Contributed by: Guadalupe Sánchez, Intelligent Control Group, Universidad Politécnica de Madrid

Introduction
ACT-R (Adaptive Control of Thought--Rational) is a cognitive architecture mainly developed by John Robert Anderson at Carnegie Mellon University, which is also a theory about how human cognition works. Most of the ACT-R basic assumptions are also inspired by the progresses of cognitive neuroscience, and, in fact, ACT-R can be seen and described as a way of specifying how the brain itself is organized in a way that enables individual processing modules to produce cognition.

Like other influential cognitive architectures, the ACT-R theory has a computational implementation as an interpreter of a special coding language. The interpreter itself is written in Lisp, and might be loaded into any of the most common distributions of the Lisp language.

Like a programming language, ACT-R is a framework: for different tasks (e.g., Tower of Hanoi, memory for text or for list of words, language comprehension, communication, aircraft controlling), researchers create "models" (i.e., programs) in ACT-R. These models reflect the modelers' assumptions about the task within the ACT-R view of cognition. The model might then be run and afterwards tested by comparing the results of the model with the results of people doing the same tasks. By "results" we mean the traditional measures of cognitive psychology:

ACT-R has been used successfully to create models in domains such as:

Beside its scientific application in cognitive psychology, ACT-R has been used in other, more application-oriented oriented domains.

Achitectural Overview
ACT-R architecture consists of a set of modules, each devoted to processing a different kind of information. Coordination in the behaviour of these modules is achieved through a central production system , constituted by the procedural memory and a pattern matcher. This central production system is not sensitive to most of the activity of these modules but rather can only respond to the limited amount of information that is stored in the buffers of the modules.

ACT-R's most important assumption is that human knowledge can be divided into two irreducible kinds of representations: declarative and procedural. Within the ACT-R code, declarative knowledge is represented in form of chunks, which are schema-like structures, consisting of an isa slot specifying their category and some number of additional slots encoding their contents

ACT-R Modules
There are two types of modules:

• Perceptual-motor modules, which take care of the interface with the real world (i.e., with a simulation of the real world). The most well-developed perceptual-motor modules in ACT-R are the visual and the motor modules.
• Memory modules. There are two kinds of memory modules in ACT-R:
  • Declarative memory, consisting of facts such as ``a dog is a mammal", ``Rome is a city", or 1+2=3, encoded as chunks.
  • Procedural memory, made of productions. Productions represent knowledge about how we do things: for instance, knowledge about how to write the letter 'a', about how to drive a car, or about how to perform addition.

Buffers
ACT-R accesses all its modules through buffers. The only exception to this rule is the procedural module, which stores and applies procedural knowledge. It does not have an accessible buffer and is actually used to access other module's contents. For each module, a dedicated buffer serves as the interface with that module. The contents of the buffers at a given moment in time represents the state of ACT-R at that moment.

The goal buffer represents where the agent is in the task and preserves information across production cycles.

Pattern Matcher
The pattern matcher searches for a production that matches the current state of the buffers. Only one such production can be executed at a given moment. That production, when executed, can modify the buffers and thus change the state of the system. Thus, in ACT-R cognition unfolds as a succession of production firings.

ACT-R Operation
The buffers in ACT-R hold representations in the form of chunks determined by the external world and internal modules. In each cycle patterns in these buffers are recognised, a production matching that pattern fires and its execution changes the state in the buffers, which are updated for the next cycle.

The architecture assumes a mixture of parallel and serial processing. Within each module, there is a great deal of parallelism. For instance, the visual system is simultaneously processing the whole visual field, and the declarative system is executing a parallel search through many memories in response to a retrieval request. Also, the processes within different modules can go on in parallel and asynchronously. However, there are also two levels of serial bottlenecks in the system. First, the content of any buffer is limited to a single declarative unit of knowledge, a chunk. Thus, only a single memory can be retrieved at a time or only a single object can be encoded from the visual field. Second, only a single production is selected at each cycle to fire.

ACT-R is a hybrid cognitive architecture. Its symbolic structure is a production system; the subsymbolic structure is represented by a set of massively parallel processes that can be summarized by a number of mathematical equations. The subsymbolic equations control many of the symbolic processes. For instance, if several productions match the state of the buffers (conflict), a subsymbolic utility equation estimates the relative cost and benefit associated with each production and decides to select for execution the production with the highest utility. Similarly, whether (or how fast) a fact can be retrieved from declarative memory depends on subsymbolic activation equations for chink retrieval, which take into account the context and the history of usage of that fact. Subsymbolic mechanisms are also responsible for most learning processes in ACT-R.

ACT-R can learn new productions through composition. In the case two productions may fire consecutively a new one can be created by collapsing the previous two and embedding knowledge from declarative memory, which would be that chunks in the buffers that matched the old productions.

References

1. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review 111, (4), 1036-1060