Page 250 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Introduction to Artificial Intelligence & Expert Systems

Notes Self Assessment

State whether the following statements are true or false:
10. Semantic and episodic memory together make up the category of declarative memory,
which is one of the two major divisions in memory.

11. Processing in a semantic network often takes the form of spreading activation.
12. The set of associations among a collection of items in memory is equivalent to the links
between nodes in a network.

13.5 Knowledge Acquisition and Validation

The frame contains information on how to use the frame, what to expect next, and what to do
when these expectations are not met. Some information in the frame is generally unchanged
while other information, stored in “terminals”, usually change. Different frames may share the
same terminals.
Each piece of information about a particular frame is held in a slot. The information can contain:

Facts or Data
Values (called facets)
Procedures (also called procedural attachments)
IF-NEEDED: deferred evaluation
IF-ADDED: updates linked information
Default Values

For Data
For Procedures
Other Frames or Subframes

Neural Network

The term “neural network” was traditionally used to refer to a network or circuit of biological
neurons. The modern usage of the term often refers to artificial neural networks, which are
composed of artificial neurons or nodes. Thus, the term may refer to either biological neural
networks, made up of real biological neurons, or artificial neural networks, for solving artificial
intelligence problems.
Unlike von Neumann model computations, artificial neural networks do not separate memory
and processing and operate via the flow of signals through the net connections, somewhat akin
to biological networks.
These artificial networks may be used for predictive modeling, adaptive control and applications
where they can be trained via a dataset.
The word “network” in the term ‘artificial neural network’ refers to the inter – connections
between the neurons in the different layers of each system. An example system has three layers.
The first layer has input neurons, which send data via synapses to the second layer of neurons,
and then via more synapses to the third layer of output neurons. More complex systems will
have more layers of neurons with some having increased layers of input neurons and output
neurons. The synapses store parameters called “weights” that manipulate the data in the
calculations.

244 LOVELY PROFESSIONAL UNIVERSITY