Computational constructivism: The algorithmic basis of discovery

计算建构主义:发现的算法基础

基本信息

  • 批准号:
    EP/T033967/1
  • 负责人:
  • 金额:
    $ 82.81万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2021
  • 资助国家:
    英国
  • 起止时间:
    2021 至 无数据
  • 项目状态:
    未结题

项目摘要

One of the defining aspects of being human is an ability to flexibly generate new ideas and hypotheses. For example, we readily come up with possible faults if our car breaks down, or plausible maladies when we feel unwell. We can brainstorm anything from party ideas, to corporate strategies, to magical creatures, and frequently hypothesise hidden motivations and beliefs in our peers to explain why they act the way they do. Our ideas often combine familiar objects, concepts, and relations, making them symbolic, easy to communicate, and a ready guide for follow up queries or evidence seeking. For example, suppose you came home from work to find your house in disarray. You might quickly suspect you have been burgled and investigate by checking whether valuables are missing. If you then discover feathers on the floor this might inspire other possibilities. Perhaps a bird got in through an open window and ran amok. These kinds of inventive inferences come quickly and easily for us, but are surprisingly difficult for artificial intelligence systems. Part of this difficulty is that, for the kinds of natural domains mentioned above, there is typically an infinite number of possibilities one could generate, but few good ones. Our best ideas have the character of "ah ha" moments, immediately providing a better explanation than preceding candidates and potentially becoming a lasting addition to one's beliefs or knowledge base. The key aim of this project is to develop algorithms that emulate the way humans generate, adapt and actively investigate such hypotheses in everyday life. The basic idea is that we combine our more primitive concepts to form more complex ideas, essentially "trying out" different combinations of primitives and connectives when searching for a better explanation, or adapting one that does not fit the latest evidence. Such a search process is governed by overarching principles of simplicity and fit to the evidence, but constrained by our finite thinking time and capacity. For example, in the above example you might rapidly generate, refine or overturn several hypotheses as you investigate the mess, discovering a feather duster, cleaning products, and finally your partner in the midst of a spring clean."Program induction" is a powerful new mathematical framework for constructing symbolic models or programs that can explain or reproduce observations. Induced programs can grow in structure and complexity as evidence is encountered, reusing past solutions as and composing them to solve new problems. We propose to use this as a framework to capture and ultimately synthesise humanlike hypothesis generation. To closely examine human hypothesis generation, we will combine theoretical work in the program induction framework with experiments with human adults. In our inductive learning tasks, participants and our algorithms will both observe and create their own physical scenes made up of simple geometric blocks and test them to discover and generate hypotheses regarding under what conditions they will produce a novel causal effect (i.e. in our pilot, produce a "newly discovered form of radiation"). This setup allows us to explore arbitrarily complex hidden causal effects that can involve combinations of features and relations, meaning the participants (and our algorithms) must use hypothesis generation, reasoning and active testing to identify the ground truth in each case.Through our modelling and our experiments we expect to deepen understanding of the mechanisms that underpin the uniquely human ability to make explanatory inferences. We expect our findings to influence robotics, and AI communities providing insight into how to build artificial systems that can better emulate, understand and be understood by humans. The goal of this project thus to develop a precise algorithmic account of idea generation in human learning that we call "computational constructivism".
作为人类的一个决定性方面是灵活地产生新想法和假设的能力。例如,如果我们的车坏了,我们很容易想出可能的故障,或者当我们感到不舒服时,我们很容易想出可能的疾病。我们可以集思广益,从聚会的想法,到公司的战略,再到神奇的生物,我们经常假设我们的同伴隐藏的动机和信仰,来解释为什么他们会这样做。我们的想法往往结合熟悉的对象、概念和关系,使它们具有象征意义,易于沟通,并为后续查询或寻找证据提供现成的指导。例如,假设你下班回家,发现家里一片混乱。你可能很快就会怀疑自己被盗了,并检查贵重物品是否丢失。如果你在地板上发现羽毛,这可能会激发其他的可能性。也许是一只鸟从开着的窗户飞进来乱飞。对我们来说,这种创造性的推断来得又快又容易,但对人工智能系统来说却异常困难。这种困难的部分原因在于,对于上面提到的各种自然领域,通常可以产生无限多的可能性,但很少有好的可能性。我们最好的想法具有“啊哈”时刻的特征,立即提供比之前的候选人更好的解释,并可能成为一个人的信念或知识基础的持久补充。该项目的主要目标是开发算法,模拟人类在日常生活中产生、适应和积极调查这些假设的方式。其基本思想是,我们将更原始的概念组合成更复杂的想法,本质上是在寻找更好的解释或调整不符合最新证据的解释时,“尝试”原始概念和连接词的不同组合。这样的搜索过程遵循简单和符合证据的首要原则,但受到我们有限的思考时间和能力的限制。例如,在上面的例子中,当你调查混乱时,你可能会迅速产生,改进或推翻几个假设,发现鸡毛掸子,清洁产品,最后是你的伴侣在春季大扫除中。“程序归纳法”是一个强大的新数学框架,用于构建符号模型或程序,以解释或再现观察结果。随着证据的出现,诱导程序可以在结构和复杂性上增长,重用过去的解决方案,并将它们组合起来解决新问题。我们建议将此作为一个框架来捕获并最终合成类人假设生成。为了仔细研究人类的假设生成,我们将把程序归纳框架中的理论工作与人类成年人的实验结合起来。在我们的归纳学习任务中,参与者和我们的算法都将观察并创建由简单几何块组成的自己的物理场景,并对它们进行测试,以发现并生成关于在什么条件下它们将产生新的因果效应的假设(即在我们的试点中,产生“新发现的辐射形式”)。这种设置允许我们探索任意复杂的隐藏因果效应,这些因果效应可能涉及特征和关系的组合,这意味着参与者(和我们的算法)必须使用假设生成、推理和主动测试来识别每种情况下的基本事实。通过我们的建模和实验,我们希望加深对支撑人类做出解释性推论的独特能力的机制的理解。我们希望我们的发现能够影响机器人技术和人工智能社区,为如何构建能够更好地模仿、理解和被人类理解的人工系统提供见解。因此,这个项目的目标是开发一种精确的算法来描述人类学习中产生的想法,我们称之为“计算建构主义”。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Active inductive inference in children and adults: A constructivist perspective.
儿童和成人的主动归纳推理:建构主义观点。
  • DOI:
    10.1016/j.cognition.2023.105471
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Bramley NR
  • 通讯作者:
    Bramley NR
Active inductive inference in children and adults: A constructivist perspective
儿童和成人的主动归纳推理:建构主义视角
  • DOI:
    10.31234/osf.io/tbm4n
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Bramley N
  • 通讯作者:
    Bramley N
Children's failure to control variables may reflect adaptive decision-making.
  • DOI:
    10.3758/s13423-022-02120-1
  • 发表时间:
    2022-12
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Bramley NR;Jones A;Gureckis TM;Ruggeri A
  • 通讯作者:
    Ruggeri A
Observing effects in various contexts won't give us general psychological theories
观察不同背景下的影响不会给我们提供一般的心理学理论
  • DOI:
    10.31234/osf.io/kpycz
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Donkin C
  • 通讯作者:
    Donkin C
Local search and the evolution of world models
本地搜索和世界模型的演变
  • DOI:
    10.31234/osf.io/e9p8k
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Bramley N
  • 通讯作者:
    Bramley N
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Neil Bramley其他文献

Lossy encoding of distributions in judgment under uncertainty
不确定性下判断中分布的有损编码
  • DOI:
    10.1016/j.cogpsych.2025.101745
  • 发表时间:
    2025-07-01
  • 期刊:
  • 影响因子:
    3.000
  • 作者:
    Tadeg Quillien;Neil Bramley;Christopher G. Lucas
  • 通讯作者:
    Christopher G. Lucas

Neil Bramley的其他文献

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