ACT (Adaptive Control of Thought), ACT*

ACT (Adaptive Control of Thought)

ACT (Adaptive Control of Thought) is a cognitive architecture based on the assumption of a unified theory of mind. The goal of this cognitive theory is to explain how human cognition works and what the structures and processes of human memory, thinking, problem solving, and language are. The core of ACT is a production system with a pattern matcher that works on memory and perceptual-motor modules via buffers. The current version of adaptive control of thought (ACT-R) is based on the principle of rationality of the human mind. Simulations with ACT-R allow for predicting typical measures in psychological experiments like latency (time to perform a task), accuracy (correct vs. false responses), and neurological data (e.g., FMRI-data).

ACT* Adaptive Control of Thought Model

Another network model, Anderson's (1990) ACT* (Adaptive Control of Thought) attempts to take into account all of our human cognition such as language, learning, decision-making and so on. It is not surprising then that it is a complex model which can be difficult to explain. ACT* distinguishes three types of memory structures: declarative knowledge, procedural knowledge and working memory. Declarative knowledge is knowledge about facts and things. Comparatively, procedural knowledge is knowledge about how to perform actions. Working memory is the part of declarative knowledge that is currently working as explained in Baddeley's approach.

According to Anderson, the meaning of a sentence can be broken down into propositions, which are small units of knowledge. For example: the sentence 'Susan gave a white cat to Maria, who is the president of the club' is broken down into three propositions:
1) Susan gave a cat to Maria.
2) The cat was white.
3) Maria is the president of the club.
Anderson also proposes that every concept in a proposition can be represented by a network. He argues that the spread of activation in working memory has a limited capacity as there are 10 nodes represented in working memory only. Thus, if there is too much activity going on, each link receives relatively little activation.

ACT* (J. Anderson) - General Theory of Cognition

Overview:

ACT* is a general theory of cognition developed by John Anderson and colleagues at Carnegie Mellon Univeristy that focuses on memory processes . It is an elaboration of the original ACT theory (Anderson, 1976) and builds upon HAM, a model of semantic memory proposed by Anderson & Bower (1973). Anderson (1983) provides a complete description of ACT*. In addition, Anderson (1990) provides his own critique of ACT* and Anderson (1993) provides the outline for a broader development of the theory. See the CMU ACT site for the most up-to-date information on the theory.

ACT* distinguishes among three types of memory structures: declarative, procedural and working memory. Declarative memory takes the form of a semantic net linking propositions, images, and sequences by associations. Procedural memory (also long-term) represents information in the form of productions; each production has a set of conditions and actions based in declarative memory. The nodes of long-term memory all have some degree of activation and working memory is that part of long-term memory that is most highly activated.

According to ACT*, all knowledge begins as declarative information; procedural knowledge is learned by making inferences from already existing factual knowledge. ACT* supports three fundamental types of learning: generalization, in which productions become broader in their range of application, discrimination, in which productions become narrow in their range of application, and strengthening, in which some productions are applied more often. New productions are formed by the conjunction or disjunction of existing productions.

Scope/Application:
ACT* can explain a wide variety of memory effects as well as account for higher order skills such as geometry proofs, programming and language learning (see Anderson, 1983; 1990). ACT* has been the basis for intelligent tutors (Anderson, Boyle, Farrell & Reiser, 1987).

Example:
One of the strengths of ACT is that it includes both proposition and procedural representation of knowledge as well as accounting for the use of goals and plans. For example, here is a production rule that could be used to convert declarative sentences into a question:

IF the goal is to question whether the proposition (LVrelation LVagent LVobject) is true THEN set as subgoals

1. to plan the communication (LVrelation LVagent LVobject)

2. to move the first word in the description of LVrelation to the beginning of the sentence

3. to execute the plan

This production rule could be used to convert the sentence: "The lawyer is buying the car." into the question: "Is the lawyer buying the car?"

Principles:
1. Identify the goal structure of the problem space.

2. Provide instruction in the context of problem-solving.

3. Provide immediate feedback on errors.

4. Minimize working memory load.

5. Adjust the "grain size" of instruction with learning to account for the knowledge compilation process.

6. Enable the student to approach the target skill by successive approximation.

References:
Anderson, J. (1976). Language, Memory and Thought. Hillsdale, NJ: Erlbaum Associates.

Anderson, J. (1983). The Architecture of Cognition. Cambridge, MA: Harvard University Press.

Anderson, J. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum Associates.

Anderson, J. (1993). Rules of the Mind. Hillsdale, NJ: Erlbaum.

Anderson, J. & Bower, G. (1973). Human Associative Memory. Washington, DC: Winston.

Anderson, J., Boyle, C., Farrell, R. & Reiser, B. (1987). Cognitive principles in the design of computer tutors. In P. Morris (ed.), Modeling Cognition. NY: John Wiley.

Note: Many of Anderson’s articles are available from his CMU home page at http://act-r.psy.cmu.edu/people/ja