cats_dogs

README

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Introduction

This project explores a simple semantic network intended to represent a (very small) set of relationships among different features used to represent a set of entities in the world. In our case, we represent some features of cats and dogs: their color, size, favorite food, and favorite toy. The network contains information about a number of individual cats and dogs, and is able to use this information to make generalizations about what cats and dogs in general have in common, and what is unique about them. It can also tell you about the consistency of a given feature with either cats or dogs -- this is where the harmony function can be useful in assessing the total constraint satisfaction level of the network with a particular configuration of feature inputs. The network can also be used to perform pattern completion as a way of retrieving information about a particular individual or individuals. Thus, this simple network summarizes many of the topics covered in this chapter.

Cats and Dogs Semantics:

Species	Name	Color	Size	Food	Toy
Cat	Chloe	Orange	Small	Grass	String
	Socks	Black & White	Small	Bugs	Feather
	Sylvester	Black & White	Small	Grass	String
	Garfield	Orange	Medium	Scraps	String
	Fuzzy	White	Medium	Grass	Feather
Dog	Daisy	Black	Large	Scraps	Bone
	Fido	Brown	Medium	Shoe	Shoe
	Spot	Black & White	Medium	Scraps	Bone
	Snoopy	Black & White	Medium	Scraps	Bone
	Penny	Brown	Large	Shoe	Shoe

The knowledge embedded in the network is summarized in the above table. Although in later networks we will see how this knowledge can be learned with experience (e.g., with individual cats and dogs), here this knowledge is encoded by simply setting a strong weight (usually 1) between an instance (Identity) node representing an individual cat or dog and the corresponding feature (e.g., brown) that this individual possesses (c.f., the Jets and Sharks model from McClelland & Rumelhart, 1988). Each of the groups of features (i.e., values within one column of the table) are represented within distinct layers that have their own within-layer inhibition, so that units in each layer compete to best represent the input. In addition, all of the identity units and the name units are within their own separate layers as well. We use the FFFB inhibitory function here which allows considerable flexibility in the actual number of active units per layer.

Exploration

• As usual, take some time to examine the weights in the network, and verify that the weights implement the knowledge shown in the table. To do so, select the Wts / r.Wt value in the Network tab and then click on individual neurons in the different layers.

Let's first verify that when we present an individual's name as input, it will recall all of the information about that individual. This is a form of pattern completion with a single unique input cue.

• Select the Act / Act value in the Network tab. Set Step to Cycle instead of Trial and press Step repeatedly to present the default input patterns to the network, which activates the Chloe name unit.

You should see that after this input is processed over time, the network activates the appropriate features for Chloe. You can think about this process as finding the most harmonious activation state given the input constraint of Chloe, and the constraints in the network's weights. Equivalently, you can think about it as settling into the Chloe attractor.

• Click on the CatsAndDogs button in the left control panel next to Patterns, and double-click on the pattern for the Name layer, which brings up an edit window where you can edit the values. Zero out the first cell and add a 1 into the second one (this is akin to now showing the network the name 'Socks' instead of Chloe). Do Init and Test Trial responds to this (should be as expected from the above table). Go ahead and try a few other name activations (change the appropriate value from 0 to 1).

Now, let's see how this network can give us general information about cats versus dogs, even though at some level it just has information about a set of individuals (i.e., the knowledge in weights from identities to each feature).

• Set all the Name inputs to 0, and then double-click on the Species inputs, and set the first unit to 1 (Cat), and do Init and Step through the cycles again. You can also use the VCR Time buttons at the bottom-right to go back and replay the cycle-by-cycle activation settling.

You should see that the network activates features that are typical of cats, and ends up settling on a subset of cat individuals.

Question 3.4: Explain why the subset of cat individuals ended up getting activated, when just cat was provided as input -- how might this differential activation of individuals provide useful information about different cats in relation to the general cat category?

• Repeat this test but instead activate the dog unit instead of cat.

Constraint Satisfaction

Now let's make use of some of the constraint satisfaction ideas. We can view the harmony of the network over cycles of settling using a graph view.

• Go back to testing just cat and select the Test Cycle Plot tab to view a plot of harmony over cycles of settling.

Notice that, as we expected, this value appears to monotonically increase over settling, indicating that the network is increasingly satisfying the constraints as the activations are updated.

Now, let's make some more specific queries for the network.

• Activate the large size input (last unit in Size column) in addition to cat, and test that.

You should see that the final harmony value is lower than that for just cat alone (even though it starts out higher initially). This lower harmony reflects the fact that you provided discordant, inconsistent constraints, which the network was not able to satisfy as well (indeed if you look network, it ended up struggling for a long time and then flipping over to representing a dog, Daisy).

• Turn off the large size input and turn on the medium one (middle unit in Size column) in addition to cat, and test again.

Although this case starts off earlier, the final harmony is also lower than cat alone, because even though there are some medium-sized cats, there are fewer of them, so the constraints are tighter. Put another way, cat is an easy constraint to satisfy, so the resulting harmony is large. cat plus medium is harder to satisfy because it applies to fewer things, so the harmony is lower.

There are a seemingly infinite number of different ways that you can query the network -- go ahead and present different input patterns and see what kinds of responses the network gives you. Most of them will we hope be recognizable as a reasonable response to the set of constraints provided by the input pattern.

Have fun experimenting!

Documentation

Overview

cats_dogs: This project explores a simple **semantic network** intended to represent a (very small) set of relationships among different features used to represent a set of entities in the world. In our case, we represent some features of cats and dogs: their color, size, favorite food, and favorite toy.