Journal of Artificial General Intelligence最新文献_第2页

Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents 通过超过滤器的智能:确定性Legg-Hutter代理的一些智能比较器的结构特性

Journal of Artificial General Intelligence Pub Date : 2019-01-01 DOI: 10.2478/jagi-2019-0003

S. Alexander

引用次数: 5

On Defining Artificial Intelligence 关于人工智能的定义

Journal of Artificial General Intelligence Pub Date : 2019-01-01 DOI: 10.2478/jagi-2019-0002

Pei Wang

引用次数: 150

Learning and decision-making in artificial animals 人工动物的学习和决策

Journal of Artificial General Intelligence Pub Date : 2018-07-01 DOI: 10.2478/jagi-2018-0002

Claes Strannegård, Nils Svangård, David Lindström, Joscha Bach, Bas R. Steunebrink

{"title":"Learning and decision-making in artificial animals","authors":"Claes Strannegård, Nils Svangård, David Lindström, Joscha Bach, Bas R. Steunebrink","doi":"10.2478/jagi-2018-0002","DOIUrl":"https://doi.org/10.2478/jagi-2018-0002","url":null,"abstract":"Abstract A computational model for artificial animals (animats) interacting with real or artificial ecosystems is presented. All animats use the same mechanisms for learning and decisionmaking. Each animat has its own set of needs and its own memory structure that undergoes continuous development and constitutes the basis for decision-making. The decision-making mechanism aims at keeping the needs of the animat as satisfied as possible for as long as possible. Reward and punishment are defined in terms of changes to the level of need satisfaction. The learning mechanisms are driven by prediction error relating to reward and punishment and are of two kinds: multi-objective local Q-learning and structural learning that alter the architecture of the memory structures by adding and removing nodes. The animat model has the following key properties: (1) autonomy: it operates in a fully automatic fashion, without any need for interaction with human engineers. In particular, it does not depend on human engineers to provide goals, tasks, or seed knowledge. Still, it can operate either with or without human interaction; (2) generality: it uses the same learning and decision-making mechanisms in all environments, e.g. desert environments and forest environments and for all animats, e.g. frog animats and bee animats; and (3) adequacy: it is able to learn basic forms of animal skills such as eating, drinking, locomotion, and navigation. Eight experiments are presented. The results obtained indicate that (i) dynamic memory structures are strictly more powerful than static; (ii) it is possible to use a fixed generic design to model basic cognitive processes of a wide range of animals and environments; and (iii) the animat framework enables a uniform and gradual approach to AGI, by successively taking on more challenging problems in the form of broader and more complex classes of environments","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116270048","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: 5

Computable Variants of AIXI which are More Powerful than AIXItl AIXI的可计算变体，比AIXItl更强大

Journal of Artificial General Intelligence Pub Date : 2018-05-22 DOI: 10.2478/jagi-2019-0001

Susumu Katayama

引用次数: 3

Towards General Evaluation of Intelligent Systems: Lessons Learned from Reproducing AIQ Test Results 迈向智能系统的一般评估:从重现AIQ测试结果中吸取的教训

Journal of Artificial General Intelligence Pub Date : 2018-03-07 DOI: 10.2478/jagi-2018-0001

Ondrej Vadinský

{"title":"Towards General Evaluation of Intelligent Systems: Lessons Learned from Reproducing AIQ Test Results","authors":"Ondrej Vadinský","doi":"10.2478/jagi-2018-0001","DOIUrl":"https://doi.org/10.2478/jagi-2018-0001","url":null,"abstract":"Abstract This paper attempts to replicate the results of evaluating several artificial agents using the Algorithmic Intelligence Quotient test originally reported by Legg and Veness. Three experiments were conducted: One using default settings, one in which the action space was varied and one in which the observation space was varied. While the performance of freq, Q0, Qλ, and HLQλ corresponded well with the original results, the resulting values differed, when using MC-AIXI. Varying the observation space seems to have no qualitative impact on the results as reported, while (contrary to the original results) varying the action space seems to have some impact. An analysis of the impact of modifying parameters of MC-AIXI on its performance in the default settings was carried out with the help of data mining techniques used to identifying highly performing configurations. Overall, the Algorithmic Intelligence Quotient test seems to be reliable, however as a general artificial intelligence evaluation method it has several limits. The test is dependent on the chosen reference machine and also sensitive to changes to its settings. It brings out some differences among agents, however, since they are limited in size, the test setting may not yet be sufficiently complex. A demanding parameter sweep is needed to thoroughly evaluate configurable agents that, together with the test format, further highlights computational requirements of an agent. These and other issues are discussed in the paper along with proposals suggesting how to alleviate them. An implementation of some of the proposals is also demonstrated.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121882212","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: 1

Homeostatic Agent for General Environment 一般环境稳态剂

Journal of Artificial General Intelligence Pub Date : 2018-03-07 DOI: 10.1515/jagi-2017-0001

N. Yoshida

引用次数: 12

Learning and Reasoning in Unknown Domains 未知领域的学习和推理

Journal of Artificial General Intelligence Pub Date : 2016-12-01 DOI: 10.1515/jagi-2016-0002

Claes Strannegård, Abdul Rahim Nizamani, Jonas Juel, U. Persson

引用次数: 5

The Sigma Cognitive Architecture and System: Towards Functionally Elegant Grand Unification 西格玛认知架构与系统:迈向功能优雅的大统一

Journal of Artificial General Intelligence Pub Date : 2016-12-01 DOI: 10.1515/JAGI-2016-0001

P. Rosenbloom, A. Demski, Volkan Ustun

{"title":"The Sigma Cognitive Architecture and System: Towards Functionally Elegant Grand Unification","authors":"P. Rosenbloom, A. Demski, Volkan Ustun","doi":"10.1515/JAGI-2016-0001","DOIUrl":"https://doi.org/10.1515/JAGI-2016-0001","url":null,"abstract":"Abstract Sigma (Σ) is a cognitive architecture and system whose development is driven by a combination of four desiderata: grand unification, generic cognition, functional elegance, and sufficient efficiency. Work towards these desiderata is guided by the graphical architecture hypothesis, that key to progress on them is combining what has been learned from over three decades’ worth of separate work on cognitive architectures and graphical models. In this article, these four desiderata are motivated and explained, and then combined with the graphical architecture hypothesis to yield a rationale for the development of Sigma. The current state of the cognitive architecture is then introduced in detail, along with the graphical architecture that sits below it and implements it. Progress in extending Sigma beyond these architectures and towards a full cognitive system is then detailed in terms of both a systematic set of higher level cognitive idioms that have been developed and several virtual humans that are built from combinations of these idioms. Sigma as a whole is then analyzed in terms of how well the progress to date satisfies the desiderata. This article thus provides the first full motivation, presentation and analysis of Sigma, along with a diversity of more specific results that have been generated during its development.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133159343","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: 72

Unnatural Selection: Seeing Human Intelligence in Artificial Creations 《非自然选择:从人工造物中看人类智能》

Journal of Artificial General Intelligence Pub Date : 2015-12-01 DOI: 10.1515/jagi-2015-0002

T. Veale

{"title":"Unnatural Selection: Seeing Human Intelligence in Artificial Creations","authors":"T. Veale","doi":"10.1515/jagi-2015-0002","DOIUrl":"https://doi.org/10.1515/jagi-2015-0002","url":null,"abstract":"Abstract As generative AI systems grow in sophistication, so too do our expectations of their outputs. For as automated systems acculturate themselves to ever larger sets of inspiring human examples, the more we expect them to produce human-quality outputs, and the greater our disappointment when they fall short. While our generative systems must embody some sense of what constitutes human creativity if their efforts are to be valued as creative by human judges, computers are not human, and need not go so far as to actively pretend to be human to be seen as creative. As discomfiting objects that reside at the boundary of two seemingly disjoint categories, creative machines arouse our sense of the uncanny, or what Freud memorably called the Unheimlich. Like a ventriloquist’s doll that finds its own voice, computers are free to blend the human and the non-human, to surprise us with their knowledge of our world and to discomfit with their detached, other-worldly perspectives on it. Nowhere is our embrace of the unnatural and the uncanny more evident than in the popularity of Twitterbots, automatic text generators on Twitter that are followed by humans precisely because they are non-human, and because their outputs so often seem meaningful yet unnatural. This paper evaluates a metaphor generator named @MetaphorMagnet, a Twitterbot that tempers the uncanny with aptness to yield results that are provocative but meaningful.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115693729","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: 9

Choosing the Right Path: Image Schema Theory as a Foundation for Concept Invention 选择正确的道路:意象图式理论作为概念发明的基础

Journal of Artificial General Intelligence Pub Date : 2015-12-01 DOI: 10.1515/jagi-2015-0003

Maria M. Hedblom, O. Kutz, F. Neuhaus

引用次数: 40