Biologically Inspired Cognitive Architectures最新文献_第9页

Simulation Analysis Based on Behavioral Experiment of Cooperative Pattern Task 基于合作模式任务行为实验的仿真分析

Biologically Inspired Cognitive Architectures Pub Date : 2019-04-08 DOI: 10.1007/978-3-030-25719-4_74

Norifumi Watanabe, Kota Itoda

引用次数: 0

Metacognition for a Common Model of Cognition 一种常见认知模型的元认知

Biologically Inspired Cognitive Architectures Pub Date : 2018-10-18 DOI: 10.1016/J.PROCS.2018.11.046

J. Kralik, J. Lee, P. Rosenbloom, Philip C. Jackson, Susan L. Epstein, Oscar J. Romero, R. Sanz, O. Larue, H. Schmidtke, Sang Wan Lee, Keith McGreggor

引用次数: 27

Bio-plausible simulation of three monoamine systems to replicate emotional phenomena in a machine 仿生模拟三种单胺系统，在机器中复制情感现象

Biologically Inspired Cognitive Architectures Pub Date : 2018-10-01 DOI: 10.1016/j.bica.2018.10.007

Alexey Leukhin , Max Talanov , Jordi Vallverdú , Fail Gafarov

引用次数: 0

An adaptive Network-Oriented cognitive model for Major Depression and its treatment 重度抑郁症的自适应网络导向认知模型及其治疗

Biologically Inspired Cognitive Architectures Pub Date : 2018-10-01 DOI: 10.1016/j.bica.2018.10.001

Marcia A. van der Poel, Jan Treur

引用次数: 0

Schema formalism for the common model of cognition 图式形式主义为认知的共同模式

Biologically Inspired Cognitive Architectures Pub Date : 2018-10-01 DOI: 10.1016/j.bica.2018.10.008

Alexei V. Samsonovich

{"title":"Schema formalism for the common model of cognition","authors":"Alexei V. Samsonovich","doi":"10.1016/j.bica.2018.10.008","DOIUrl":"10.1016/j.bica.2018.10.008","url":null,"abstract":"<div><p><span><span>Common Model of Cognition (CMC) is a collective attempt to develop a consensus on cognitive architectures. The model needs to be extended to include components and functions that are vital to achieving the goals of Humanlike AI, supporting humanlike learnability, </span>social acceptability and humanlike creativity. Being biologically grounded, together these components will enable social-emotional character reasoning in artifacts and support emotionally-driven </span>behavior<span> generation. Historically, cognitive architectures originated from rule-based systems. Their main building block then evolved to a variety of structures, collectively called here schemas. While a schema is an overloaded term, in the field of biologically inspired cognitive architectures (BICA) it can be given a precise and useful meaning, allowing comparison of different models. Here one particular model is used as the main example: emotional BICA, or eBICA (Samsonovich, BICA, 2013) that extends GMU BICA (Samsonovich & De Jong, 2005) and supports human-like socially-emotional intelligence. This becomes possible with the help of so-called moral schemas. Their operation relies on semantic maps and contributes to the functioning of narrative networks. The present work documents the general formalism of schemas of eBICA, defines moral schemas, and explains their usage on examples. This framework is expected to enable a human-level believability and social compatibility in virtual actors and cobots across a variety of practically important domains and paradigms, thereby contributing to the expected breakthrough in humane artificial intelligence. Expected applications include virtual cobots-assistants and actors-partners in a broad spectrum of tasks. Forming a consensus on goals, paradigms, metrics and target applications for the new framework is equally important in understanding the overarching mission of solving the BICA Challenge.</span></p></div>","PeriodicalId":48756,"journal":{"name":"Biologically Inspired Cognitive Architectures","volume":"26 ","pages":"Pages 1-19"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bica.2018.10.008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48202805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

The SOSIEL Platform: Knowledge-based, cognitive, and multi-agent SOSIEL平台:基于知识、认知和多智能体

Biologically Inspired Cognitive Architectures Pub Date : 2018-10-01 DOI: 10.1016/j.bica.2018.09.001

Garry Sotnik

{"title":"The SOSIEL Platform: Knowledge-based, cognitive, and multi-agent","authors":"Garry Sotnik","doi":"10.1016/j.bica.2018.09.001","DOIUrl":"10.1016/j.bica.2018.09.001","url":null,"abstract":"<div><p>This article describes the open-source cognitive multi-agent knowledge-based SOSIEL (Self-Organizing Social & Inductive Evolutionary Learning) Platform, designed for building the social components of social-ecological decision support systems, consisting of agents empowered with a cognitive architecture. The platform can simulate the cross-generational progression of one or a large number of agents that can interact among themselves and/or with coupled natural and/or technical systems, learn from their and each other’s experience, create new practices, and make decisions about taking and then take (potentially collective) actions. The platform can also be used for conducting hypothetical experiments that are focused on studying the interactions among: (a) cross-generational population dynamics, (b) self-organizing multi-layered social network structures, (c) evolving place-based knowledge, (d) learning, (e) decision-making, (f) collective action and its potential, and (g) social and (when coupled) social-ecological outcomes. The article describes a simple model that was built with the SOSIEL Platform, which simulates the co-evolution of mental models among socially learning agents.</p></div>","PeriodicalId":48756,"journal":{"name":"Biologically Inspired Cognitive Architectures","volume":"26 ","pages":"Pages 103-117"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bica.2018.09.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47901471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

A biological brain-inspired fuzzy neural network: Fuzzy emotional neural network 一个生物脑启发的模糊神经网络:模糊情感神经网络

Biologically Inspired Cognitive Architectures Pub Date : 2018-10-01 DOI: 10.1016/j.bica.2018.07.019

Ehsan Zamirpour, Mohammad Mosleh

引用次数: 13

Model of interaction between learning and evolution. Computer simulation and analytical results 学习和进化相互作用的模型。计算机模拟及分析结果

Biologically Inspired Cognitive Architectures Pub Date : 2018-10-01 DOI: 10.1016/j.bica.2018.09.002

David B. Saakian , Vladimir G. Red'ko

{"title":"Model of interaction between learning and evolution. Computer simulation and analytical results","authors":"David B. Saakian , Vladimir G. Red'ko","doi":"10.1016/j.bica.2018.09.002","DOIUrl":"10.1016/j.bica.2018.09.002","url":null,"abstract":"<div><p>The current work develops the previous model of interaction between learning and evolution (Red’ko, 2017). The previous model investigated this interaction by means of computer simulation. The mechanisms of the main properties of the interaction between learning and evolution (the genetic assimilation<span>, the hiding effect, the influence of the learning load on the interaction between learning and evolution) were analyzed. The results were obtained for the finite size of the population. Fortunately, there is the possibility to analyze the same effect analytically for the case of the infinite size of the population. The current article considers sufficiently large sizes of population. Computer simulation demonstrates that the essential results of the model do not depend on the population size if this size is sufficiently large. Moreover, at such large population size, the results of computer simulation actually coincide with the results of analytical estimations. We consider the processes of learning and evolution for the population of modeled organisms that have genotype and genotype. Genotypes are modified during evolution, phenotypes are optimized by means of learning. At the end of the generation, organisms are selected in accordance with their final phenotype. The main attention is paid to the hiding effect. This effect means that learning can suppress the evolutionary optimization of genotypes: the optimal phenotype can be found by means of learning for a rather large set of different genotypes, so there is no need to find the optimal genotype. The hiding effect is analyzed by both computer simulation and analytically.</span></p></div>","PeriodicalId":48756,"journal":{"name":"Biologically Inspired Cognitive Architectures","volume":"26 ","pages":"Pages 96-102"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bica.2018.09.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44461687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Episodic memory transfer for multi-task reinforcement learning 情景记忆在多任务强化学习中的迁移

Biologically Inspired Cognitive Architectures Pub Date : 2018-10-01 DOI: 10.1016/j.bica.2018.09.003

Artyom Y. Sorokin, Mikhail S. Burtsev

{"title":"Episodic memory transfer for multi-task reinforcement learning","authors":"Artyom Y. Sorokin, Mikhail S. Burtsev","doi":"10.1016/j.bica.2018.09.003","DOIUrl":"10.1016/j.bica.2018.09.003","url":null,"abstract":"<div><p><span><span>Episodic memory plays important role in animal </span>behavior. It allows to reuse general skills for solution of specific tasks in changing environment. This beneficial feature of biological cognitive systems is still not incorporated successfully in an artificial neural architectures. In this paper we propose a neural architecture with shared episodic memory for multi-task </span>reinforcement learning<span> (SEM-PAAC). This architecture extends Parallel Advantage Actor Critic (PAAC) with two recurrent<span> sub-networks for separate tracking of environment and task states. The first subnetwork store episodic memory and the second one allows task specific execution of policy. Experiments in the Taxi domain demonstrated that SEM-PAAC has the same performance as PAAC when subtasks are solved separately. On the other hand when subtasks are solved jointly for completing full Taxi task SEM-PAAC is significantly better due to reuse of episodic memory. Proposed architecture also successfully learned to predict task completion. This is a step towards more autonomous agents for multitask problems.</span></span></p></div>","PeriodicalId":48756,"journal":{"name":"Biologically Inspired Cognitive Architectures","volume":"26 ","pages":"Pages 91-95"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bica.2018.09.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48457212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Approaches to cognitive architecture of autonomous intelligent agent 自主智能体的认知体系结构研究

Biologically Inspired Cognitive Architectures Pub Date : 2018-10-01 DOI: 10.1016/j.bica.2018.10.004

Yuriy Dyachenko , Nayden Nenkov , Mariana Petrova , Inna Skarga-Bandurova , Oleg Soloviov

引用次数: 28