This short paper is an abridged version of [1], where we introduce a framework for the dynamic generation of novel knowledge obtained by exploiting a recently introduced extension of a Description Logic of typicality able to combine prototypical descriptions of concepts. Given a goal expressed as a set of properties, in case an intelligent agent cannot find a concept in its initial knowledge base able to fulfill all these properties, our system exploits the reasoning services of the Description Logic \(\mathbf{T}^{\textsf {\tiny CL}}\) in order to find two concepts whose creative combination satisfies the goal.
In this work the logical analysis is carried out for the predetermined subject domain, that represents an object and its describing characteristics in terms of variable-valued logic, besides the set of algorithms operating at the given domain is studied. The study developed logical procedure for creation of the correct algorithms that analyze subject domain, model the knowledge base for the set objects, minimize it and allocate a unique feature set for each object.
What role does the study of natural language play in the task of developing a unified theory and common model of cognition? Language is perhaps the most complex behaviour that humans exhibit, and, as such, is one of the most difficult problems for understanding human cognition. Linguistic theory can both inform and be informed by unified models of cognition. We discuss (1) how computational models of human cognition can provide insight into how humans produce and comprehend language and (2) how the problem of modelling language processing raises questions and creates challenges for widely used computational models of cognition. Evidence from the literature suggests that behavioural phenomena, such as recency and priming effects, and cognitive constraints, such as working memory limits, affect how language is produced by humans in ways that can be predicted by computational cognitive models. But just as computational models can provide new insights into language, language can serve as a test for these models. For example, simulating language learning requires the use of more powerful machine learning techniques, such as deep learning and vector symbolic architectures, and language comprehension requires a capacity for on-the-fly situational model construction. In sum, language plays an important role in both shaping the development of a common model of the mind, and, in turn, the theoretical understanding of language stands to benefit greatly from the development of a common model.
The number of Internet users increases and the Internet is part of people’s daily lives, as a result, the behavior of the user becomes free and informal. This is the basis of the assumption that the manner of user actions on the Internet has become a factor that can be used by authentication using artificial intelligence. In turn, existing works related to users’ web browsing behavior-based authentication with using machine learning do not analyze some important behavioral user’s characteristics, such as patterns of behavior or user behavior on a frequently visited resource. It causes to suggest own features and check their contribution to the accuracy of the system. The aim of this work is to study the possibility of introducing a map of clicks, bigrams, trigrams of frequent web pages and their domains, evaluation of the contribution of added features. In this work, we replace the web pages’ genre classification by domain classification and don’t take into account the spikes in views. We have created a system based on artificial intelligence. As a work result, we have shown a significant improvement in the accuracy of the system using the click map and a slight improvement in the use of bigrams and trigrams.
When processing semantic information, there are difficulties in modeling the behavior of an individual with a variation in his behavior, which is accompanied by a change in its properties. This paper presents a semantic model that is able to take into account the effect of variation in behavior based on an individual-as-process representation. The problem of interaction between individuals who have differing intentions at some stage is considered, which is taken into account by attributing various properties to the information processes representing them. During the development of events at a later stage, individuals may approve similar intentions, which is taken into account by attributing equivalent properties to the information processes representing them. In the future, the approved properties may again differ, requiring the presentation of different processes. The presented semantic model is distinguished by conciseness and minimum of used mathematical tools, which increases its practicality.
In our days, robotics development grow and became fast changing industry, robots spread over the world far more than any time before. Most of all, robots have presented by manufacturing robots, but robots capable to move can be more flexible to give a solution to even new kinds of problems. Hexapods are one of those kind of robots. Today they are widely known, but they are not widespread, despite their advantages, and most frequently using in research purposes. One of the main problem is that locomotion can be done by many different gaits. At the same time, hexapods have six legs, that leads to complexity of control algorithm, which must provide correct positioning for all legs at any moment in time. But to simplicity, often only one specific gait is using. In this paper, we propose system that is able to work with multiple gaits simultaneously. This system allows robot to use different methods of locomotion which are more efficient in specific situations. As a proof of concept was implemented control software. It respond for locomotion, saving different gaits and their switching, even in movement. The result of the paper is an automated robot locomotion system.
The practice of using the Viola-Jones algorithm and its modifications to solve the problem of finding objects of interest (OI) in the image frame is analyzed. It is shown that the Viola-Jones algorithm is usually used in conjunction with other algorithms in order to solve a complex task-searching for OI, identifying and analyzing its characteristic features. The relevance of the unification of the applied computing means for solving the above complex problem is underlined. The main computational procedures of the Viola-Jones algorithm are considered: obtaining the integral form of the representation of the input image frame, processing the Haar features (HF), implementation of the cascade classifier. Presented and analyzed options for building a neural network (NN) for the implementation of these procedures. The possibility of expanding the functionality of the Viola-Jones algorithm in its implementation based on the NN is shown. The results of an experimental approbation approach are considered in solving the problem of isolating a person’s face with the subsequent isolation of the eye and pupil areas in order to assess their motor activity.
M-Path is an embodied conversational agent developed to achieve natural interaction using empathic behaviors. This paper is aimed to describe the details of the conversational management system within the M-Path framework that manages dialogue interaction with an emotional awareness. Our conversational system is equipped with a goal-directed narrative structure that adapts to the emotional reactions of the user using empathy mechanisms. We further show the implementation and a preliminary evaluation of our system in a consultation scenario, where our agent uses text-based dialogue interaction to conduct surveys.
The peculiarities of the batch data transmission networks make it possible to use covert channels, which survive under standard protective measures, to perform data leaks. However, storage covert channels can be annihilated by means of limiting the flow capacity, or by use of encryption. The measures against storage covert channels cannot be implemented against timing covert channels (TCCs), otherwise their usage has to be conditioned by certain factors. For instance, while packet encryption an intruder still possesses the ability to covertly transfer the data. At the same time, normalization of inter-packet delays (IPDs) influences the flow capacity in a greater degree than sending fixed-length packets does. Detection can be called an alternative countermeasure. At the present time, detection methods based on artificial intelligence have been widespreadly used, however the possibility to implement these methods under conditions of a covert channel parametrization has not been investigated. In the current work, we study the possibility to implement artificial intelligence for detecting TCCs under conditions of varying covert channel characteristics: flow capacity and encoding scheme. The detection method is based on machine learning algorithms that solve the problem of binary classification.
Attention deficit-hyperactivity disorder is considered a mental disease that affects a significant number of the world’s youth population. Brain-computer interfaces have been used to study and treat this mental disease. In this paper, we present the current state of unmanned aerial vehicles controlled by mental commands. We hope this study can be useful to guide future research focused to develop brain-computer interfaces able of controlling unmanned aerial vehicles for therapeutic purposes.
We have a behavior experiment using pattern task abstracting cooperative behaviors that require intention estimation and action switching to specific goals. And we have analyzed strategies to adjust cooperative intention estimations. In this research, we constructed an agent model that have three strategies of “random selection”, “self-priority selection”, and “other agent’s target pattern estimation”. Moreover the decision making process was verified by simulation.
The biologically inspired Meaningful-Based Cognitive Architecture (MBCA) integrates the subsymbolic sensory processing abilities found in neural networks with many of the symbolic logical abilities found in human cognition. The basic unit of the MBCA is a reconfigurable Hopfield-like Network unit (HLN). Some of the HLNs are configured for hierarchical sensory processing, and these groups subsymbolically process the sensory inputs. Other HLNs are organized as causal memory (including holding of multiple world views) and as logic/working memory units, and can symbolically process input vectors as well as vectors from other parts of the MBCA, in accordance with intuitive physics, intuitive psychology, intuitive scheduling and intuitive world views stored in the instinctual core goals module, and similar learned views stored in causal memory. The separation of data flow into the subsymbolic and symbolic streams, and the subsequent re-integration in the resultant actions, are explored. The integration of logical processing in the MBCA predisposes it to a psychotic-like behavior, and predicts that in Homo sapiens psychosis should occur for a wide variety of mechanisms.
Natural language text consists of an author’s or narrator’s text and direct speech fragments. They have different speakers so they could use different vocabularies and syntactic structures. In order to analyze the dependency of vocabulary and sentence structure on speaker, it is necessary to attribute each text fragment to its speaker. The results of such analysis can be used in natural language text generation tasks, allowing to convey different narrative voice depending on the purpose of the generated text. The authors developed a set of rules for attributing direct speech fragments to speaking characters, created a method of direct-speech scene analysis and implemented it in a software tool. In order to evaluate the accuracy of the attribution of direct speech fragments to speakers, an experiment was carried out. The results of the experiment show the viability of the developed method and allow to improve it for further use. The potential applications of the developed method and the software tool are discussed.
Emoticons are one of the means of communication widely used by Internet users. Emoticons are used to express information that cannot be fully transmitted only by text, such as emotions or feelings. However, not all emoticons are easily understandable. In this research, we aimed at estimating such general level of comprehensibility, or “meaning ambiguity”, defined by their correspondence with linguistic expressions, such as onomatopoeia. The method could help users apply more meaningful and understandable emoticons in their everyday communication and improve their online communication.
This paper presents a graph-theoretical model of ontologies of subject domains. An ontology represented as a weighted graph. At the vertices of the graph are concepts. The edges of the graph marked the relationship between concepts. In addition, the basic operations on the ontology graph representations introduced for ontology editing in the semantic search system.
The article presents a new approach to building semantic maps based on the use of the eBICA model and the calculation of neurophysiological correlates of behavioral motives based on fMRI data. In this study, a social videogame paradigm was used in combination with fMRI and other recording tools. The obtained data show the complex neural network dynamics of behavioral motives distributed on the cortical and subcortical regions of the brain, related to the interaction of a person with the collaborative non-player character powered by the eBICA cognitive architecture.
This paper describes the results of a work to develop an algorithm for analyzing images of embossed impressions in paper documents under oblique lighting. The described algorithm could also be used for recognition of similarly-structured objects, for example, some of biological structures. This type of analysis is necessary during forensic analysis of certain security features of paper documents. Part of this analysis is determining to which category new, uncategorized impression belongs to. This research explores the potential for automation of this task using neural networks. The core element of the algorithm is a neural network which determines the similarity between two embossed impressions. The paper describes the structure of the algorithm, a method for creating an image database for training and testing, as well as testing results for proposed algorithm.
