Daniel Gall

EDACC - an advanced platform for the experiment design, administration and analysis of empirical algorithms

The design, execution and analysis of experiments using heuristic algorithms can be a very time consuming task in the development of an algorithm. There are a lot of problems that have to be solved throughout this process. To speed up this process we have designed and implemented a framework called EDACC, which supports all the tasks that arise throughout the experimentation with algorithms. A graphical user interface together with a database facilitates archiving and management of solvers and problem instances. It also enables the creation of complex experiments and the generation of the computation jobs needed to perform the experiment. The task of running the jobs on an arbitrary computer system (or computer cluster or grid) is taken by a compute client, which is designed to increase computation throughput to a maximum. Real-time monitoring of running jobs can be done with the GUI or with a web frontend, both of which provide a wide variety of descriptive statistics and statistic testing to analyze the results. The web frontend also provides all the tools needed for the organization and execution of solver competitions.

A Formal Semantics for the Cognitive Architecture ACT-R

The cognitive architecture ACT-R is very popular in cognitive sciences. It merges well-investigated results of psychology to a unified model of cognition. This enables researchers to implement and execute domain-specific cognitive models. ACT-R is implemented as a production rule system. Although its underlying psychological theory has been investigated in many psychological experiments, ACT-R lacks a formal definition from a mathematical-computational point of view.

Exchanging Conflict Resolution in an Adaptable Implementation of ACT-R

In computational cognitive science, the cognitive architecture ACT-R is very popular. It describes a model of cognition that is amenable to computer implementation, paving the way for computational psychology. Its underlying psychological theory has been investigated in many psychological experiments, but ACT-R lacks a formal definition of its underlying concepts from a mathematical-computational point of view. Although the canonical implementation of ACT-R is now modularized, this production rule system is still hard to adapt and extend in central components like the conflict resolution mechanism (which decides which of the applicable rules to apply next). In this work, we present a concise implementation of ACT-R based on Constraint Handling Rules which has been derived from a formalization in prior work. To show the adaptability of our approach, we implement several different conflict resolution mechanisms discussed in the ACT-R literature. This results in the first implementation of one such mechanism. For the other mechanisms, we empirically evaluate if our implementation matches the results of reference implementations of ACT-R.

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

Cognitive architectures are used to abstract and simplify the process of computational cognitive modeling. The popular cognitive architecture ACT-R has a well-defined psychological theory, but lacks a formalization of its computational system. This inhibits computational analysis of cognitive models, e.g. confluence or complexity analysis. In this paper we present a source to source transformation of ACT-R models to Constraint Handling Rules (CHR) programs enabling the use of analysis tools for CHR to analyze computational cognitive models. This translation is the first that matches the current abstract operational semantics of ACT-R.

A refined operational semantics for ACT-R: investigating the relations between different ACT-R formalizations

The popular cognitive architecture ACT-R is used in many cognitive models to explain cognitive features of human-beings. It has a well-defined psychological theory but lacks a formalization of its underlying computational system. This lack allows for technical ad-hoc artifacts in the original reference implementation. More importantly, formal analysis of cognitive models is not possible without a well-defined semantics. In prior work we have defined an abstract operational semantics for ACT-Rs production system that is suitable for model analysis. It abstracts from details like timings and conflict resolution methods. However, to describe the behavior of ACT-R implementations a more refined semantics is needed. In this paper, we first introduce a new very abstract operational semantics for ACT-R that serves as formal base to compare different semantics. We define an improved version of the abstract semantics as an instance of our new very abstract semantics. Furthermore, we present a more refined operational semantics that also captures details from actual ACT-R implementations. We show that the refined semantics is sound w.r.t. the abstract semantics. This makes model analysis with the abstract semantics suitable for real-world ACT-R models.

Confluence Modulo Equivalence with Invariants in Constraint Handling Rules

Confluence denotes the property of a state transition system that states can be rewritten in more than one way yielding the same result. Although it is a desirable property, confluence is often too strict in practical applications because it also considers states that can never be reached in practice. Additionally, sometimes states that have the same semantics in the practical context are considered as different states due to different syntactic representations. By introducing suitable invariants and equivalence relations on the states, programs may have the property to be confluent modulo the equivalence relation w.r.t. the invariant which often is desirable in practice. In this paper, a sufficient and necessary criterion for confluence modulo equivalence w.r.t. an invariant for Constraint Handling Rules (CHR) is presented. It is the first approach that covers invariant-based confluence modulo equivalence for the de facto standard semantics of CHR. There is a trade-off between practical applicability and the simplicity of proving a confluence property. Therefore, a better manageable subset of equivalence relations has been identified that allows for the proposed confluence criterion and and simplifies the confluence proofs by using well established CHR analysis methods.

A Decidable Confluence Test for Cognitive Models in ACT-R

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.

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

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, second, there are many different implementations and extensions of ACT-R with many technical artifacts complicating formal reasoning even more. This article 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.