Extending and integrating a hybrid knowledge representation

system into the cognitive architecture ACT-R

Valentina Rho Università degli Studi di Torino

supervisor: Daniele P. Radicioni co-supervisor: Antonio Lieto

15th International Conference of the Italian Association for Artificial Intelligence, 1 December 2016

Thesis objectives

(a) To extend a hybrid knowledge representation system based both on classical and typical information (S1S2)

(b)To integrate this system within the well-known cognitive architecture ACT-R

What is a concept and how to represent it?

• A concept is an abstract mix of information about a set of items that share common characteristics.

• Different theories try to find a way to represent concepts: for example classical, prototypes or exemplar-based theories.

The heterogeneity hypothesis

Machery (2009)

Concept of dog

refers to

bodies of knowledge

Dual-PECCS kb

*

Lieto, Radicioni, Rho (2015)

Dual process theory

• In our mind there are two types of reasoning processes:

• S1: fast, instinctive and emotional

• S2: slower, more deliberative, and more logical

Kahneman (2011)

Dual-PECCS reasoning

Type 1 Processes

Type 2 Processes

The proxytypes theoryPrinz (2002)

…

birds

…black

penguins

penguins

proxyfication

short-term memory

long-term memory

Heterogeneous proxytypesLieto (2014)

Short-term memory

Long-term memory

exemplars

MAMMAL concept

prototypes…

whale exemplar

proxyfication

similarity-based check

classical representation

Stimulus

concept v

concept y

concept x

concept z

Dual-PECCS algorithm“Thebigfishthateatsplankton”

Typical System - S1 (Conceptual

Spaces)

Classical System - S2 (OpenCyc)

Information Extractor

Internalrepresentation(dimension:big,family:fish,

feeding:plankton)

whale1.0whale-shark0.8

shark0.7…

whaleisnotafishwhale-sharkisok

ourfirstunconsciousanswerwouldbewhale

ourconsciousandreasonedanswerwouldbewhaleshark

Cognitive architectures

• The objective of a cognitive architecture is to define a comprehensive theory about the structure and the underlying mechanisms of the human mind.

• Some examples: ACT-R, Clarion, SOAR

ACT-R ArchitectureAnderson et al. (2004)

External Environment

Vision module Aural module

Motor module

Visual buffer Visual-location buffer

Manual buffer

Goal bufferRetrieval

buffer Imaginal buffer

Goal moduleDeclarative module

Imaginal module

Procedural module (match; select; fire) Aural buffer Aural-location

buffer

Speech module

Vocal buffer

Working memory(buffers)

Production list[…]

Chunks list[…]

Long-term memory

Integration in ACT-RWhat we’ve done? • Translated the Dual-PECCS typical KB into chunks,

considering bodies-of-knowledge chunks and conceptual chunks

• Extended the ACT-R DM with a dedicated action to allow access to the Dual-PECCS subsystems

• Implemented the main reasoning algorithm within the ACT-R production rules system.

Additional points of extension

• We extended the attentional markers of ACT-R to emulate the “change of mind” process when the S2 system doesn’t confirm the fast typical answer.

• We preliminarily studied how to extend the activation formulas of ACT-R (based on recency and frequency of retrieval) in order to follow the intuition that the activation of a concept should be function of the activation values of its representations.

Experiments• We used 90 textual riddles in two types of

experiments:

1. with manual information extraction

2. with automatic information extraction

i.e.“The big fish that eats plankton”

The results produced by the system have been compared to the responses provided in a psycological experiment by 10 human volunteers.

Results

• CC-Acc is the conceptual categorization accuracy: when the system returns the correct concept.

• P-Acc is the proxyfication accuracy: when Dual-PECCS not only returns the correct concept but also proxyfies the correct representation of it.

P-Acc analyses

• The system fails mostly when we are expecting a Prototype but and Exemplar is proxyfied. This means we need to improve the generalization process within the S1 system.

What’s next / now?

• Integration of Dual-PECCS in other cognitive architectures (SOAR, Clarion)

• Automatic population of the typical knowledge base

• Improving generalization within typical system (S1)

Thanks!

http://www.dualpeccs.di.unito.it/