Session 5

This week we learnt about cognitive architecture. Understanding Cognitive architecture (CA) is imperative because it can lead to designing an artificial intelligent system that can replicate human capabilities. CA is more about the underlying infrastructure of an intelligent system. It is not concerned with what is stored in the memory because the content in the memory can change over time and CA deals with those aspects that are constant overtime. CA varies from expert systems as the latter have finite information based on which the possible outcomes are also finite.

There are a few examples of how CA is utilized to construct machines that cognitive abilities. One of them is ACT-R which tries to model human behavior. It has set modules (sensory, motor, intentional and declarative) which process different type of information. In order to understand better how ACT-R works, please follow this link as it shows how a small robot tries to find its way out of a maze (ACT-R Robot). Soar is another example. It is slightly more advanced than ACT-R because it has episodic and semantic memories. It is more goal oriented and in order to achieve it, it dynamically selects the best possible method/way/technique from the information it already has to get to the end result. If it does not have prior information about certain goals then it tries to collect that information first. To truly understand how a Soar system functions, please follow this link as it depicts an example of a train track and train engine (Soar Explained). The other examples of CA would be ICARUS and PRODIGY.

CAs have capabilities such as recognizing, categorizing, decision making, situation assessment, prediction, planning etc which enables them to function dynamically. In addition, the properties of CA include representation, organization, utilization and refinement of knowledge. When evaluating CA aspects such as its generality, taskability, efficacy, reactivity, improvability and autonomy are taken into consideration. The most important take away from this is to understand that CA does not follow “all or none” approach implying that there is possibility that an intelligent agent might not have CA related capabilities. Similarly, if it does not have all the capabilities then it cannot be expected to have all the properties CA has to offer and cannot be assessed or evaluated against all the evaluation criteria mentioned above. Systems based on CA are dynamic and hence will differ in properties.