Translation of Cognitive Models from ACT-R to Constraint Handling Rules

Gall, Daniel; Frühwirth, Thom

doi:10.1007/978-3-319-42019-6_15

Cited by 3 publications

(9 citation statements)

References 16 publications

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“…The formal definition of the operational semantics can be found in [9,8]. We define the operational semantics of ACT-R through our CHR translation that has been first presented in [9] and in its most current form in [8]. Since the translation is sound and complete, we omit the formal definition of the ACT-R semantics here, since it would only distract from the contribution of this paper.…”

Section: Syntax Of Act-rmentioning

confidence: 99%

“…In this paper, we present a decidable confluence test for the abstract operational semantics of ACT-R using confluence analysis tools for CHR. In prior work, we presented a sound and complete embedding of ACT-R in CHR [9,8]. An invariant-based confluence test for CHR [6,11] is used to decide confluence of the translated models with invariants on CHR states that come from the abstract operational semantics of ACT-R.…”

Section: Introductionmentioning

confidence: 99%

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A Decidable Confluence Test for Cognitive Models in ACT-R

Gall¹,

Frühwirth²

2017

Rules and Reasoning

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Abstract. Computational cognitive modeling investigates human cognition by building detailed computational models for cognitive processes. Adaptive Control of Thought -Rational (ACT-R) is a rule-based cognitive architecture that offers a widely employed framework to build such models. There is a sound and complete embedding of ACT-R in Constraint Handling Rules (CHR). Therefore analysis techniques from CHR can be used to reason about computational properties of ACT-R models. For example, confluence is the property that a program yields the same result for the same input regardless of the rules that are applied. In ACT-R models, there are often cognitive processes that should always yield the same result while others e.g. implement strategies to solve a problem that could yield different results. In this paper, a decidable confluence criterion for ACT-R is presented. It allows to identify ACT-R rules that are not confluent. Thereby, the modeler can check if his model has the desired behavior. The sound and complete translation of ACT-R to CHR from prior work is used to come up with a suitable invariant-based confluence criterion from the CHR literature. Proper invariants for translated ACT-R models are identified and proven to be decidable. The presented method coincides with confluence of the original ACT-R models.

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Section: Syntax Of Act-rmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Decidable Confluence Test for Cognitive Models in ACT-R

Gall¹,

Frühwirth²

2017

Rules and Reasoning

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“…The paper is a revised and extended version of our prior work in [16] and [17]. It has the following contributions: The formulations of the semantics (1 and 2) have been improved compared to [16] and the translation (3) has been revised substantially compared to [17] making it suitable for the proofs. The soundness and completeness proofs (4) have not been published before and are fundamentally new.…”

Section: Introductionmentioning

confidence: 99%

An Operational Semantics for the Cognitive Architecture ACT-R and Its Translation to Constraint Handling Rules

Gall

Frühwirth

2018

ACM Trans. Comput. Logic

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Computational psychology has the aim to explain human cognition by computational models of cognitive processes. The cognitive architecture Adaptive Control of Thought -Rational (ACT-R) is popular to develop such models. Although ACT-R has a well-defined psychological theory and has been used to explain many cognitive processes, there are two problems that make it hard to reason formally about its cognitive models: First, ACT-R lacks a computational formalization of its underlying production rule system and secondly, there are many different implementations and extensions of ACT-R with many technical artifacts complicating formal reasoning even more.This paper describes a formal operational semantics -the very abstract semantics -that abstracts from as many technical details as possible keeping it open to extensions and different implementations of the ACT-R theory. In a second step, this semantics is refined to define some of its abstract features that are found in many implementations of ACT-R -called the abstract semantics. It concentrates on the procedural core of ACT-R and is suitable for analysis of the general transition system since it still abstracts from details like timing, the sub-symbolic layer of ACT-R or conflict resolution.Furthermore, a translation of ACT-R models to the declarative programming language Constraint Handling Rules (CHR) is defined. This makes the abstract semantics an executable specification of ACT-R. CHR has been used successfully to embed other rule-based formalisms like graph transformation systems or functional programming. There are many theoretical results and practical tools that support formal reasoning about and analysis of CHR programs. The translation of ACT-R models to CHR is proven sound and complete w.r.t. the abstract operational semantics of ACT-R. This paves the way to analysis of ACT-R models through CHR analysis results and tools. Therefore, to the best of our knowledge, our abstract semantics is the first abstract formulation of ACT-R suitable for both analysis and execution.An Operational Semantics for ACT-R and its Translation to CHR 39:3[11] like term rewriting systems [22], graph transformation systems [20,23] and business rules [19]. Such embeddings have been used successfully to make the analysis results of CHR available to other approaches. The sound and complete embedding of ACT-R in CHR enables the use of these results and tools to formally reason about cognitive models.The paper is structured as follows: We first give a short introduction to ACT-R in section 2. The formal definition of the very abstract semantics is given in section 3 and the the abstract semantics is defined as an instance in section 4. The translation scheme of abstract ACT-R models to CHR programs is described in section 5 and proven sound and complete w.r.t. the operational semantics in section 6. Related work is discussed in section 7 with a detailed comparison to prior work.

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“…Thus, a different direction was needed; a direction that does not require performing changes to the existing systems. The CHR implementation of ACT-R [8,9,10,11] was used. This implementation uses source-to-source transformation removing the need for any changes.…”

Section: Animation Methodologymentioning

confidence: 99%

“…The rest of the section introduces the basic modules of ACT-R. The work presented in [8,9,10,11] gave a more detailed introduction. Some of the examples given in the section were also introduced in [8].…”

Section: Constraint Handling Rulesmentioning

confidence: 99%

Animating Cognitive Models and Architectures: A Rule-Based Approach

Sharaf¹,

Abdennadher²,

Fruehwirth³

et al.

EPiC Series in Computing

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Computational psychology provides computational models exploring different aspects of cognition. A cognitive architecture includes the basic aspects of any cognitive agent. It consists of different correlated modules. In general, cognitive architectures provide the needed layouts for building intelligent agents. The paper presents a rule-based approach to visually animate the simulations of models done through cognitive architectures. As a proof of concept, simulations through Adaptive Control of Thought-Rational (ACT-R) were animated. ACT-R is a well-known cognitive architecture. It was deployed to create models in different fields including, among others, learning, problem solving and languages.

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Translation of Cognitive Models from ACT-R to Constraint Handling Rules

Cited by 3 publications

References 16 publications

A Decidable Confluence Test for Cognitive Models in ACT-R

A Decidable Confluence Test for Cognitive Models in ACT-R

An Operational Semantics for the Cognitive Architecture ACT-R and Its Translation to Constraint Handling Rules

Animating Cognitive Models and Architectures: A Rule-Based Approach

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