life scenarios. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch. In this paper, we describe a framework tailored for analyzing user interactions with intelligent systems while seeking for some domain specific information (e.g., choosing a good restaurant in a visited area). The framework enhances user quest for information by performing a data exchange activity (called data posting) which enriches the information sources with additional background information and knowledge derived from experiences and behavioral properties of domain experts and users.
Nunziato Cassavia, Elio Masciari, Chiara Pulice, Domenico Saccà

Rule Induction and Learning

PRIMER – A Regression-Rule Learning System for Intervention Optimization
We introduce intervention optimization as a new area of exploration for data mining research. Interventions are events designed to impact a corresponding time series. The task is to maximize the impact of such events by training a model on historical data. We propose PRIMER as a new regression-rule learning system for identifying sets of event features that maximize impact. PRIMER is for use when domain experts with knowledge of the intervention can specify a transfer function, or the form of the expected response in the time series. PRIMER’s objective function includes the goodness-of-fit of the average response of covered events to the transfer function. Incorporating domain knowledge in this way makes PRIMER robust to over-fitting on noise or spurious responses. PRIMER is designed to produce interpretable results, improving on the interpretability of even competing regression-rule systems for this task. It also has fewer and more intuitive parameters than competing rule-based systems. Empirically, we show that PRIMER is competitive with state-of-the-art regression techniques in a large-scale event study modeling the impact of insider trading on intra-day stock returns.
Greg Harris, Anand Panangadan, Viktor K. Prasanna

Event Driven Architectures and Active Database Systems

Rule-Based Real-Time ADL Recognition in a Smart Home Environment
This paper presents a rule-based approach for both offline and real-time recognition of Activities of Daily Living (ADL), leveraging events produced by a non-intrusive multi-modal sensor infrastructure deployed in a residential environment. Novel aspects of the approach include: the ability to recognise arbitrary scenarios of complex activities using bottom-up multi-level reasoning, starting from sensor events at the lowest level; an effective heuristics-based method for distinguishing between actual and ghost images in video data; and a highly accurate indoor localisation approach that fuses different sources of location information. The proposed approach is implemented as a rule-based system using Jess and is evaluated using data collected in a smart home environment. Experimental results show high levels of accuracy and performance, proving the effectiveness of the approach in real world setups.
George Baryannis, Przemyslaw Woznowski, Grigoris Antoniou
SmartRL: A Context-Sensitive, Ontology-Based Rule Language for Assisted Living in Smart Environments
To automate assisted living tasks in smart environments, the contextual and temporal aspects associated with activities of daily life (ADL) can be exploited to (1) detect and act upon inconsistent context, i.e., when an activity occurs outside of its usual context; and (2) guidance through ADL routines, by automatically executing or suggesting a next subtask at the correct context. This paper presents SmartRL, a context-sensitive rule language supporting task automation in smart environments, and applies it to an Assisted Ambient Living (AAL) use case. SmartRL realizes a number of key opportunities in this setting, such as linking the language to a domain ontology, and facilitating the detection and influencing of context; as well as considering the temporal nature of smart environment rules, the need to revert rule effects, and writing activity routines.
William Van Woensel, Patrice C. Roy, Syed Sibte Raza Abidi
Rule Technologies. Research, Tools, and Applications
Jose Julio Alferes
Leopoldo Bertossi
Guido Governatori
Paul Fodor
Dumitru Roman
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