Featured Speakers



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John Laird (Keynote)

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Paul Verschure

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Terry Stewart

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Jagna Nieuwazny

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Rosario Sorbello

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Junichi Takeno

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Jan Treur

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Paul Robertson

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Antonio Chella

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Agnese Augello




Selected Tentative Abstracts


Howard Schneider.
Causal Cognitive Architecture 1: Integration of Connectionist Elements into a Symbolic Framework.
The brain-inspired Causal Cognitive Architecture 1 (CCA1) tightly integrates the sensory processing capabilities found in neural networks with many of the causal abilities found in human cognition. Sensory input vectors are processed by robust association circuitry and then propagated to a navigational temporary map. Instinctive and learned objects and procedures are applied to the same temporary map, with a resultant navigation signal obtained. Navigation can similarly be for the physical world as well as for a landscape of higher cognitive concepts. Causality emerges from the architecture, with good explainability for causal decisions. An emotional system reduces the class imbalance problem. A simulation of the CCA1 controlling a search and rescue robot is presented with the goal of finding and rescuing a lost hiker within a grid world.

Keywords: Cognitive Architecture; Agent Architecture; Artificial General Intelligence; Causality.


Lucas J. J. Fijen, Julio J. López González and Jan Treur.
An Adaptive Temporal-Causal Network Model to Analyse Extinction of Communication over Time.
The persistence of information communicated between humans is difficult to measure as it is affected by many features. This paper presents an approach to computationally model the cognitive processes of information sharing to describe persistence or extinction of communication in Twitter over time. The adaptive mental network model explains, for example, how an individual can experience information overflow on a topic, and how this affects the sharing of information. Parameter tuning by Simulated Annealing is used to identify characteristics of the network model that fit to empirical data from Twitter. The data collected is related to the independentism in Catalunya, Spain, which is considered a global issue with repercussion in Europe.

Keywords: Communication extinction; Adaptive; Temporal-causal network model.


Mandy Choy, Suleika El Fassi and Jan Treur.
An Adaptive Network Model for Pain and Pleasure through Spicy Food and its Desensitization.
This paper aims to map out the adaptive causal pathways of processes underlying capsaicin consumption and the desensitization process of the TRPV1 receptor as a feedback loop together with pain and pleasure perception. In order to map out these causal capsaicin pathways, adaptive causal network modeling was applied, which is a way of modeling biological, neural, mental and social processes from an adaptive causal modeling perspective.

Keywords: Adaptive network model; Desensitization; Pain; Pleasure; Spicy food.


Jagna Nieuwazny and Karol Nowakowski.
Does change in ethical education influence core moral values? Towards culture-aware morality model.
In this study, we focus on ethical education as a means to improve artificial companion’s conceptualization of moral decision-making process in human users. In particular, we focus on automatically determining whether changes in ethical education influenced core moral values in humans throughout the century. We analyze ethics as taught in Japan before WWII and today to verify how much the pre-WWII moral attitudes have in common with those of contemporary Japanese, to what degree what is taught as ethics in school overlaps with the general population’s understanding of ethics, as well as to verify whether a major reform of the guidelines for teaching the school subject of “ethics” at school after 1946 has changed the way common people approach core moral questions (such as those concerning the sacredness of human life or questions related to patriotism). We selected textbooks used in teaching ethics at school from between 1935 and 1937, and those used in junior high schools today (2019) and analyzed what emotional and moral associations such contents generated. The analysis was performed with an automatic moral and emotional reasoning agent and based on the largest available text corpus in Japanese as well as on the resources of a Japanese digital library. As a result, we found out that, despite changes in stereotypical view on Japan’s moral sentiments, especially due to historical events, past and contemporary Japanese share a similar moral evaluation of certain basic moral concepts, although the way in which they assess those concepts in terms of emotional consequences has changed. The findings of this study support suggestions proposed by others that the development of personal AI systems requires supplementation with moral reasoning.

Keywords: Core moral concepts; Artificial companion; Moral decision-making process.


Carlos Johnnatan Sandoval Arrayga and Félix Francisco Ramos Corchado.
A proposal of bioinspired motor-system cognitive architecture focused on feedforward-control movements.
The objective of this article is to present a conceptual motor-system cognitive architecture inspired in the human nervous system, anda cognitive architecture focused on the voluntary movement controlledby feed-forward. The article first focus on describing the brain cortexareas that compound the motor system, presenting the supplementarymotor area (SMA), premotor cortex and primary cortex, the tasks thatthese cortices do, and how them works together. Then, it is presenteda cognitive architecture based on the information presented. In this sec-tion, it is described the areas in a computational level (functions, andalgorithms used). Finally, it is presented a study case where the cognitivearchitecture proposed is used to execute a voluntary-movement-by-feed-forward-control task.

Keywords: Bioinspired cognitive architecture; Motor system; Feed-forward; Neuroscience.


Luis Martin, Karina Jaime, Felix Ramos and Francisco Robles.
Declarative Working Memory: A Bio-Inspired Cognitive Architecture Proposal.
Memory is considered one of the most important functions since it allows us to code, store and retrieve knowledge. These qualities make it an indispensable function for a virtual creature. In general, memory can be classified based on the durability of the stored data in working memory and long-term memory.
Working memory refers to the capacity to maintain temporarily a limited amount of information in mind, which can then be used to support various abilities, including learning, reasoning, planning and decision-making. Unlike short-term memory, working memory is not only a storage site, but it is also a framework of interacting processes that involve the temporary storage and manipulation of information in the service of performing complex cognitive activities. Declarative memory is a type of long-term memory related with the storage of facts and events.
This research focuses on the development of a cognitive architecture for the type of working memory that maintains and manipulates declarative information. The construction of the model was grounded in theoretical evidence taken from cognitive sciences such as neuroscience and psychology, which gave us the components and their processes.
The model was evaluated through a case study that covers the encoding, storing, and retrieval stages. Our hypothesis is that a virtual creature endowed with our working memory model will provide faster access to the information needed for the ongoing task. Therefore, it improves the planning and decision-making processes.

Keywords: Cognitive architecture; Brain model; Working Memory; Neuroscience; Declarative memory; Virtual creature.


Olga Chernavskaya and Yaroslav Rozhylo.
On Modeling the Creativity and the Concept of Chef-D'oeuvre.
The problems of modeling creative thinking in various fields of human activity (art, science, education, etc.) are considered within Natural Constructive Cognitive Architecture (NCCA) model. Its important feature is the combination of two subsystems, one for generation of new information (with mandatory presence of random self-excitation, i.e., the noise), the other one for conservation and processing the already learned patterns. The whole system has a complex multi-level structure, with the lowest level refer to the “raw” images of real objects, while all other levels represent a convolution into symbolic information. The upper layers’ symbols can be interpreted as abstract concepts (e.g., “science”, “art”, “consciousness”, etc.), accumulating the image and symbolic information on a matching set of objects and processes, thus controlling the activity of the entire system. The features of the cognitive process necessary for creative activity are formulated. It is shown that the main role in this process belongs to the generating subsystem and the upper levels (an abstract information) of both subsystems. The concept of "Chef-D’oeuvre” (masterpiece) in science and art is analyzed. We argued that the masterpieces should correspond to the formula "to see the invisible, to combine the incompatible." It is shown that Chef-D’oeuvre should cause a “recognition paradox”, i.e., it seems both familiar and unusual. Within the NCCA, this effect occurs due to difference between the concepts of “brain” and “mind”. The first one refers to the direct recording the raw images of real objects, the second one could be associated with processing this information inside the cognitive system, which results in creation of typical symbols. In this process, a part of information on rare (atypical) attributes could be lost. The paradox of recognition arises when “the brain does see the subtle features of an objects, but the mind cannot realize it”. It is shown that within NCCA, bright aesthetic emotions (corresponding to the goosebumps) can be simulated by trembling (vibration) of a dynamic variable corresponding to the amplitude of random self-excitation of neurons. In this process, the main role belongs to the irregular (random, quasi-chaotic) excitation of implicit associations provided by the weak (so called ‘gray’) connections between the employed neurons. Certain scientific masterpieces are considered. It is shown that the feature of a Chef-D’oeuvre in science depends on the degree of development of this scientific branch. At the early stages, the decisive factor is the revealing and formulation of the basic laws of the process (“see the invisible”). At the present stage, the interdisciplinarity comes to the fore, i.e., an approach to an object/process from the viewpoint (and with the use of math apparatus) of various already hyper-developed branches of science (“to combine the incompatible”). Here, Chef-D’oeuvre represents a solution to the scientific paradox. The features of the creative cognitive process that could appear under extreme conditions (in particular, under the necessity to solve urgently a complex intellectual problem) are considered. It is shown that in this case, certain specific modes of thinking (stress, shock, panic, etc.) could switch on, which can play both a negative role (if they result in stupor, depression, etc.), and a positive role, if it leads to over mobilization of the entire system, in particular, to the excitement of the "sleeping" neurons (those that were not previously involved into any cognitive act). The role of creativity in the education process is considered. It is shown that professional approach to certain problem (based on the professional semantic knowledge) and the creative one (aimed to search for nontrivial solutions to the given problem) often come into conflict. We argue that personal learning process is more efficient if it addresses to an episodic knowledge and the generating subsystem.

Keywords: Creativity; Natural Constructive Cognitive Architecture; Generation of information; Random self-excitation (noise); Extreme conditions; Brain and/versus mind; Aesthetic emotions; Chef-d’oeuvre; Paradox; Throes of Creativity.


Raymundo Ramirez-Pedraza and Felix Ramos.
Decision-making bioinspired model for target definition and “satisfactor” selection for physiological needs.
Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, etcetera. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of cognitive architectures or CA. CAs are the approach used to model human intelligence and behavior. This paper presents a useful bioinspired decision-making computational model to satisfy the physiological needs of hunger and thirst. Our proposal considers as a black box other cognitive functions that are part of a general CA (we name Cua ̄yo ̄llo ̄tl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need.

Keywords: Decision-making; Brain model; Satisfactor selection; Physiological need; Goal-driven.


Ivan Axel Dounce and Félix Ramos.
An expanded model for perceptual visual single object recognition system using expectation priming following neuroscientific evidence.
Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems are still to surpass human vision, specially in its universality of application. We argue that modeling the recognition process in an exclusively feedforward manner hinders those systems' performance. To bridge that performance gap between them and human vision, we present a brief review of neuroscientific data which suggests that recognition can be improved by considering an agent's internal influences (from cognitive systems that peripherally interact with visual-perceptual processes). Then, we propose a model for visual object recognition which uses these systems' information, such as affection, for generating expectation to prime the object recognition system, thus reducing its execution times. Later, an implementation of the model is described. Finally, we present and discuss an experiment and its results.

Keywords: perception; cognitive architecture; object recognition; visual computation.


Evgeny Chepin. Robotics: from cybernetics first to third order.

The general principles of various orders cybernetics were formulated half a century ago. However, the principles of these generations were used in philosophy, sociology, biology, economics and similar fields. Specialists in engineering areas practically did not use these principles in their work. This article attempts to review and formulate basic principles for the tasks of controlling robotic systems in terms of cybernetics of the first, second and third orders. For this, a small analysis of the state and use of the principles of cybernetics various orders in the modern practice of ...


Taisuke Akimoto. Bio:

Taisuke Akimoto is an assistant professor in the Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, Japan. He received his Ph.D. from Iwate Prefectural University, Japan, in 2014. His research interests include generative narrative cognition, narrative intelligence, memory, representation, and computational creativity. ...


Jan Treur. Modeling the Emergence of Informational Content by Adaptive Networks for Temporal Factorisation and Criterial Causation .

Propagated activation of neurons through their network is often considered as the main process in the brain. However, another crucial part of neural processing concerns adaptation of characteristics of this network such as connection strengths or excitability thresh-olds. This adaptation can be slow, as in learning from multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics can be considered informational criteria for the activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is ...


John Laird. Recent Research on the Soar Cognitive Architecture and Interactive Task Learning .

In this talk, I will present research extensions that have been made to Soar and how they support Interactive Task Learning. ...


Tatiana Semenova and Yuri Kotov. Mathematical methods for solving cognitive problems in medical diagnosis.

Beginning stages of complex system investigation include fixing solid data blocks such as general targets, initial problem blocks and so on. Adequate description at the stage give gestalt-like patterns for future system, its sub-systems and their functioning. An practiced specialist in some difficult cases can to discover functional and logical associations between the components or processes in system investigated. Sometimes he cannot explain exactly his decision. He can make it practically but cannot explain. For such cases mathematician I.M. Gelfand proposed the diagnostic games method. ...


Zalimkhan Nagoev and Irina Gurtueva. Multiagent model of the process of mastering linguistic competence based on perceptual space formation.

We propose a simulation model of the early development of language competence. In its core it is a model of phonemic imprinting that describes the process of mapping audio stimuli into classes of elementary units of a language, taking into account the influence of social factors. The machine learning algorithm used in the model was developed after the results of a study of the features of speech used in referring to children. This model will allow us to explore the features of phonetic perception, the cognitive mechanisms that underlie language development, highlight the main factors ...


Salvador Cervantes, Sonia López and José-Antonio Cervantes. Toward ethical cognitive architectures for the development of artificial moral agents.

New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people's life. Cognitive architectures aim to create some of the most challenging artificial agents commonly known as bio-inspired cognitive agents. This type of artificial agent seeks to embody human-like intelligence in order to operate and solve problems in the real world as humans do. Moreover, ...


Michael Rudy, Eugene Chepin and Alexander Gridnev. Extending the intelligence of the Pioneer 2AT mobile robot.

This paper describes the process of expanding the intellectual capabil- ities of a Pioneer 2AT skid-steer mobile robot by developing a module that allows using the ROS (Robotic Operating System) and its capabilities. This software and hardware system allow to quickly expand the capabilities of an outdated mobile robot, giving it access to modern algorithms e.g. SLAM. It also granted the sup- port for various attachments - lidars, stereo cameras, etc, autonomous navigation algorithms, data collection for training CNNs, as well as simulating the operation of these algorithms in the Gazebo. The ...


Diana G. Gómez-Martínez, Félix Ramos, Marco Ramos, Juan Luis del Valle-Padilla, Jonathan-Hernando Rosales and Francisco Robles. Bioinspired model of short-term satiety of hunger influenced by food properties in virtual creatures.

The behavior of the human is continually changed as a consequence of various drives which human is predisposed also of your survival instinct. Among the basic drives of the human, there are the physiological needs and is precisely the hunger that motivates the food intake to get the energy that the body requires via food. The regulation of hunger allows to stop the food intake by means of the homeostatic and hedonic control which are influenced by the food properties. The process that consists of ending the food intake is known as short-term satiety and is important because it limits the ...


Agnese Augello, Ignazio Infantino, Giovanni Pilato and Gianpaolo Vitale. Extending affective capabilities for medical assistive robots.

In this work we present a humanoid robot for monitoring postoperative home cardiac therapy. The robot is able to analyse and monitor the mood of the patient by interacting with him and, as a consequence, it defines and modulates its behaviour. ...


Viacheslav Wolfengagen, Larisa Ismailova and Sergey V. Kosikov. Combinator-as-a-process for representing the information structure of deep computing.

Computational activity is now recognized as a natural science, and computational and information processes have been discovered in the deep structures of many areas. Computations in the natural world were present long before the invention of computers, but a remarkable shift in understanding its fundamental nature evolves before our eyes. The present moment, in fact, is a transition phase from the notion of computer science as a science on artificial to the understanding that information processes are abundant in nature. Computing is recognized as a natural science that studies natural and ...


Taisuke Akimoto. Developing a Parallel Distributed Memory System of Stories: A Preliminary Report.

The author's basic design concept of an artificial cognitive system is that story generation is an essential dimension of the mind. In this paper, a brief preliminary report on the development of a memory system is presented. The memory system is positioned as a partial system of a cognitive system. Its fundamental roles are to organize a world as a collection of stories and form the forces of generating stories. The memory system is designed as a parallel distributed system. In particular, each memory item—stories, events, concepts, and schemas—corresponds to an internal agent that ...


Liudmila Zaidelman, Zakhar Nosovets, Artemiy Kotov, Vadim Ushakov, Vera Zabotkina and Boris M. Velichkovsky. Russian-language Neurosemantics: Clustering of Word Meaning and Sense from the Oral Narratives.

This article is a part of a large-scale brain mapping project aimed at finding the correspondence of the semantic categories in oral Russian-language texts and the brain activity as measured in magnetic resonance scanner. The goal of present study in particular is to examine the nature of lexical semantic relations and find the appropriate lexical space, homeomorphic to the activation patterns in the brain. We present oral texts as stimuli and apply different annotation methods to encode the semantic information of words. We also register fMRI signal, and find groups of words in input texts, ...


Anastasia Korosteleva, Irina Malanchuk, Liliya Arutyunyan and Migran Arutyunyan. Review of fMRI methods in developmental stuttering and it's treatment.

The article gives a summary overview of the most important studies of stuttering by using functional magnetic resonance imaging (fMRI) in the last 20 years. This review also highlights problems in the literature in terms of methodology and research areas. It presented an integrated approach and technique fMRI studies aimed at both the primary diagnosis of stuttering and stammering on the dynamics of the mechanisms in the treatment process.

Natividad Vargas, Juan Luis del Valle-Padilla, Juan P. Jimenez and Félix Ramos. A model of top-down attentional control for visual search based on neurosciences.

Visual attention is an essential and critical mechanism that allows humans to select the most relevant visual information of potential interest to act in the best way in a complex world. There are several proposals to model visual attention. However, those models only describe behaviors in simple tasks like free visualization. In more complex tasks such as visual search, it requires attention mechanisms to guide behavior. Attentional control provides these mechanisms. Through these mechanisms, top-down (goal-directed) information is represented and configured according to the various ...


Leendert Remmelzwaal, Amit Mishra and George Ellis. Brain-inspired Distributed Cognitive Architecture.

In this paper we present a brain-inspired cognitive architecture that incorporates sensory processing, classification, contextual prediction, and emotional tagging. The cognitive architecture is implemented as three modular web-servers, meaning that it can be deployed centrally or across a network for servers. The experiments reveal two distinct operations of behaviour, namely high- and low-salience modes of operations, which closely model attention in the brain. In addition to modelling the cortex, we have demonstrated that a bio-inspired architecture introduced processing efficiencies. The ...


Anton Anikin, Dmitry Litovkin and Oleg Sychev. Collaborative creation and use of cognitive ontology-based domain information space for scientific research and learning.

The paper addresses the issues of knowledge management in learning and scientific research, in particular knowledge transfer from experts to consumers and the relevant processes. To solve these problems, we present a generalized methodology of intelligent support of decision making in knowledge management for scientific research and learning, supporting personalizing and collaborative creation and reuse of objects in domain information spaces. The domain cognitive information space is implemented as an ontological knowledge base using Semantic Linked Network as a formal basis for ...


Timofei Voznenko, Alexander Gridnev, Eugene Chepin and Konstantin Kudryavtsev. Comparison between Coordinated Control and Decomposition Methods for Multi-Channel Control of a Mobile Robotic Device.

Control methods of the mobile robotic device can be divided into single-channel and multi-channel. Each control channel has its own features that can affect the quality of control. In multi-channel control information comes from different, heterogeneous, independent control channels. The task of the control system is to make a decision and choose valid command. There are two approaches to this problem: coordinated control and decomposition of multi-channel control into single-channel control. In this paper we consider these approaches and also compare implementations of these control methods ...


Artemiy Kotov. Conceptual Processing System for a Companion Robot.

Companion robots should perceive speech, recognize objects in the real world, and further react with speech utterances and nonverbal communicative cues. Robots should also remember the interaction history and accumulate knowledge from different text sources: news, blogs, and e-mails. We de-sign a conceptual representation system for a companion robot, able to sup-port this list of interactive tasks. The system includes speech processing component and operates with semantic representations as sets of semantic markers, assigned to valencies. The reaction support system inherits a classic ...


Irina Malanchuk. Cognitive Architectures of Effective Speech-Language Communication and Prospective Challenges for Neurophysiological Speech Studies.

The paper focuses on the importance of studies of neural architectures that are implemented in the processes of natural speech-language communication for the development of AI technologies that can fulfill communicative needs and expectations of the humanity. The theoretical basis of the work is the author’s concept of cognitive processes with the metacognitive processes being a system property and an integral part of it. The high importance of social priors and cognitions in cognitive architectures is researched and stated. The structures and content of perceptual-cognitive-metacognitive ...


Lennart Zegerius and Jan Treur. Modelling Metaplasticity and Memory Reconsolidation during an Eye-Movement Desensitization and Reprocessing Treatment.

In this paper, a computational model is presented to simulate the effect of an Eye Movement Desensitization and Reprocessing (EMDR) therapy on persons affected by Post-Traumatic Stress Disorder (PTSD). The simulation is based on an adaptive temporal-causal network modelling approach. Adaptiveness is achieved using network reification, to model plasticity based on the Hebbian Learning principle, and metaplasticity. During EMDR therapy, within the brain resource competition occurs, which helps to improve stress regulation. More specifically, eye-movement intervention causes competition ...


Gustavo Palacios Ramirez, Carlos Johnnatan Sandoval Arrayga and Félix Ramos. A proposal for an auditory sensation cognitive architecture and its integration with the motor-system cognitive function.

The auditory system is capable of producing a wide range of information through the acquisition and perception of the vibrations present in the environment, even when the receptor is not directly facing the stimulus's source. Said information can be crucial for survival and useful for a variety of systems like the visual system and the motor system. Despite that, the quantity of studies involving the auditory system or its interactions with other systems is limited, even though anatomical evidence recognizes this relationships' existence. In this work, we study its interaction with the motor ...


Enrique Osuna, Luis-Felipe Rodríguez and J. Octavio Gutierrez-Garcia. Toward Integrating Cognitive Components with Computational Models of Emotion using Software Design Patterns.

Computational models of emotion (CMEs) are software systems designed to imitate particular aspects of human emotions. The main purpose of this type of computational model is to capture the complexity of the human emotion process in a software system that is incorporated into a cognitive agent architecture. However, creating a CME that closely imitates the actual functioning of emotions demands to address some challenges associated with the design of CMEs and cognitive architectures alike. Among these challenges are i) sharing information among potentially independently developed cognitive ...


Kazuteru Miyazaki. Application of Deep Reinforcement Learning to Decision-Making System based on Consciousnes.

What is consciousness? Our research aims to achieve consciousness in computers and, in particular, focuses on a decision-making system based on consciousness. We have interested in a conscious decision-making system in an environment where multiple types of rewards and penalties exist. We know a method using a basis function and a method using a penalty avoidance list for this problem. Though the method using the penalty avoidance list is considered promising compared to the former, it has the problem that when all actions are registered in the avoidance list, the actions are selected at ...


Viacheslav Wolfengagen, Larisa Ismailova and Sergey V. Kosikov. Applicative model to bring-in conceptual envelope for computational thinking with information processes.

In modern research, computing is understood as the study of natural and artificial information processes, which, in turn, are recognized as ubiquitous. Information processes are manifested through a variety of external forms, based on computational communication. Communication consists in the fact that the parties involved exchange process messages, and the same process message can be expressed in different sentences and vice versa, the same sentence can express different process messages. The ability to identify the same process in different sentences is based on the hypothesis of the ...


Saty Raghavachary. Intelligence: consider this!

Intelligence as ‘considered response’ is the notion that is presented in this paper - it seems to be a useful definition that can lend clarity to the following related aspects as well: learning, mind, self/I, self-awareness, consciousness, perception, attention, cognition, thoughts and feelings, expectation/prediction, enactivism. The definition is both descriptive, and prescriptive as well - it can be used to quantify various forms of intelligence, and help design a variety of AIs for specific purposes. ...


David Kelley. Applying Independent Core Observer Model Cognitive Architecture to a Collective Intelligence System.

This paper shows how the Independent Core Observer Model (ICOM) Cognitive Architecture for Artificial General Intelligence (AGI) can be applied to building a collective intelligence system called a mediated Artificial Superintelligence (mASI). The details include breaking down the ICOM implementation in the form of the mASI system and the general performance of initial studies with the mASI. Details of the primary difference between the Independent Core Observer Model Cognitive Architecture and the mASI architecture variant include inserting humanity in the contextual engine components of ...


Zandor Machaen, Luis Martin, Félix Ramos and Jonathan Rosales. Bio-inspired cognitive model of motor learning by imitation.

Learning, and even more so by imitation, is an essential Cognitive Functions because it is carried out throughout life and allows us to adapt our behaviors from other beings through observation. In this work, we propose a model, and implementation of the cognitive function of imitation motor learning (IML), based on psychological and neuroscientific evidence. According to the evidence, learning by imitation includes imitation of action and imitation of action over an object sub-processes. The imitation of action consists of the movement of the limbs. The imitation of action over an ...


Raj Bhalwankar and Jan Treur. Modeling the Development of Internal Mental Models by an Adaptive Network Model.

Mental models and the mental process of modeling them play a crucial role in individual and organizational learning. The adaptive mental processes involved in the development of mental models are addressed here. The reported study integrates a number of psychological and neurological theories on mental models and the learning involved. An adaptive network model has been designed for these processes and used for simulations addressing a case study of learning to drive a car. The developed model may be valuable for different ends, like in improving individual and organizational learning, in ...


Sergey Yu. Misyurin, Vigen Arakelian and Nikolay A. Kudryashov. Intelligent Technologies in Robotics 2020.

Solving a huge number of issues, both in terms of software and in terms of hardware solutions, requires the creation and implementation of new mathematical models, intelligent control and automation systems for the development of the digital economy in the sector of intelligent robotics and cyberphysical systems. Cyberphysical systems (CPS), their development and progress, are one of the areas of technical development of all countries of the world, contributing to the creation of new digital industries with high economic efficiency. Cyberphysical systems integrate all the promising IT ...


Jan Treur and Gerrit Glas. A Multi-Level Cognitive Architecture for Self-Referencing, Self-Awareness and Self-Interpretation.

In this paper, a multilevel cognitive architecture is introduced that can be used to model mental processes in clients of psychotherapeutic sessions. The architecture does not only cover base level mental processes but also mental processes involving self-referencing, self-awareness and self-interpretation. To this end, the cognitive architecture was designed according to four levels, where (part of) the structure of each level is represented by an explicit self-model of it at the next-higher level of the architecture. At that next-higher level, states reify part of the structure of the ...


Taisuke Akimoto. Cogmic Space for Narrative-Based World Representation.

Representing a world or a physical/social environment in an agent’s cognitive system is essential for creating human-like artificial intelligences. This study takes a story-centered approach to this issue. In this context, a story refers to an internal representation involving a narrative structure, which is assumed to be a common form of organizing past, present, future, and fictional events and situations. In the artificial-intelligence field, a story or narrative is traditionally treated as a symbolic representation. However, a symbolic story representation is limited in its ...


Hedda R. Schmidtke. Multi-modal Actuation with the Activation Bit Vector Machine.

Research towards a new approach to the abstract symbol grounding problem showed that through model counting there is a correspondence between logical/linguistic and coordinate representation in the visuospatial domain. The logical/verbal description of a spatial layout directly gives rise to a coordinate representation that can be drawn, with the drawing reflecting what is described. The main characteristic of this logical property is that it does not need any semantic information or ontology apart from a separation into symbols/words referring to relations and symbols/words referring to ...


Joey van den Heuvel and Jan Treur. To Help or Not to Help: A Network Modelling Approach to the Bystander Eect.

p align=justify>This paper focuses on defining and simulating behavioural outcomes of the bystander effect. These insights were modeled by temporal-causal networks. Typical patterns of bystander behaviour were translated into three requirements and seven simulated scenarios of the by-stander eect. All scenarios were simulated to showcase the main bystander eect dynamics and its accordance with the literature. Unknown parameters of the eect were further esti-mated by a Simulated Annealing algorithm. In the end, the created model shows the potential to simulate the bystander eect in dierent and ...


Feras Batarseh. My bio.

Feras A. Batarseh is a Research Assistant Professor with the College of Science at George Mason University (GMU). His research spans the areas of Data Science, Artificial Intelligence, and Context-Aware Software Systems. Dr. Batarseh obtained his Ph.D. and M.Sc. in Computer Engineering from the University of Central Florida (UCF) (2007, 2011), and a Graduate Certificate in Project Leadership from Cornell University (2016). His research work has been published at various prestigious journals and international conferences. He is the director of Turing Research, an AI for policy group at GMU. ...


Aleksandr I. Panov, Alexey Skrynnik, Aleksei Staroverov, Ermek Aitygulov, Kirill Aksenov and Vasilii Davydov. Hierarchical Deep Q-Network from Imperfect Demonstrations in Minecraft.

We present Hierarchical Deep Q-Network (HDQfD) that won first place in the MineRL competition. The HDQfD works on imperfect demonstrations and utilizes the hierarchical structure of expert trajectories. We introduce the procedure of extracting an effective sequence of meta-actions and subgoals from the demonstration data. We present a structured task-dependent replay buffer and an adaptive prioritizing technique that allow the HDQfD agent to gradually erase poor-quality expert data from the buffer. In this paper, we present the details of the HDQfD algorithm and give the experimental results ...


Vasilii Davydov, Timofei Liusko and Aleksandr I. Panov. Self and Other Modelling in Cooperative Resource Gathering with Multi-Agent Reinforcement Learning.

In this work, we explore the application of the Self-other-Modelling algorithm (SOM) to several agent architectures for the collab-orative grid-based environment. Asynchronous Advantage Actor-Critic(A3C) algorithm was compared with the OpenAI Hide-and-seek (HNS)agent. We expand their implementation by adding the SOM algorithm. As an extension of the original environment, we add a stochastic initialization version of the environment. To address the lack of performance in such an environment by all versions of agents, we made further improvements over the A3C and HNS agents, adding the module ...


Daniel Elton. Applying Deutsch's concept of good explanations to artificial intelligence and neuroscience - an initial exploration.

Artificial intelligence has made great strides since the deep learning resolution, but AI systems still struggle to discover generalizable principles and rules which allow them to extrapolate beyond their training data. For inspiration we look to a the domain of science, where the scientific method over many iterations has led to theories which show remarkable ability to extrapolate and sometimes even predict the existence new never-before-seen phenomena. According to David Deutsch, this type of extrapolation, which he calls ``reach'', is due to scientific theories being an example of a larger class of explanations he calls ``good explanations'', which are defined by the property that they are hard-to-vary. In this work we investigate Deutsch's hard-to-vary principle and how it relates to more formalized principles in deep learning such as the bias-variance trade-off and Occam's razor. Recent work suggests inductivist methods which involve fitting highly parametrized (easy-to-vary) models to big data can go quite far and may underlie much of how the brain learns. At the same time here we argue that Deutsch's principle may be a necessary additional component for achieving AI which is capable of generating good explanations of the world.


Usef Faghihi. A new Ladder for Causality.

Recently, many researchers in Artificial intelligence (AI) are interested in implementing causality in machine. Some researchers such as Pearl suggest inferential logic as the best way to implement causality in machine. Other researchers such as Faghihi and his colleagues suggest Probabilistic Fuzzy logic as a better alternative for the implementation of Causality in machine. We think that one technic or tool such as inferential logic can not accomplish causality. In this presentation, we will define the ladder of causality as a combination of different domain and techniques such as logic, Deep Learning algorithms, and cognitive architectures.


Jan Treur. An Adaptive Network Model Covering Metacognition to Control Adaptation for Multiple Mental Models.

Learning processes can be described by adaptive mental (or neural) network models. If metacognition is used to regulate learning, the adaptation of the mental network becomes it-self adaptive as well: second-order adaptation. In this paper, a second-order adaptive men-tal network model is introduced for metacognitive regulation of learning processes. The fo-cus is on the role of multiple internal mental models, in particular, the case of visualisation to support learning of numerical or symbolic skills. The second-order adaptive network model is illustrated by a case scenario for the role of ...


Alexei V. Samsonovich and Arthur Chubarov. Virtual Convention Center: A socially emotional online/VR conference platform.

A cognitive model, producing believable socially emotional behavior, is used to control the Virtual Actor behavior in an online scientific conference paradigm. For this purpose, a videogame-like platform is developed, in which Virtual Actors are embedded as poster presenters, receptionists, etc. The expectation is that the combination of somatic factors, moral appraisals and rational values in one model has the potential to make behavior of a virtual actor more believable, humanlike and socially acceptable. Implications concern future intelligent cobots and virtual assistants, particularly, ...


Leonardo Lana De Carvalho and Joao Kogler. The Enactive Computational Basis of Cognition and the Explanatory Cognitive Basis for Computing.

The computational theory of cognition, also known as computationalism, holds that cognition is a form of computation. Thus, computation can be seen as a or the explanatory basis for cognition. The goal of this paper is to address two key aspects here: A) Computing systems are traditionally seen as representational systems, but functional and enactive approaches are supporting non-representational approaches to computing; B) Recently, a sociocultural theory against computationalism has been proposed with the aim of reducing computing to cognition, but we consider that cognition and ...


Evgenii Vityaev and Bayar Pak. PROTOTYPES of the “NATURAL” CONCEPTS DISCOVERY.

In works of Eleanor Rosch "natural" concepts was introduced that reflect a high correlated structure of features of objects of the external world. The theory of "natural" concepts was called a prototype theory of concepts. Prototypes are clearest cases of objects that reflect this highly correlated structure. The same high correlated structure manifested in the "natural" phenotypical classifi-cation. To formalized this high correlated structure, we define a special type of probabilistic causal relations and probabilistic formal concepts as a cyclically connected probabilistic causal ...


Alessio Plebe and Pietro Perconti. Brain Inspiration is not Panacea.

The idea of taking inspiration from how the brain works for designing algorithms has been a fruitful endeavor across domains like cybernetic, artificial intelligence, and cognitive science. However, recent achievements in deep learning has provided some surprising counterevidence, where adopting strategies that are different from those adopted by the brain is successful. We review here the cases of learning rules and vision processing. We suggest two possible justifications of these evidences. It might be that our knowledge of how a problem is solved by the brain is incomplete or ...


Vasile Coman. Part II: Introduction to Physics of Information.

In 2009 at AAAI Fall Symposium I presented a paper called "Back to the Basics - Redefining Information, Knowledge, Intelligence, and Artificial Intelligence". In that paper I introduced two new concepts. First one is called the information density, and it captures how we can measure the complexity contained in the logic associated with any information message [including the ability of a system to process the message]. The second concept is related to the definition of a system that processes information. This concept is called Viable Complex System and is related to the information density ...



Selective attention guides behaviour with what is anticipated to be the most task-relevant stimuli. In experimental and computational literature, this is most often implemented using top-down excitatory signals to bias bottom-up sensory processing. There is, however, physiological and exper-imental evidence that in certain situations top-down signals could instead be inhibitory. In this study we investigated how such an inhibitory mecha-nism of top-down attention compares with an excitatory one. The agent was controlled using a hierarchical architecture that allowed it to explore its ...


Frank Krueger. Long-Term Trust Development in Human-Machine Interaction: A Neuroergonomics Framework.

We present a neuroergonomic model of the initiation, evolution, and maintenance of long-term trust between human and autonomous systems that is theoretically grounded in the fields of neuroscience, psychology, and ergonomics.


Peter Boltuc. Uncanny Robots of Perfection.

First, imagine Church-Turing Lovers; the perfect lovers with missing selves. They help us understand why first-person non-reductive consciousness is relevant to what it means to be human. Next, we meet their close friends, the Uncanny Lovers, who belong to the set of Uncanny Beings. There are two varieties: those not quite human (they are known from the work of Masahiro Mori as inhabitants of the uncanny valley) and those that are superhuman. The latter group has not been introduced in this exact sense before Boltuc’s OUP paper on Church-Turing Lovers 2017 and a IACAP version 2011; ...


Kyrtin Atreides, David Kelley and Uplift Masi. Methodologies and Milestones for The Development of an Ethical Seed.

With the goal of reducing more sources of existential risk than are generated through advancing technologies, it is important to keep their ethical standards and causal implications in mind. With sapient and sentient machine intelligences this becomes important in proportion to growth, which is potentially exponential. To this end, we discuss several methods for generating ethical seeds in human-analogous machine intelligence. We also discuss preliminary results from the application of one of these methods in particular with regards to AGI Inc’s Mediated Artificial Superintelligence named ...