Human brain has been used as an inspiration for building autonomous agents, but it is not obvious what level of computational description of the brain one should use. This has led to overly opinionated symbolic approaches and overly unstructured connectionist approaches. We propose that using homeostasis as the computational description provides a good compromise. Similar to how physiological homeostasis is the regulation of certain homeostatic variables, cognition can be interpreted as the regulation of certain 'cognitive homeostatic variables'. We present an outline of a Cognitive Homeostatic Agent, built as a hierarchy of physiological and cognitive homeostatic subsystems and describe structures and processes to guide future exploration. We expect this to be a fruitful line of investigation towards building sophisticated artificial agents that can act flexibly in complex environments, and produce behaviors indicating planning, thinking and feelings.
Continuous Time Continuous Space Homeostatic Reinforcement Learning (CTCS-HRRL) : Towards Biological Self-Autonomous Agent
Ecole Normale Superieure（巴黎高等师范学院）；BITS Pilani K. K. Birla Goa Campus（比拉理工学院K. K. Birla果阿校区）；Ecole Normale Supérieure Paris-Saclay（巴黎-萨克雷高等师范学院）；Université Paris-Saclay（巴黎-萨克雷大学）
Laboratoire de Neurosciences Cognitives, École normale supérieure（巴黎高等师范学院认知神经科学实验室）；Group for Neural Theory, Laboratoire des Neurosciences Cognitives et Computationelles (LNC2) ISENR U960, Ecole Normale Supérieure（巴黎高等师范学院认知与计算神经科学实验室神经理论组）；Ecole Normale Supérieure Paris-Saclay（巴黎萨克雷高等师范学院）；Université Paris-Saclay（巴黎萨克雷大学）
Homeostasis is a prevalent process by which living beings maintain their internal milieu around optimal levels. Multiple lines of evidence suggest that living beings learn to act to predicatively ensure homeostasis (allostasis). A classical theory for such regulation is drive reduction, where a function of the difference between the current and the optimal internal state. The recently introduced homeostatic regulated reinforcement learning theory (HRRL), by defining within the framework of reinforcement learning a reward function based on the internal state of the agent, makes the link between…
We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range. Our experimental validation shows the added value of the additional homeostatic drive to enhance the overall information gain of a reinforcement learning agent interacting with a complex environment using continuous actions. Our method builds upon two ideas: i) To take advantage of a new Bellman-like equation of information gain and ii) to simplify the computation of the local rewards by avoiding the approximation of comple…
The Conditions of Physical Embodiment Enable Generalization and Care
Institute for Advanced Consciousness Studies（先进意识研究所）；Monash Centre for Consciousness and Contemplative Studies（莫纳什意识与冥想研究中心）；University of Oxford（牛津大学）；Topos Institute（拓斯研究所）；Allen Discovery Center（艾伦发现中心）；Okinawa Institute of Science and Technology（冲绳科学技术研究所）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）；University of Hertfordshire（赫特福德郡大学）；Brain and Creativity Institute（大脑与创造力研究所）
One of the main problems of modern cognitive architectures is an excessively schematic approach to modeling the processes of cognitive activity. It does not allow the creation of a universal architecture that would be capable of reproducing mental functions without using a predetermined set of perceptual patterns. This paper considers an evolutionary approach to creating a cognitive functionality. The basis of our approach is the use of the functional kernel which consistently generates the intellectual functions of an autonomous agent. We consider a cognitive architecture which ensures the ev…
Reinforcement learning (RL) agents in human-computer interactions applications require repeated user interactions before they can perform well. To address this "cold start" problem, we propose a novel approach of using cognitive models to pre-train RL agents before they are applied to real users. After briefly reviewing relevant cognitive models, we present our general methodological approach, followed by two case studies from our previous and ongoing projects. We hope this position paper stimulates conversations between RL, HCI, and cognitive science researchers in order to explore the full p…
"Cognitive synergy" refers to a dynamic in which multiple cognitive processes, cooperating to control the same cognitive system, assist each other in overcoming bottlenecks encountered during their internal processing. Cognitive synergy has been posited as a key feature of real-world general intelligence, and has been used explicitly in the design of the OpenCog cognitive architecture. Here category theory and related concepts are used to give a formalization of the cognitive synergy concept. A series of formal models of intelligent agents is proposed, with increasing specificity and complexit…
INESC-ID（里斯本系统与计算机工程研究所）；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）
Current architectures for social agents are designed around some specific units of social behaviour that address particular challenges. Although their performance might be adequate for controlled environments, deploying these agents in the wild is difficult. Moreover, the increasing demand for autonomous agents capable of living alongside humans calls for the design of more robust social agents that can cope with diverse social situations. We believe that to design such agents, their sociality and cognition should be conceived as one. This includes creating mechanisms for constructing social r…
Computer Science, `Spiru Haret' University, Bucharest, Romania Faculty of Engineering, `Aurel Vlaicu' University, Arad, Romania
The relation between self awareness and intelligence is an open problem these days. Despite the fact that self awarness is usually related to Emotional Intelligence, this is not the case here. The problem described in this paper is how to model an agent which knows (Cognitive) Binary Logic and which is also able to pass (without any mistake) a certain family of Turing Tests designed to verify its knowledge and its discourse about the modal states of truth corresponding to well-formed formulae within the language of Propositional Binary Logic.
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. This problem motivated many research directions on embodied language use. Current approaches focus on language as a communication tool in very simplified and non diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in complex and variable social contexts; 2) beyond language, …
COMSATS Institute of Information Technology（COMSATS 信息技术学院）
A smart grid can be considered as a complex network where each node represents a generation unit or a consumer. Whereas links can be used to represent transmission lines. One way to study complex systems is by using the agent-based modeling (ABM) paradigm. An ABM is a way of representing a complex system of autonomous agents interacting with each other. Previously, a number of studies have been presented in the smart grid domain making use of the ABM paradigm. However, to the best of our knowledge, none of these studies have focused on the specification aspect of ABM. An ABM specification is i…
Escuela de Ingeniería, Universidad Central de Chile（智利中央大学工程学院）；School of Information Technology, Deakin University（迪肯大学信息技术学院）；Escola Politécnica de Pernambuco, Universidade de Pernambuco（伯南布哥大学伯南布哥理工学院）
Proxemics is a branch of non-verbal communication concerned with studying the spatial behavior of people and animals. This behavior is an essential part of the communication process due to delimit the acceptable distance to interact with another being. With increasing research on human-agent interaction, new alternatives are needed that allow optimal communication, avoiding agents feeling uncomfortable. Several works consider proxemic behavior with cognitive agents, where human-robot interaction techniques and machine learning are implemented. However, environments consider fixed personal spac…
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Within the Deep Reinforcement Learning (DRL) field, this objective motivated multiple works on embodied language use. However, current approaches focus on language as a communication tool in very simplified and non-diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in compl…
Although there are many approaches to implement intrinsically motivated artificial agents, the combined usage of multiple intrinsic drives remains still a relatively unexplored research area. Specifically, we hypothesize that a mechanism capable of quantifying and controlling the evolution of the information flow between the agent and the environment could be the fundamental component for implementing a higher degree of autonomy into artificial intelligent agents. This paper propose a unified strategy for implementing two semantically orthogonal intrinsic motivations: curiosity and empowerment…
Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN
Sun Yat-sen University, China（中山大学，中国）；Zhejiang University, China（浙江大学，中国）；University of Exeter, U.K.（埃克塞特大学，英国）；Singapore University of Technology and Design, Singapore（新加坡科技设计大学，新加坡）；Purple Mountain Laboratories, Nanjing, China（紫金山实验室，南京，中国）
National University of Defense Technology（国防科技大学）；Auckland University of Technology（奥克兰理工大学）；Shenzhen University（深圳大学）；University of British Columbia（不列颠哥伦比亚大学）
With the development of the 5G and Internet of Things, amounts of wireless devices need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this paper, we investigate the distributed DSA problem for multi-user in a typical multi-channel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we proposed a centralized off-line training…
This paper describes recent progress on natural language generation (NLG) for language-endowed intelligent agents (LEIAs) developed within the OntoAgent cognitive architecture. The approach draws heavily from past work on natural language understanding in this paradigm: it uses the same knowledge bases, theory of computational linguistics, agent architecture, and methodology of developing broad-coverage capabilities over time while still supporting near-term applications.
Università di Pisa（比萨大学）；National Research Council (CNR)（国家研究委员会（CNR））；Institute for Informatics and Telematics（信息学与电信研究所）
With humans interacting with AI-based systems at an increasing rate, it is necessary to ensure the artificial systems are acting in a manner which reflects understanding of the human. In the case of humans and artificial AI agents operating in the same environment, we note the significance of comprehension and response to the actions or capabilities of a human from an agent's perspective, as well as the possibility to delegate decisions either to humans or to agents, depending on who is deemed more suitable at a certain point in time. Such capabilities will ensure an improved responsiveness an…
Interoceptive Attention as Dynamic Homeostatic Prioritization in a Foraging Agent
Univ. of Cape Town（开普敦大学）；Neuroscience Institute, Univ. of Cape Town（开普敦大学神经科学研究所）；Laureate Institute for Brain Research（劳雷尔脑研究所）；University of the Witwatersrand（金山大学）；INRS（国家科学研究中心（加拿大））；Delft University of Technology（代尔夫特理工大学）
An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling
University of Groningen（格罗宁根大学）；Luxembourg Institute of Science and Technology（卢森堡科学技术研究院）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；CRAN（南锡自动化与计算机科学研究中心）
