We propose a novel module for computing all possible passages through an intersection by assigning indexes to entry and exit points extracted from textual narratives and computing relative directions between them. Our method relies on a contiguity condition that ensures every input record is connected and computes relative directions using predefined tables. The resulting passages serve as the basis for constructing reliable sketch maps with topological accuracy. This approach facilitates early error detection in spatial data and supports future extensions to cases where intersections are incomplete by retaining non-contiguous records until additional data become available.

This paper explores an outline of the application of AI in standardizing place names within urban narratives, addressing discrepancies caused by diverse agent terminologies. By leveraging AI chatbots for named entity recognition, coreference resolution, and entity linking, the study proposes an interactive methodology for homogenizing place names across different accounts. This innovative approach aims to enhance the accuracy of information extraction from narratives, demonstrating the potential of AI models over traditional linguistic methods in resolving place name inconsistencies.

Knowledge about the real world is often recorded in plain text, such as posts on social networks, descriptions in various guides, etc. These messages include spatial information that can be extracted using natural language processing methods. The extracted information can then be represented as a planar graph, which can be further transformed into a topological map using additional information describing the area. This paper outlines an algorithm that takes a given planar graph as input and uses a multi-agent system to place individual points in 2D space, creating a topological map respecting all edge directions given in the narratives.

Maps constructed in Euclidean space are commonly used to visually present information about the real world. However, their creation is resource intensive, be it financial, technical, human, or time-consuming, which can limit their timeliness and detail. A simpler form of visualization of data about the real world is represented by sketch maps, which mainly capture the topology and mutual features’ spatial location. By default, they are drawn by hand. This presupposes that the creator has a good knowledge of the depicted territory, can create a cognitive map, and is skilled in transforming it into a graphical form. Sketch maps can be detailed and up-to-date if these prerequisites are met. Our question was whether it is possible to meet these assumptions in another way: acquire knowledge of the territory by processing narratives related to the area of interest, create a suitable computer representation for further processing, and automatically generate the resulting sketch map. This article presents the last step – creating a sketch map based on spatial data acquired from narratives. The results show that even without metric data, it is possible to automatically generate a sketch map visually close to the actual situation.

A significant amount of real-world information is documented in simple text format, such as messages found on social networks. These messages include various types of data, including spatial details, which can be extracted through natural language processing. The extracted data can be represented as a plain topological graph, stored as tuples that describe individual edges. This paper outlines an algorithm that utilizes these tuples to generate a simplified map.

The paper deals with the rules for converting natural language text with motion verbs into TIL-Script, the computational variant of Transparent Intensional Logic (TIL). This function is part of the TILUS tool, which is now being worked on, and which will be used for the needs of appropriate textual information sources retrieval and natural language processing. Our work is currently starting on a module that allows the transformation of a particular subset of natural language texts describing journey descriptions into logical constructions. Hence, in this paper, we focused on the transformation rules for sentences containing motion verbs describing the agent’s movement on the infrastructure. These rules are based on the utilization of Stanford typed dependencies representation and verb valency frames of motion verbs.

Navigation and an agent’s map representation in a multi-agent system become problematic when agents are situated in complex environments such as the real world. Challenging modifiability of maps, long updating period, resource-demanding data collection makes it difficult for agents to keep pace with rather quickly expanding cities. This study presents the first steps to a possible solution by exploiting natural language processing and symbolic methods of supervised machine learning. An adjusted algorithm processes formalized descriptions of one’s journey to produce a description of the journey. The explication is represented employing Transparent Intensional Logic. A combination of several explications might be used as a representation of spatial data, which may help the agents to navigate. Results of the study showed that it is possible to obtain a topological representation of a map using natural language descriptions. Collecting spatial data from spoken language may accelerate updating and creation of maps, which would result in up-to-date information for the agents obtained at a rather low cost.

The paper deals with the introduction of TILUS tool for the needs of appropriate textual information sources retrieval and natural language processing. TILUS tool presupposed up to now that all the data are formalized in TIL-Script, the computational variant of Transparent Intensional Logic (TIL). We outline the general proposal of utilizing the Stanford typed dependencies representation for semi-automate conversion of natural language into TIL-Script. In order to be able to correctly solve this problem, we also introduce our universal conceptualization which is able to cover the thematic variations of processed texts.

This paper deals with an optimization of methods for recommending relevant text sources. We summarize methods that are based on a theory of Association Rules and Formal Conceptual Analysis which are computationally demanding. Therefore we are applying the ‘Iceberg Concepts’, which significantly prune output data space and thus accelerate the whole process of the calculation. Association Rules and the Relevant Ordering, which is an FCA-based method, are applied on data obtained from explications of an atomic concept. Explications are procured from natural language sentences formalized into TIL constructions and processed by a machine learning algorithm. TIL constructions are utilized only as a specification language and they are described in numerous publications, so we do not deal with TIL in this paper.

In this paper, we deal with the support in the search for appropriate textual sources. Users ask for an atomic concept that is explicated using machine learning methods applied to different textual sources. Next, we deal with the so-obtained explications to provide even more useful information. To this end, we apply the method of computing association rules. The method is one of the data-mining methods used for information retrieval. Our background theory is the system of Transparent Intensional Logic (TIL); all the concepts are formalised as TIL constructions.

In this paper we deal with machine learning methods and algorithms applied to the area of geographic data. First, we briefly introduce learning with a supervisor that is applied in our case. Then we describe the algorithm 'Framework' together with heuristic methods used in it. Definitions of particular geographic objects, i.e. their concepts, are formulated in our background theory Transparent Intensional Logic (TIL) as TIL constructions. These concepts serve as general hypotheses. Basic principles of supervised machine learning are generalization and specialization. Given a positive example, the learner generalizes, while after a near-miss example specialization is applied. Heuristic methods deal with the way generalization and specialization are applied.

In this paper we deal with the extension of the functionalities of the TIL-Script language, namely the proof system based on natural deduction. The system processes a subset of the set of TIL-Script constructions that are typed to v-construct a truth-value. Since TIL-Script is a functional programming language based on a hyperintensional lambda calculus with procedural semantics, we also describe the way how to validly apply beta conversion and how to operate in a hyperintensional context where the very procedure is an object of predication.

The paper deals with the fundamental computational rule of functional programming languages, namely the rule of beta conversion. This rule specifies the way in which a function f is applied to its argument a. There are two possible ways of executing the conversion, to wit ‘by name’ and ‘by value’. It has been proved that these two ways are not operationally equivalent, and, which is worse, the execution by name is not a denotationally equivalent transformation in the logic of partial functions. Since Transparent Intensional Logic (TIL) is a partial, typed lambda calculus, we examine the validity of the rule in TIL, or rather in its computational variant the TIL-Script language. We show that there are contexts in which the rule by name can be validly applied. The main result is the specification of such contexts, and comparison with the reduction by value. To this end, we present a tool that recognizes a context in which a formal parameter of a given calling procedure occurs and interactively navigates the user to a correct way of reduction. In case of an invalid way the program informs the user about the problem and warns against undesirable side effects. As a result, the program proposes to execute the rule by value.

Intensional epistemic logics are not apt for handling properly the specification of communication and reasoning of resource-bounded agents in a multi-agent system. They oscillate between two unrealistic extremes: either the explicit knowledge of an ‘idiot’ agent, deprived of any inferential capabilities, or the implicit knowledge of an agent who is a logical/mathematical genius. The goal of this paper is to introduce the notion of inferable knowledge of a rational yet resource-bounded agent. The stock of inferable knowledge of such an agent a is the closure of a chain-of-knowledge sequence validly derivable from a's existing stock of explicit knowledge via one or more rules of inference that a masters. We are using Pavel Tichý's Transparent Intensional Logic as our framework. This logic models knowing as a relation-in-intension between an agent and a construction (a hyperintensional mode of presentation of a possible-world proposition) rather than a set of possible worlds or a piece of syntax. We motivate the restriction of the epistemic closure principle to inferable knowledge, present the theoretical framework, define the concept of inferable knowledge, and explain the technicalities of the so restricted closure principle.

In this paper we introduce the Learning Management System (LMS) eLogika that has been developed in our department for teaching mathematical logic. There were many reasons that led us to the decision to develop such a system, including inter alia a great amount of students enrolled for the courses on logic. Yet the most important reason was a specific character of logic education. As a result, the eLogika system is a web application that provides didactic material for courses on mathematical logic. Its main goal is an automatic test generation and computer-aided test evaluation based on a large database of logic tasks. The system makes it possible to adjust the level of particular tests according to students' knowledge level. To this end we developed a feedback module that makes use of statistics and data mining methods. The system can generate a large number of training as well as exam test variants for each common thematic topic. At the same time it provides effective semi-automatic methods of test rating and evaluation. In the paper we describe particular modules of eLogika with the focus on the modules of data mining and statistics.

In the last decades we got used to software applications (or computers, if you like) being everywhere and working for us. Yet sometimes they fail to work as desired. The current situation is often characterized as the second software crisis. There are many alleged causes of this state. They include, inter alia, web net overload, loss of data, inconsistency of data, intrusions by hackers, etc. etc. Yet in our opinion, the main problem is an old one. It consists in an insufficient specification of the procedures to be executed. We have been dealing with this problem since the beginning of computer era. Though there are many specification methods and languages, the problem remains very much a live issue and so far no satisfactory solution has been found. Our stance is that a declarative logical specification is needed. A serious candidate for such a high-quality declarative specification is a higher-order logic equipped with a procedural semantics. The goal of our contribution is to describe a specification method using Transparent Intensional Logic (TIL). TIL is a hyperintensional, typed, partial lambda-calculus. Hyperintensional, because the meaning of TIL-terms are not the functions/mappings themselves; rather, they are procedures producing functions as their products. Proper typing makes it possible to define inputs and outputs of a procedure. Finally, we must take into account partiality and the possibility of a failure to produce a product. A procedure may fail to produce a correct product for one of two reasons. Either the mapping the procedure produces is undefined at the argument(s) serving as point(s) of evaluation, or the procedure is ill- or under-specified, in which case the empirical execution process has undesirable results. This paper investigates, in a logically rigorous manner, how a detailed specification can prevent these problems.

We view the content of ontology via a logic of intensions. This is due to the fact that particular intensions like properties, roles, attributes and propositions can stand in mutual necessary relations which should be registered in the ontology of a given domain, unlike some contingent facts. The latter are a subject of updates and are stored in a knowledge-base state. Thus we examine (higher-order) properties of intensions like being necessarily reflexive, irreflexive, symmetric, anti-symmetric, transitive, etc., mutual relations between intensions like being incompatible, being a requisite, being complementary, and so like. We also define two kinds of entailment relation between propositions, viz. mere entailment and presupposition. Finally, we show that higher-order properties of propositions trigger necessary integrity constraints that should also be included in the ontology. As the logic of intensions we vote for Transparent Intensional Logic (TIL), because TIL framework is smoothly applicable to all three kinds of context, viz. extensional context of individuals, numbers and functions-in-extension (mappings), intensional context of properties, roles, attributes and propositions, and finally hyper-intensional context of procedures producing intensional and extensional entities as their products.