Black-box explainability tools like LIME and SHAP are widely used to interpret machine learning models.However, their post-hoc, local nature often results in inconsistent and semantically opaque explanations.This paper presents a model-driven explainability approach using grammatical evolution (GE), enabling the discovery of symbolic, human-readable models.We compare black-box explanations to symbolic GEgenerated models on two benchmark tasks: a quadratic equation classification problem and the Iris dataset.GE produces interpretable, consistent, and semantically meaningful expressions consistent with domain knowledge, offering a more trustworthy foundation for explainable AI.The use of Meaningful Intermediate Variables (MIVs) further improves the clarity and expressiveness of the symbolic models.

In this paper, we present a case study research on designing the system interface for handling organisational processes implemented in the editorial office of the AGH-UST Students Information Bulletin.A study of the basic formal and legal sources, such as regulations and statutes of the organisation, was also carried out for the proper identification of goals, responsibilities, and organisational structure.To deepen this knowledge, social research was carried out, which examined how the users use the current solutions and what their needs are related to the designed system.Based on the research results and the formulated conclusions, the diagrams of business processes carried out in the organisation were created.In the discovered process models, we defined reusable sub-processes using Justified Aggregation of Neighboring Activities.Finally, the information architecture of the target system solution, followed by a mediumfidelity interface were designed.

Business process models are subject to changing requirements.The purpose of this paper is to present methods that enable computer-aided recomposition of process models, understood as using existing processes to design new ones.This procedure involves dividing existing BPMN diagrams into smaller components, from which new models can be created.This kind of model generation can be performed manually by the user or run automatically, based on the Constraint Programming technique.The presented algorithms can improve the process of model redesign and allow users to avoid typical anomalies that may occur in the modeling phase.

The natural transformation - as a pilar of a functional dynamism in category theory - forms a unique transfor-mation between the so-called functors, which operate between categories and their morphisms. The natural transformations are determined by the appropriate commutativity conditions in diagrams, which co-define them and their general form may be predicted by the so-called Yoneda's lemma. The situation seems to change radically if we exchange single diagrams for multi-diagrams. This paper is aimed at proposing a new concept of multi-fuzzy natural transformation as based on the concept of fuzzy natural transformation, which may be just defined by the scenario with multi-diagrams. It seems to be noteworthy that such a multi-fuzzy natural transformation may be referred to coding theory. In addition, a multi-fuzzy version of Yoneda's lemma is formulated and proved.

High complexity of business processes in real-life organizations is a constantly rising issue.In consequence, modeling a workflow is a challenge for process stakeholders.Yet, to facilitate this task, new methods can be implemented to automate the phase of process design.As a main contribution of this paper, we propose an approach to generate process models based on activities performed by the participants, where the exact order of execution does not need to be specified.Nevertheless, the goal of our method is to generate artificial workflow traces of a process using Constraint Programming and a set of predefined rules.As a final step, the approach was implemented as a dedicated tool and evaluated on a set of test examples that prove that our method is capable of creating correct process models.

In 1983, B. Moszkowski introduced a first intervally-interpreted temporal logic system, the so-called Interval Temporal Logic, denoted by ITL. ITL forms the modal-temporal system suitable to represent temporal relations between intervals and their subintervals and it found a broad application area in system specifications. This paper is aimed at proposing a kind of an epistemic extension of a simplified propositional version of ITL in order to express fuzzified justifications in it. This newly introduced system is also semantically interpreted in an interval-based fibred semantics. The theoretic considerations are exemplified in some epistemic situation extracted from the classical Traveling Salesman Problem.

Juxtapositioning manually created business process models with diagrams generated using process discovery algorithms exposes high complexity of the latter.As a consequence, their formal verification requires significant computational resources due to a large state space.Nevertheless, an analysis of the generated model is needed to assure its correctness and the ability to represent source data.As a solution to this problem, we present an approach for constraint-based generation of a complete workflow log for a given BPMN model.In this paper, we propose a method to extract directed subgraphs representing token flows in the process together with a set of predefined constraints.Likewise, in the case of process simulation, these constraints ensure the correctness of the generated traces.Ultimately, the obtained results can be compared to the original workflow log used for process discovery in order to verify the obtained model.

The Allen's relations between temporal intervals found its logical representation in terms of the so-called Halpern-Shoham logic (HS). This system forms a modal-type logic interpreted in the interval-based Kripke semantics. Unfortunately, not all phenomena of temporal reasoning may be rendered by HS. In 2013, an epistemic extension of HS was proposed by A. Lomuscio. In this paper a new epistemic Halpern-Shoham logic - as a system of multi-valued logic - for gradable justification is proposed. This system is semantically interpreted in the so-called fibred semantics, enriched by some elements of the so-called behavioral semantics of Fagin. The subject basis of the analysis is determined by a unique epistemic version of Traveling Salesman Problem in this paper.

We present a decision support application which can be used for alternative route generation in case of tramway traffic disruptions.Our solution is based on a mixed graph network model, where vertices represent major points and edges are used to model track sections.The proposed application uses model data stored in a set of source files and enables the user to execute one of four algorithms which are useful for tramway traffic management in case of a crisis situation.

System specifications can be modeled using various types of notations and diagrams regarding applications of the particular model.In this paper, we present an overview of the existing solutions, focusing on UML, BPMN and DMN models and the diagrams provided by these notations.We perform a comparison of these approaches and provide examples of representing system requirements in these notations.

Abstract Most of the research in the area of performance evaluation of rule‐based expert systems (RBESs) is focused on verification and validation issues. Many researchers discuss usability, usefulness, portability, and response time for the evolution of RBES. The final goal of all such studies is to construct a system with optimal, accurate knowledge base. Arguably, a system with best knowledge base is actually worthless if it is never utilized in the real world. We have proposed “benefit” as a measure of evaluation and suggested some guidelines for performance evaluation of RBESs. The proposed measure has been demonstrated for performance evaluation of an RBES applied in the medical billing domain. Results showed that the system has saved hundreds of working hours during the evaluation period of 3 months. Moreover, other associated measures have also been considered. Associated measures in the medical billing domain are “claim rejection rate”—reduced by 54%—and “claim aging,” which has decreased from 34 to 28 days due to the RBES. Guidelines proposed by this research can be applied for the evaluation of expert systems implemented in other application domains, including in the first place business decision support systems.

This paper is aimed at presenting some general construction method of the hybrid plan controller for some task of temporal planning with preferences.This construction is multi-stage and it begins with a description of a chosen robot environment and its plan in some extended version of Linear Temporal Logic.This description is later transformed to the appropriate preferential Büchi automaton.In the same way, the real plan performing by the robot is encoded by the similar automaton.Finally, both automata are exploited to construct its product automaton, which is later described in PROLOG.

Process models can specify various aspects of business processes.In this paper, we present an overview of the existing solutions for describing time aspects of such models.We focus on Business Process Model and Notation and provide examples of representing time patterns in this notation.As temporal issues can be specified using temporal logics, we provide a short overview of selected temporal logics which can be used to specify the time patterns in business process models.

In this paper a preferential extension of the Halpern-Shoham logic (HS) is proposed. The proposed extension (PHS) may be exploited for a representation of both temporal relations and preferences, which adds some “rationality” for temporal reasoning. The next, a new interval-based fibred semantics will be proposed for some subsystem of PHS with temporal modalities restricted to 〈L〉-operator, denoted by PHSL. It appears that some ideas of this semantics type can be useful in more practical contexts such as a plan controller construction.

This paper is aimed at proposing a new convolution-based approach to the specification of solutions of the so-called Simple Temporal Problem under Uncertainty. We intend to show that this approach type allows us to extend some intuitive and common-sense solutions -that can be obtain in a rudimentary way - to the whole class definable by the appropriate functions. We show how their features can be found be means formal tools of the mathematical analysis. In addition, we introduce a formal and complete system of fuzzy logic capable of representing the “before”-relation between intervals in some convolution-based depiction.

Engaging users in threat reporting is important in order to improve threat monitoring in urban environments. Today, mobile applications are mostly used to provide basic reporting interfaces. With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows for real-time processing of data gathered by their sensors. Universal access to the Internet via WiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Although there exist numerous frameworks for context-aware systems, they are usually dedicated to static, centralized, client-server architectures. There is still space for research in the field of context modeling and reasoning for mobile devices. In this paper, we propose a lightweight context-aware framework for mobile devices that uses data gathered by mobile device sensors and performs on-line reasoning about possible threats based on the information provided by the Social Threat Monitor system developed in the INDECT project.

Abduction can be considered as a principal way of reasoning for problem solving. Abductive inference consists in generation of hypotheses which explain — or logically imply — the phenomenon under investigation in view of accessible background knowledge and are consistent with all other observations. Looking for such hypotheses is typically performed with a spectrum of trial-and-error or search methods and tools. In case of purely logical statements the hypotheses take the form of a set of facts, both positive and negative ones. For example, in case of model based diagnostic reasoning, such diagnostic hypotheses can be generated by consistency based reasoning with minimal search effort. In more complex cases, where values of certain variables are to be found, pure backtracking search becomes inefficient. In this paper we attempt to put forward such abductive inference into a formal framework of Constraint Programming in order to enable the use of constraint propagation techniques. The main idea behind this approach is to make abduction more constructive. The discussion is illustrated with a diagnostic example of a multiplier-adder system.

This paper presents a comparative analysis of expressive power of PDDL and its known extensions versus a language of a Fuzzy Logic for Integrals (as a far extension of Łukasiewicz logic) with respect to some components of temporal planning. This comparative perspective is determined not only by the problem of the representation of planning objects, actions and preferences, but also by some ability of both languages to express the plan construction.

In this paper we propose a multi-valued extension of the Halpern-Shoham logic (HS). The HS system is a modal logic of time intervals, with a rich set of ordering relations between intervals, which is potentially useful for temporal planning issues. Our extension may be exploited for a representation of both temporal relations and preferences, which adds some "rationality' for this planning. The linearity relations of preferences (less or more preferable), when combined with time, often lose their sense, and become inapplicable. We intend to show the completeness of a preferential component of the system, w.r.t. the interval-based semantics and that its satisfiability property belong to the class of PSPACE problems.

International audience

Modeling based on a graphical notation understandable for different specialists has become very popular.Within the area of business processes, the most common one is the Business Process Modeling and Notation (BPMN).BPMN is aimed at all business users who design, analyze, manage and monitor business processes.Most papers in this area focus on making use of the possibilities that BPMN makes available, but there is lack of papers analyzing possible errors and ways of detecting and eliminating them.Specification of a BPMN diagram is relatively precise, but it is only a descriptive form presented at some abstract, graphical level.Hence, the main focus of this article is an attempt to analyze the topic of the anomalies which are likely to occur when modeling with use of BPMN.

This paper presents a new approach to multi-agent planning under temporal constraints and uncertainty of admissible operations. A planning problem in AI is usually specified as follows: given a description of the current state of some system, a set of actions that can be performed on the system and a description of a goal set of states for the system, find a sequence of actions that can be performed to transform the system into one of the goal states. The problem described in this paper is to find a way to reach a set of goals satisfying all specified constraints. By constraints, we mean temporal constraints and other constraints in the form of costs, number of agents, etc. Temporal constraints concern both qualitative and quantitative aspects of the system. We consider a set of agents, a set of goals and a description of the environment. The objective is, for one or more agents, to execute a set of tasks to satisfy all goals while respecting all the constraints. A planning procedure over AND-OR graphs is used to generate plans. The proposed multi-agent planning method is based on a centralized scheme in which all the coordination is done at a central location and the agents have no autonomy at the planning level. A specific feature of the task statement in this paper is that we aim at solving tasks of planning and scheduling, the complexity of which is conditioned by temporal and other constraints.

The paper describes a new approach to generating partially ordered plans with durative actions. Both cost and duration of any task is defined through probability distribution. We propose a planner that generates a plan represented in the form of a partially ordered set of tasks. Each task has a set of temporal constraints, a set of probabilities and a set of constant costs. The planning system generates admissible plans satisfying temporal constraints and allows to select the plan of lowest expected cost.

Specification and efficient handling of temporal knowledge is an important issue in design and implementation of contemporary information systems, such as databases, knowledge-based systems or decision support systems. This paper re-explores TUS, the time unit system being an algebraic tool for constructing simple yet powerful temporal specifications. In particular, an extended version of TUS, to be called XTUS is introduced and its basic operations and properties are shown

Rule-based systems constitute the most popular tool for specification of operational knowledge in the majority of knowledge-based systems. This paper addresses the issue of analysis and verification of selected properties of such systems in a systematic way. A uniform, tabular form of single-level rule-based systems is put forward. Such systems can be used independently as a generalized form of decision tables, or as the lower level components of a hierarchical, multilevel control and decision support knowledge-based system. An algebraic knowledge representation form is proposed and algebraic bases for system verification are outlined. © 2001 John Wiley & Sons, Inc.

One of the key issues for efficient application of Case-Based Reasoning (CBR) methodology is to define an appropriate, powerful knowledge representation structure for case encoding and definition of flexible matching algorithms. The basic paradigm consisting in straightforward application of Relational Databases, although conceptually simple and computationally efficient, suffers from inflexible matching algorithms and the lack of possibilities to represent more complex data structures. This paper provides an analysis of certain possible extensions of data structures to be represented within the widely accepted relational database like paradigm, here called Tabular Systems , while providing new possibilities to represent quite complex patterns. As the basic underlying structure the concept of an object is used. A review of selected non-atomic data items, i.e. structures such as sets, record-like structures, lists, intervals, terms, trees, graphs etc. is provided. Flexibility of this structures comes from the fact that they can cover a number of particular cases. For such structures flexible partial matching procedures are proposed. The flexibility is achieved through admission of several types (levels) of matching, different with respect to qualitative perception of the level of similarity. Such flexible matching of data structures provides a way for generation of a match both with respect to parameter adjustment and structure manipulation.

The paper is devoted to presentation of temporal causal networks (TCN) for representing and dealing with causal dependencies propagation over time. A temporal causal network is a causal network incorporating explicit representation of intervals of time during which its symptoms/nodes are valid, not valid, and unknown. The temporal knowledge is represented with use of the so called characteristic functions formalism, which allows for classical binary valued logic specification and can be easily extended towards multivalued or fuzzy logic. The presented approach makes use of specific time constraints propagation algorithm for determining causality propagation over the network in time. This is done by evaluating the characteristic functions used as means for representing time constraints for network nodes. The main application includes simulation, monitoring and elements of diagnostic reasoning for dynamic systems with explicit time representation.