Cognitive Science – The Artificial Intelligence Approach

Artificial intelligence (AI)
A potential for emulating the human brain
Shift the focus of research to the realization of an intelligent agent
Intelligent agent (IA)
Aims to understand and build intelligent machines
Entities that act/ interact with an environment (i.e. hands, legs, mouth)
Universal computing machine
Developed by Alan Turing, a concept that provides that basis on which all modern digital systems are designed: in its contemporary form, commonly referred to as a “finite-state” design or “sequential-system” design; could be employed to design and build an AI machine
Finite state model
Readily implemented within a machine with dedicated integrated circuit switches that can control the entire system
Digital computer organizations
Central processing unit (CPU)
Calculations, logical decisions, program sequence control; determines state transitions; makes cognitive decisions (cognitive manipulation)
Stores programs, results, temporary results, data; stores state definitions, external information, transition rules; facts, cognitive rules, cognitive methods
Input/ output
Sensor information, control of all external system elements; receives sensory information, provides control to external world changes; signals from external sensors and to external actuators, conversion to internal representation, conversion to action signals
Communication (bus)
Communication between other elements of the computer; communication with external world
Turing test
Ultimate test of an IA, if a machine were able to pass the test in its most refined form, it would enable one to make the argument that the “intellectual” responses of that machine were indistinguishable from those of human being
Must surmount a significant number of hurdles
Hierarchical network
Information communication in this is bidirectional; upward flow produces coherent perception, but learned information in memory also flows downward
Completing a task, such as recognizing an object
Vast memory capacity
Moore’s law
A functional relationship between the number of components in integrated circuits and industrial product cycles
Corresponds to 2^40 bits
Units of binary information
An intelligence that is beyond the human level
Evolutionary computing (EC)
A collection of computational methods that have been modeled on the principles of biological evolution
Simple reflex agents
Select actions on the basis of existing precepts – a “survival” mechanism (i.e. IF car ahead is braking, THEN initiate braking)
Model-based reflex agents
Must keep track of precepts that they cannot follow continuously; thus, they maintain some internal state (i.e. agent negotiating a maze needs to keep track of map)
Goal-based agents
Actions depend on the goal to be achieved, in which the agent retains some goal information that describes desirable situations (i.e. when riding in a taxi, the goal might be a passenger’s destination; agent must have some planning and searching algorithms to achieve this)
Utility-based agents
Enhanced goal-based agents, goals are not sufficient to achieve “high-quality” behavior, to these goals, we need to add a “quality factor”
Learning agents
An outgrowth of an idea from Turing, to build a learning machine and then teach it
Multiagent system
A system may often include collections of different agents interacting with one another
Game theory
The rational side of social science – where social is interpreted broadly to include human as well as nonhuman players, such as computers, animals, and plants (i.e. the prisoner’s dilemma)
The programmer must find a suitable representation of the information
Facts, rules; the programmer must specify a sufficient set of rules to define all the categories that the program must support
Algorithmic processes, the programmer must specify in advance the actions to be taken by the system for all combinations of inputs that may occur
Numerous examples come from biological systems where researchers seek to develop models for this behavior, bird migration, visual navigation, and predator-avoidance behavior in fish