Selected Abridged Abstracts*

 

 

Angelo Cangelosi. Developmental Robotics for Language Learning, Trust and Theory of Mind. Abstract: Growing theoretical and experimental research on action and language processing and on number learning and gestures clearly demonstrates the role of embodiment in cognition and language processing. In psychology and neuroscience, this evidence constitutes the basis of embodied cognition, also known as grounded cognition (Pezzulo et al. 2012). In robotics and AI, these studies have important implications for the design of linguistic capabilities in cognitive agents and robots for human-robot collaboration, and have led to the new interdisciplinary approach of Developmental Robotics, as part of the wider Cognitive Robotics field (Cangelosi & Schlesinger 2015; Cangelosi & Asada 2022). During the talk we will present examples of developmental robotics models and experimental results from iCub experiments on the embodiment biases in early word acquisition and grammar learning (Morse et al. 2015; Morse & Cangelosi 2017) and experiments on pointing gestures and finger counting for number learning (De La Cruz et al. 2014). We will then present a novel developmental robotics model, and experiments, on Theory of Mind and its use for autonomous trust behavior in robots (Vinanzi et al. 2019, 2021). The implications for the use of such embodied approaches for embodied cognition in AI and cognitive sciences, and for robot companion applications will also be discussed.

Howard Schneider. A Non-Hybrid Neurosymbolic Solution to the Compositionality Problem. Abstract: Compositionality can be considered as finding (or creating) the correct meaning of the constituents of a non-simple language expression or visual image. Artificial neural network-based artificial intelligence systems generally perform poorly in implementing compositionality. Symbolic systems can more readily implement compositionality. Neurosymbolic systems attempt to achieve the benefits of both neural networks and symbolic logic, but they are hybrid systems of both, with the issues of trying to combine very different systems. Here we present a different, non-hybrid neurosymbolic system, ...

Peter Boltuc. BICA for Consciousness. Abstract: BICA goes beyond recommender engines or standard big data computing since brains do not work as simple big data processors. We are mid-data processors, using brains not quite as discrete computing engines. We compute using biochemical features [Sloman 2020], topological learning [Thaler 2021], forgetting [Kelley; NARS] or paraconsistent interzones [Goertzel 2022]. Recent collapse of IBM-Watson exemplifies not only IBM's proven record of mishandling novel technologies; misery brought about by technologically underinformed AI management, but, more interestingly, the need to distinguish ...

Serge Shumsky. Hierarchical AGI from first principles. Abstract: The paper provides evidence based on the free energy principle in favor of the hierarchical design of AGI. A neuro-symbolic hierarchical architecture of AGI is proposed as a development of Friston's hierarchical model of the brain.

Bradly Alicea. Super-performance: sampling, planning, and ecological information. Abstract: The connection between active perception, representation, and the limits of performance provide a path to understanding naturalistic behavior. We can take an evolutionary perspective to understand the limits of this performance, particularly extreme forms of sensory sampling and mental representation. We will discuss two categories that are hypothesized to originate in terms of coevolutionary relationships and evolutionary trade offs: supersamplers and superplanners. Supersamplers take snapshots of their sensory world at a very high rate and represent species that are highly specialized in some way. We can use a concept called Gibsonian Information (GI) to evaluate sensory sampling and the need for representation in this context. By contrast, superplanners utilize mental representations to ...

Peter Boltuc. Digital transformation and new AI. Abstract: Digital Transformation slow-started in the late 1930s with information theory used in such projects as IBM preparing bookkeeping authomata for German Concentration Camps, though more advanced forms were attained in incription breaking machines in use in WW2. The most notable are those developed by Alan Turing for Britain. This is in part, due to Turing's academic publications, especially his 1950 article in 'Mind'. This is what philosophical historiography of Luciano Floridi calls the 4th Revolution. In business history we distinguish Industrial Revolution 1 (IR1)late 1700s: steam engine; ...

Simon Duan. Emotion from P bit - Computing emotions using a Platonic computer. Abstract: Emotion is a major aspect of human conscious experience. Some common emotions include happiness, sadness, fear, anger, surprise, and disgust. Generating emotions is a desirable goal for conscious AI systems. Emotions can be detected and classified using computational methods, but whether or not emotions can truly be "computed" is still a matter of philosophical and scientific debate. Some researchers argue that emotions are subjective experiences, not just objective phenomena that can be measured or quantified. Therefore, it is unlikely to be computable. In this paper, it is proposed ...

Yuehu Ji, David Gamez and Chris Huyck. Associative Memory with Biologically-Inspired Cell Assemblies. Abstract: Associative memory is a central cognitive task. However, the actual biological architecture that supports this memory is not currently known, so sim- ulating with biologically plausible neurons and topologies is an ideal mechanism to improve understanding of associative memory. Simulations of spiking net- works that perform associative memory tasks lay the groundwork for utilizing biological neurons in cognitive tasks. Specifically, this paper explores simula- tions of spiking networks that perform associative memory tasks using Hebbian cell assemblies of neurons to represent nodes ...

Sophie Hendrikse and Jan Treur. New Analysis and Modeling Directions for Multimodal Social Interaction. Abstract: Although there is much literature on multimodal interaction, multimodal synchrony analysis, and related behavioural adaptivity, mathematical formalisation and computational simulation of it is a nontrivial topic. Moreover, the subjective, agent-oriented perspective on synchrony analysis has not yet received much attention in the literature. This presentation provides from an agent-oriented perspective an overview of recent work on mathematical formalisation and computational simulation of multimodal interaction, subjective multimodal synchrony analysis, and related adaptivity of the interaction behaviour. It does so by exploiting the possibilities of multi-adaptive self-modeling network models for agents to analyse these dynamic and adaptive processes formally.

Kenneth Del Signore. How Language Could Have Evolved. Abstract: This paper begins to develop a biologically inspired computational model of the Hu- man language faculty and some associated thought processes. This model is developed starting from a simple proto-language, which humans are assumed to have inherited at speciation. This proto-language consists of single symbol exchange using a small set of symbols; similar to the observed gestural communication systems in the existing Great Ape families. Computationally, the model is built using a single class with the form of a Markov graph node. Instances of this node class are used to symbolically ...

Tingting Liu. Information technologies and ethics,supporting embodied agents and human-computer interactions. Abstract: Discuss information technologies and ethics which can support embodied agents and human-computer interactions. The information technologies could include Virtual Reality, Augmented Reality and other graphic techniques that can generate environment,performance or behavior of embodied agents and can support human-computer interactions. The ethics issues may cover all ethics issues for the application of Artificial Intelligence.

Wenjie Huang, Angelo Cangelosi and Antonio Chella. An Incremental Cognitive Architecture of Consciousness with Global Workspace Theory. Abstract: This paper revisits the Global Workspace Theory as a neuro-scientifically plausible theory for developing conscious cognitive architecture. Based on the discussion of the authors’ previous implementations, the Global Workspace Theory’s compatibility with the working mechanisms underneath human brains is enhanced by demonstrating cognitive features of attention and consciousness. To progress in the incremental research pathway for the architecture implementation, two principles of work are stressed. The primitive cognitive mechanisms are emphasised more than the functional complexity and ...

Kyrtin Atreides. Automated Bias and Indoctrination at Scale… Is All You Need. Abstract: The market-driven trajectory of current trends in AI combined with emerg-ing technologies and thresholds of performance being crossed create a subset of new and novel risks relating to human bias and cognition at scale. Though the topic of AI Ethics and risk has been discussed increasingly over the past few years, popular talking points and buzzwords have been used ad-versarially to steer the conversation with increasing success. This has left a subset of risks in the blind spot of most discussions, risks which have now become both urgent and imminent. Automation is actively seeking to ...

Jiajun Cheng, Zhen Liu, Tingting Liu and Yanjie Chai. Simulation of Fabric Wetting Based on Particle Sampling. Abstract: This article proposes a novel simulation method for fabric wetting. Firstly, in or-der to achieve the wetting effect of the interaction between liquid and fabric, a fast sampling method for fabric boundaries is proposed, which analyzes and samples the edges and interior of the fabric triangle during the preprocessing process. Then the forces of the sampled particles and fluid particles were calculated, and the forces of the sampled particles were applied to each vertex of the fabric trian-gle. Finally, update the positions and velocities of fluid particles and cloth parti-cles. In order to ...

Ron Sun. Relevance of Cognitive Architectures to Neural-Symbolic Models and Dual-Process Theories. Abstract: In this presentation, I address neural-symbolic (or “neurosymbolic”) models, dual-process theories, and cognitive architectures —their relevance to each other. I provide some historical backgrounds and argue that dual-process theories have significant implications for developing neural-symbolic models. Computational cognitive architectures can help disentangle complex issues concerning dual-process theories and thus neural-symbolic models. The notion of neural-symbolic models harkens back to the 1990s when such models first emerged (see, e.g., Sun & Bookman,1994). There have been ...

John Laird. Cognitive Architectures, Transformers, and LLM: Have I Got Your Attention? Abstract: TBA.

Shuya Yang. The role of social stress in the development of mental disorders. Abstract: Social stress is a major risk factor for the development of depression. In a clinical study, exposure to daily social stress has been linked to the occurrence of major depressive disorder. To investigate the underlying mechanisms, animal depressive models have been proposed. In this study, we comprehensively summarized the different methodologies used to induce social-related stress in animals, including the most commonly used resident-intruder model, housing condition, social isolation, early life social event as well as social hierarchy model. Meanwhile, we investigated the behavioral ...

Felix-Francisco Ramos-Corchado, David Kelley, Kyrtin Atreides, Jan Treur, Alexei V. Samsonovich and Tingting Liu. BICA Society Panel. Abstract: BICA Society Panel is organized every year in the middle of the BICAAI conference. It is at the same time a business meeting of BICA Society (therefore, requires participation of BICA Society Directors) and a yearly report of the BICA Society BOD to the Membership (all registered participants automatically become members of BICA Society). Once in every 3 years elections of BOD are held at the BICA Society Panel, last elections were held in 2022. Otherwise, the panel discusses past progress and future plans of BICA Society, in particular, plans for the next BICAAI conference. Usually the ...

Ursula Addison. Human-Inspired Goal Reasoning Implementations: A Survey. Abstract: Goal reasoning is the ability of an artificial system to reason over its goals; it can identify, manage, plan, and execute its goals [1]. Goal Reasoning (GR) agents may rely on motivation system elements to guide the goal reasoning process. In this survey, we review those GR systems whose metareasoning and other GR subprocesses are at least in part intrinsic or identified, i.e. arising from idiosyncratic factors such as identity, a value system, emotions, experiences and so forth. For each system surveyed we evaluate its GR processes according to the Goal Reasoning and Analysis Framework ...

Chenyang Yan. A Novel Feature Selection Method Based on Slime Mold Network Formation Behavior. Abstract: Slime mold (Physarum polycephalum) is a remarkable organism that can solve complex problems such as mazes, shortest path networks, and optimal transport networks. Inspired by the adaptive network formation behavior of slime mold, we propose a novel feature selection algorithm (SMFS). The SMFS converts the feature selection into an optimal subgraph problem and employs a slime mold network formation inspired strategy to guide the sub-graph search procedure. We evaluate the performance of SMFS against 3 well-known meta-heuristic feature selection methods using different classifiers ...

Vasile Coman. Information, Energy, Maxwell's Laws, and Evolutionary Universe. Abstract: 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 a new concept called information density that describes how we can measure the complexity contained in any information message associated logic. Information density made use of a new concept called Viable Complex System. This paper takes these two new concepts to the next level. This paper is split in four sections. The first section starts with Claude Shannon's 1948 information theory and it adds the ...

Eduardo Yuji Sakabe, Anderson Anjos da Silva, Luiz Fernando Coletta, Alexandre da Silva Simões, Esther Luna Colombini, Paula Dornhofer Paro Costa and Ricardo Ribeiro Gudwin. An Episode Tracker for Cognitive Architectures. Abstract: This paper introduces the Episode Tracker Module, an encoding mechanism that tracks sensory information through space and time, building up high-level semantic representations called episodes. This module is aimed to extend the Cognitive Systems Toolkit (CST) as a reusable framework for building different cognitive models for episode detection. We created two instances of the episode tracker with two different mechanisms for identifying property categories (geographical regions). Each mechanism correctly induced a different episode detection dynamic. Overall, the Episode Tracker architecture ...

Ruth Alejandra Bastidas Alva, Angie Luisa Herrera Poma, Valeryia Estthefania Perez Villa and Frank William Zarate Peña. The COPPER babysitter robot, a child care monitoring system from the first year of age. Abstract: This research work presents the design of a mobile robot that allows monitoring and achieving the care and integrity of children from the first year. The proposed design will allow us to monitor the behavior of the child with a surveillance system with the help of artificial intelligence, particularly artificial vision. This design presents a stable and friendly structure for the child, an intuitive interface for the user, family or caregiver of the child; in addition to an artificial neural network suitable for image processing, as well as the use of a microprocessor that has enough memory ...

Yuanyi Wang, Zhen Liu and Tingting Liu. A Probabilistic Formal description of BDI Agent's Emotions. Abstract: Emotions play an important role in establishing the cognition of rational agent. In this paper, a formalization of emotions grounded on the OCC model is presented. We are interested in the emotions whose appraisal evaluates the consequences for others. The formal modelling presented here is based on the multiagent-AfPL probabilistic logic, which allows us to compute the potential of an emotion when the triggering condition is satisfied. We use the value of potential to differentiate experienced emotions from affective reaction by checking if this value surpasses a minimum threshold. ...

Haoran Fu, Chundong Wang, Jiaqi Sun, Hao Lin, Junqing Sun and Baixue Zhang. WordIllusion: An Adversarial Text Generation Algorithm Based on Human Cognitive System. Abstract: Although natural language processing technology has shown strong performance in many tasks, it is very vulnerable to adversarial examples, i.e., sentences with some small perturbations can fool AI models. Current adversarial texts for English are usually generated by finding substitute words in adjacent spaces of keyword vectors. Unlike English, Chinese is more discrete and has a more complex font structure, which words that are closer in vector spaces may differ greatly in physical structure. Therefore, adversarial examples generated by current methods possess lower quality and can be ...

Wanqing Huang, Yang Chen, Yongqi Chen, Tao Zhang, Feiyu Yu and Xiaoyan Mao. A bearing fault diagnosis method based on VMD-HPE. Abstract: Since rolling bearings operate in complex and harsh conditions with high speed and heavy load for a long time, their fault signals have the problems of difficulty in feature extraction and low diagnostic accuracy. Therefore, a rolling bearing fault diagnosis method based on variational mode decomposition(VMD) and hierarchical permutation entropy(HPE) is proposed in this paper. Firstly, the fault signals of rolling bearings are decomposed by variational mode decomposition. Secondly, several node signals are obtained after hierarchical decomposition, and the permutation entropy value of the ...

Evgenii Dzhivelikian, Petr Kuderov and Alexandr I. Panov. Learning Hidden Markov Model of Stochastic Environment with Bio-Inspired Probabilistic Temporal Memory. Abstract: Learning models online in partially observable stochastic environments can still be challenging for artificial intelligent agents. In this paper, we propose an algorithm for the probabilistic modeling of observation sequences based on the neurophysiological model of the human cortex, which is notoriously fit for this task. We argue that each dendritic segment of a pyramidal neuron may be considered an independent naive Bayesian detector of afferent neuron activity patterns. Experiments show that our model can learn the dynamics of the partially observable environments for very few ...

Xuyao Dai, Zhen Liu, Tingting Liu, Guokun Zuo, Jialin Xu, Changcheng Shi and Yuanyi Wang. Modeling of Conversational Agent with Empathy Mechanism. Abstract: Cognitive function is increasingly understood as a coordinated pattern of activity across multiple brain regions, which is similarly complex for conversational agent(CA) with multimodal human-computer interaction capabilities. Previous research on CA has been limited by a lack of communication methods. In this interdisciplinary study, we aimed to address the issue of a lack of empathy mechanism emotional interaction in previous research on CA and achieve effective interaction between humans and CA. To this end, we developed a comprehensive framework for multimodal human-computer emotional ...

Emanuel Diamant. BICA’s fears and troubles: GPT-based AI tools are its friends or foes? Abstract: ICA*AI is a well-established long-lasting R&D enterprise aimed at creating computational architectures intended to emulate Human-level Artificial Intelligence. Recently and quite unexpectedly in its field has appeared another contender – a GPT-based AI tool designed to mimic man-computer conversation in a user-friendly natural human language. As its designers claim, the device exhibits signs of General AI. After an exciting and joyful reception, it became clear that the new competitor does not fulfill its expected promises – it returns wrong and misleading responses, deceptions, and ...

Amit Kumar Mishra and Yi Zhong. DIPy-AI: DIKW Pyramid-based Agile AI Architecture for Sensor Data Assimilation. Abstract: The paper proposes DIPy-AI, an agile AI architecture based on the data-knowledge-information-wisdom (DIKW) pyramid, for processing sensor data in production environments. DIKW is one of the accepted models abstract- ing the assimilation of sensory data by the human brain. DIPy-AI aims to address challenges related to data assimilation, quality detection, and modular infor- mation extraction. The proposed architecture consists of three layers, viz a sen- sor-dependent data pre-processing layer, a sensor-agnostic ML layer for convert- ing data into information, and an application-specific ...

Kethellen Silva, Ana Clara Cardoso, Selma Oliveira and José Cláudio Damaso. Digitally-Enabled Labor Market: The Dark Side of Digital Transformation. Abstract: Although it is widely reported in the literature that digital transformation and emerging digital technologies can improve the performance of organizations, little is known about how digital transformation is affecting the labor market. This study shows how new technologies are affecting a digitally-enabled labor market in an emerging economy. Primary data were collected from experts from Brazili-an multinational companies. The results of this study indicate that the labor mar-ket has been little affected by digital technologies. Our findings suggest substan-tive capabilities of experts in ...

Jiaqi Fu, Tiejun Pan, Leina Zheng and Zichu Xue. The research on key technologies for intelligent education based on LLM. Abstract: This article investigates the influence of the artificial intelligence model on intelligent education and explores the prospects of integrating virtual human technology to enable the LLM model to perform instructional tasks. A digital teacher will be created via the integration of the LLM model with virtual human technology. The virtual human will perform live streaming on specific platforms, acting as a teacher for various subjects based on different prompts, and engaging with the audience in the livestreaming environment.

Tiejun Pan, Jinjie Yu, Leina Zheng and Yuejiao Li. Application and modeling of LLM in quantitative trading using deep learning strategies. Abstract: After more than 100 years of development, with the breakthrough of computer technology, deep learning and big data industry, the quantitative trading market has gradually matured, and more and more investors have begun to use quantitative trading to invest. Quantitative trading automatically executes transactions through written programs, eliminating the interference of human subjective factors on transaction execution. But the threshold for quantitative trading is high, requiring researchers to have a deep understanding of mathematics, statistics, finance, and computer technology. The newly ...

Nouf Abukhodair, Meehae Song, Serkan Pekçetin and Steve Dipaola. Designing a Wheel-based Assessment Tool to Measure Visual Aesthetic Emotions. Abstract: Currently, there is much debate surrounding affect and emotion conveyed in art-work as these elements are considered to be subjective higher-level semantics and difficult to measure objectively. This paper introduces the Visual Aesthetic Wheel of Emotion (VAWE), a domain-specific device for measuring visual aesthetic emotions which was structurally inspired by the Geneva Emotion Wheel (GEW) developed by Scherer et al. [31]. The development of the emotion terms used in this device was based on an extensive literature review on emotions induced by visual art and music, as well as various ...

John Laird. MRIntegrating Cognitive Architectures and Generative Models. Abstract: In this talk, I explore three possible variants of how generative models can integrate with cognitive architectures, potentially overcoming their corresponding weaknesses. In the first variant, pre-trained generative models are one or more modules in a traditional cognitive architecture, acting as a fixed, read-only long-term memory or as a perceptual or motor module. Here, the cognitive architecture retains its native knowledge representations (such as symbolic graph structures and rules) and must translate them into natural language to access the generative models. The second variant is ...

Sourav Yadav, Sankalp Arora, Akash Kumar and Kaveri Verma. Unraveling the Elements of Effective Altruistic Appeals through Machine Learning and Natural Language Processing. Abstract: In today’s world, online platforms such as social media, philanthropic communities, and Q&A websites provide opportunities for people to be altruistic by donating money or answering questions without expecting anything in return. The r/Random Acts Of Pizza subreddit on Reddit is one such online community where users can post requests for free pizza while explaining their current situation, and the outcome of each request is either successful or unsuccessful. This study seeks to explore the determinants that impact the outcome of such selfless appeals. To achieve this, we propose a ...

Damiem Rolon-Merette, Thaddé Rolon-Merette and Sylvain Chartier. Are Associations All You Need to Solve the Dimension Change Card Sort and N-bit Parity Task? Abstract: When problem-solving, humans can cycle between learned rules to solve tasks. Yet, in artificial neural networks, this cognitive strategy is replaced by learning the entire solution space, making it far less effective. This work aimed to emulate the basis of this human strategy by using a recurrent neural associative memory model. To achieve this, two networks interacted; one served as a task Identifier and the other as a memory Extractor, giving the desired behavior influenced by the Identifier. Each network was trained on sets of interacting associations to represent behavior, such as ...

Xiao Chen, Zhen Liu, Jiangjian Xiao, Tingting Liu and Yumeng Zhao. DDG: Dependency-Difference Gait based on Emotional Information Attention for Perceiving Emotions from Gait. Abstract: Perceving human emotions constitutes a crucial element of affective computing. As a nonverbal biological feature, gait plays an important role in affective computing as it is not easily manipulated or imitated. In this paper, we propose a gait-based emotion perception framework called Dependency-Difference Gait (DDG). This framework allows for comprehensive and efficient extraction of emotional features embedded in gait patterns. Specifically, we propose a method of spatiotemporal difference representation, which constructs static spatial difference information within frames, and dynamic ...

Adil Chakhtouna, Sara Sekkate and Abdellah Adib. A statistical WavLM embedding features with auto-encoder for Speech Emotion Recognition. Abstract: Background: Speech Emotion Recognition (SER) is an emerging field that encompasses various disciplines such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), computer vision, and cognitive sciences like psychology and social sciences. Method: The primary objective of this SER study is to analyze and quantify human emotions using a combination of statistical feature extraction and Deep Learning (DL) techniques. To achieve this goal, the Mi-Auto-Encoder (MiAE) is proposed to compress the embedding features representation of the WavLM model; in addition, a dense layer is ...

Vasile Coman. Information, Energy, Maxwell's Laws, and Evolutionary Universe. Abstract: This paper is a continuation of the paper called "Back to the Basics — Redefining Information, Knowledge, Intelligence, and Artificial Intelligence Using Only the Adaptive Systems Theory" presented at the 2009 AAAI Fall Symposium. This paper introduces the relationship that exists between information and energy. By using a dipole pattern for the origin for energy to build a holarchy model of the Universe, this paper demonstrates that causal [semantic] information attribute can be identified as the fifth dimension. All entities that are sharing the same dipole type of interactions form a ...

Habtom Kahsay Gidey, Peter Hillmann, Andreas Karcher and Alois Knoll. Modeling Design Constraints in Cognitive Architectures. Abstract: Design constraints are fundamental pillars in establishing reference models, design patterns, and architectures in software systems. Their importance extends further as they are critical in ensuring safety, predictable behavior, and trust in artificially intelligent cognitive software systems. Within these systems, constraints are integrated as components of cognitive architectures. This study looks into the methodologies utilized in conceptualizing and modeling design constraints in cognitive architectures. Furthermore, it investigates their application in behavioral verification and ...

Thomas Pederson and Amit Kumar Mishra. Symbiotic Artificial and Human Cognitive Architectures Managing Human Attention. Abstract: Wearable digital technologies such as Augmented Reality glasses offer a unique platform not only for monitoring proxies of individual human behaviour data (e.g. eye and body limb movements, posture, location, skin conductivity) but also for affecting behaviour, as instances of persuasive technologies often used to achieve personal human goals, e.g. for integrating physical exercise into everyday life. For artificial computational systems to gracefully affect in-situ human behavior is however associated with several challenges. It requires carefully interfacing digital processes running on ...

Srikari Rallabandi and Obulesh Avuku. Ethical Use of AI in Social Media. Abstract: As AI algorithms become increasingly integrated into social media platforms, concerns regarding their ethical implications have grown. This research paper examines the ethical use of AI in social media, focusing on three key areas: content moderation, user manipulation, and the spread of misinformation. We analyze the ethical concerns that arise due to personal, political, social, and ethical biases embedded within AI algorithms. The paper highlights the challenges of striking a balance between human moderation and algorithmic enforcement, as well as the need to address biases in the ...

Yelyzaveta Mukeriia, Jan Treur and Sophie Hendrikse. A Multi-Adaptive Network Model for Human Hebbian Learning, Synchronization and Social Bonding Based on Adaptive Homophily. Abstract: This paper presents a multi-adaptive network model integrating multiple adaptation mechanisms, specifically focusing on five types of adaptation mechanisms. Two of them address first-order adaptation by learning of responding on others and first-order adaptation by bonding with others based on homophily. Three other adaptation mechanisms addressed are second-order adaptation of the speed of both Hebbian learning and bonding by homophily, and second-order adaptation of the homophily tipping point. The paper provides a comprehensive explanation of these concepts and their role in controlled ...

Nisrine Mokadem, Jan Treur, Fakhra Jabeen, H. Rob Taal and Peter Roelofsma. An Adaptive Network Model for AI-Assisted Monitoring and Management of Neonatal Respiratory Distress. Abstract: This article presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of introducing a virtual AI-Coach in a hospital setting. The practical application setting revolves around a medical team responsible for managing neonates with respiratory distress. In this setting ...

Anton Kolonin. Model of personal consciousness based on the principles of social proof and free energy. Abstract: We propose a model of personal consciousness based on the principles of social proof and free energy, based on fundamental research on the principle of free energy, which states the minimization of uncertainty as the goal of the evolution of matter and mind (Friston, 2006), as well as the principle of social proof, supported by phenomenological studies in the field of social science (Cialdini, 2001). At the same time, it is assumed aligned with the notion of general intelligence as the ability to adapt to dynamic environments given insufficient knowledge and resources (Wang, 2019) within the ...

Natasha Devine. Culture, Machine Intelligence and Human Evolution. Abstract: The pinnacle of human accomplishment and the steering wheel of the vehicle that is humanity is our diverse human cultural development. Culture controls us as much as we control it. What is humanity without culture? Further, what is culture without humanity? Without technological development, culture would stagnate. Yet with only technological development, in lieu of cultural development, humanity may also stagnate. How can we retain our symbiotic evolutionary relationship with culture as emerging technologies hold large potential to influence culture? My talk will focus on ...

Damien Vervoordeldonk, Jan Treur, Sophie Hendrikse and Peter Roelofsma. A Higher-Order Adaptive Network Model on the Interplay of Synchrony and Adaptation for Reliving Memory and Religious Experience through Multimodal Interaction. Abstract: Religion can be a controversial topic to discuss. Depending on the person asked, religion can seem to have many wonderful or awful aspects and impacts. Regardless the view, emotion often takes the upper hand over objectivity. This thesis aims to contribute to a more objective look on the positive impact religion can have. This is done by computationally recreating, simulating and subsequently inspecting a real-life scenario in which positive impacts were experienced. In order to recreate such scenario computationally, a network-oriented modeling approach was used to create an adaptive agent ...

Junichi Takeno. Self-Aware Robots and Conscience. Abstract: I recently found that a self-aware system could be utilized as a very important element for explaining human conscience. My research group and I have presented our MoNAD structure which is configured as a double-layered recursive neural network. The self-aware system is built using many of these MoNADs. The most important feature of a MoNAD is its self-reflective function. This functionality enables the self-aware system to discriminate between its own self and some other entity, and such a capability could make it possible to generate a representation of the self on the system. The presentation that my research group and I gave at the prior BICA conference described how the self-aware system could represent pleasant and unpleasant states. I plan to speak about conscience in the self-aware system at this conference.

Karley Dionne, Maya Vermeer and Jan Treur. Gossipping Until You Get Tired of It: A Network Model of the Adaptive Exchange of Rumors in a Small Scale Social Environment. Abstract: The spread of rumors, otherwise known as gossiping, is an inevitable part of life for most people. Therefore, it is important to understand the way that information is actually spread through social environments of smaller proportions. This spread is modeled using a Higher-Order Adaptation Social Network, utilizing states for “people” and their boredom. Representation states for their connection weights and their speed factors are also used to represent the adaptivity of the model. Different scenarios are modeled as real-life examples to better illustrate how a rumor is spread when ...

Florian David, George Kalibala, Blandine Pichon and Jan Treur. A Network Model for Modulating Sensory Processing Sensitivity in Autism Spectrum Disorder: Epigenetics, Adaptivity, and Other Factors. Abstract: This paper presents a computational agent model that simulates the regulation of sensory processing and behavioural responses to stimuli in autism spectrum disorder (ASD). The model incorporates feedback loops and accounts for the heterogeneity, variability, and adaptivity of these behavioural responses. We specifically investigate how epigenetic mechanisms, or the modulation of gene expression by environmental factors, can influence sensory processing sensitivity, or the responsiveness to subtle sensory signals, in ASD. We evaluate our model with simulation experiments.

Scott Fahlman. Deep Learning AI vs. Symbolic Knowledge-Based AI: We're Going to Need BOTH. Abstract: I will argue that, if we want to build AI systems that achieve human-like generality, flexibility, reliability, and resilience in dealing with messy, real-world problems, we need BOTH symbolic knowledge-based AI (KBAI) and statistical ML/DL components, with each side doing the jobs that it does best. In very broad terms, this means that ML/DL components will handle most of the lower-level sensory-motor tasks and unconscious or “reflex” actions. The KBAI components will take care of higher-level “conscious” reasoning and understanding, complex planning, and communication via ...

Saty Raghavachary. The Embodied Intelligent Elephant in the Room. Abstract: The central point made in this paper is this: human-level grounded meaning in an agent can only result from directly experiencing the world, which in turn can only be possible via embodiment (coupled with 'embrainment' - a suitable brain architecture). Via embodiment, we humans are able to represent our direct interactions the world, in addition to being able to associate symbols with them - this allows us to communicate via symbols, thereby externalizing our representations for mutual, collective benefit. Without embodiment, In contrast, AI agents are able to only operate at a derivative, ...

Kazuteru Miyazaki and Hitomi Miyazaki. Suppression of Negative Tweets using Reinforcement Learning Systems in a Multi-Agent Environment. Abstract: In recent years, damage caused by negative tweets has become a social problem. In this paper, we consider a method of suppressing negative tweets by using reinforcement learning. In particular, we consider the case where tweet writing is modeled as a multi-agent environment. Numerical experiments verify the effects of suppression using various reinforcement learning methods. If machines can appropriately intervene and interact with posts made by humans, we can expect that negative tweets and even blow-ups can be suppressed automatically without the need for costly human eye monitoring.

Alexei V. Samsonovich. Toward a human-level artificial social-emotional intelligence. Abstract: This talk will present one specific approach to the development of Artificial Social-Emotional Intelligence (ASEI). The key word here is "social". While affective modeling mostly concerns with emotional states of an agent, here the focus of attention is on social relationships of agents and their feelings to each other, manifested in behavior. The ultimate goal is to create a human-level ASEI. Although a system of deep neural networks can become its embodiment, the path to the goal lies through cognitive modeling. The eBICA cognitive architecture is the basis of the approach, grounded in ...

Ivan Axel Dounce and Felix Ramos. Biological inspired architecture for the identification of ambiguous objects using scene associations. Abstract: As humans, we have an excellent performance when perceiving the environment. In the artificial world, it is important for machines to perceive their environment so they can make correct decisions and act accordingly. An essential process to accomplish perception is to identify objects in a scene, but, as in reality, these objects can appear as ambiguous, and additionally, those objects are embedded into a particular scene. For this proposal we designed an architecture for the identification of ambiguous objects, using the information from the scene to guide the identification process. This ...

Olivier Georgeon, David Lurie and Paul Robertson. The Enactive Inference Problem Formalized with Dynamic Bayesian Networks. Abstract: The enactive inference problem is the problem for an autonomous enactive artificial agent to infer data structures useful to select future adapted behavior. An enactive artificial agent is an agent that experiences its environment through motorsensory loops rather than passively receiving data that represent the environment's state. Motorsensory loops are loops of actuator commands and non-representational sensory signal. The measure of behavioral adaptation does not need to be computed by the agent; it can be judged by an observer upon criteria such as self-motivation, playfulness, ...

Vladimir Red'Ko. Approaches to Modeling Autonomous Agents with Scientific Abilities. Abstract: An analysis of the methods of scientific knowledge that can be used in modeling autonomous agents-scientists is carried out. Different methods of cognition are considered, in which two systems analyzed by Daniel Kahne-man are used: System 1 (intuitive, subconscious) and System 2 (logical, ab-stract). The features of cognition associated with insight are briefly character-ized. The importance of searching for general principles covering a wide area of knowledge is emphasized.

Antonio Lieto. Avoiding the behaviouristic trap with the Minimal Cognitive Grid. Abstract: The enormous success of modern AI systems (e.g. in computer vision, natural language processing etc.) has led to the formulation of the hypothesis that such systems - since are able to obtain human or superhuman level performances in a number of tasks - actually have acquired the underlying competence that we humans possess in order to exhibit the same kind of behavior. This hypothesis, I argue, is however based exclusively on a behavioristic analysis of (some of) the output produced by them. And, as such, it is methodologically problematic. In this talk I will show how by using a tool ...

Paul Robertson. Artificial Social Intelligence, Artificial Independent Intelligence and the Future of AI. Abstract: The future of AI is in interacting fluidly with humans. Huge advances, especially over the last 12 years have put in place the building blocks for AI that can understand the world the way that we do, see the world the way we do, and can communicate about our world the way we do. Using examples from our own work as well as that of other labs, I will describe what these advances are, why they are important, and why more remains to be done. Arguably, none of the systems that exist today are intelligent in any reasonable sense. I will discuss what advances are required to achieve ...

 




*Submissions were received from many countries around the world, including Australia, Brazil, Canada, China, France, Germany, India, Israel, Italy, Japan, Mexico, Morocco, the Netherlands, Peru, Poland, Serbia, South Africa, Sweden, Taiwan, Turkey, Ukraine, United Kingdom, and the United States.

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