PhilSci Archive

Exploring Minds. Modes of Modelling and Simulation in Artificial Intelligence

Greif, Hajo (2019) Exploring Minds. Modes of Modelling and Simulation in Artificial Intelligence. [Preprint]

This is the latest version of this item.

[img]
Preview
Text
Greif_Exploring_Minds_2019-12-31.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (486kB) | Preview

Abstract

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. This taxonomy cuts across the traditional dichotomies between symbolic / embodied AI, general intelligence / cognitive simulation and human / non-human-like AI.
According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programmes: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI).
In continuation of pragmaticist views of the modes of modelling and simulating world affairs (Humphreys, Winsberg), this taxonomy of approaches to modelling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research – and what made that research programme uniquely dependent on them.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Preprint
Creators:
CreatorsEmailORCID
Greif, Hajomail@hajo-greif.net0000-0002-1003-7494
Additional Information: Manuscript accepted for publication in Perspectives on Science, Special Issue on Exploratory Models and Exploratory Modelling in Science, Guest Editors: Axel Gelfert, Grant Fisher, Friedrich Steinle
Keywords: Material models Formal models Theory-guided models Exploratory models Computer simulations Cognitive science Theory of computation
Subjects: Specific Sciences > Cognitive Science
Specific Sciences > Artificial Intelligence
General Issues > Computer Simulation
General Issues > Models and Idealization
Depositing User: Dr. Hajo Greif
Date Deposited: 31 Dec 2019 23:01
Last Modified: 31 Dec 2019 23:01
Item ID: 16773
Subjects: Specific Sciences > Cognitive Science
Specific Sciences > Artificial Intelligence
General Issues > Computer Simulation
General Issues > Models and Idealization
Date: 30 December 2019
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/16773

Available Versions of this Item

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item