Evolutionary Concept Formation
A simple and useful way to combine ConceptNodes is to use GA-inspired evolutionary operators: crossover and mutation. In mutation, one replaces some number of a Node's links with other links in the system. In crossover, one takes two nodes and creates a new node containing some links from one and some links from another.
More concretely, to cross over two ConceptNodes X and Y, one may proceed as follows:
- Create a series of empty nodes Z1, Z2,.., Zk
- Choose a number w in [0,1]
- Form a "link pool" consisting of all X's links and all Y's links, and then divide this pool into clusters (clustering algorithms will be described below).
- For each cluster with significant cohesion, allocate the links in that cluster to one of the new nodes Zi
On the other hand, to mutate a ConceptNode, a number of different mutation processes are reasonable. For instance, one can
- Cluster the links of a Node, and remove one or more of the clusters, creating a node with less links
- Cluster the links, remove one or more clusters, and then add new links that are similar to the links in the remaining clusters
The EvolutionaryConceptFormation CIM-Dynamic selects pairs of nodes from the system, where the probability of selecting a pair is determined by
- the average importance of the pair
- the degree of similarity of the pair
- the degree of association of the pair
(Of course, other heuristics are possible too). It then crosses over the pair, and mutates the result.
Note that, unlike in some GA implementations, the parent node(s) are retained within the system; they are not replaced by the children. Regardless of how many offspring they generate by what methods, and regardless of their age, all Nodes compete and cooperate freely forever according to the fitness criterion defined by the importance updating function. The entire AtomSpace may be interpreted as a large evolutionary, ecological system, and the action of Novamente dynamics, as a whole, is to create fit nodes.
A more advanced variant of the EvolutionaryConceptFormation CIM-Dynamic would adapt its mutation rate in a context-dependent way. But our intuition is that it is best to leave this kind of refinement for learned cognitive schemata, rather than to hard-wire it into a CIM-Dynamic. To encourage the formation of such schemata, we introduce elementary schema functions that embody the basic node-level evolutionary operators:
ConceptNode ConceptCrossover(ConceptNode A, ConceptNode B) ConceptNode mutate(ConceptNode A, mutationAmount m)
There will also be a role for more abstract schemata that utilize these. An example cognitive schema of this sort would be one that said: "When all my schema in a certain context seem unable to achieve their goals, then maybe I need new concepts in this context, so I should increase the rate of concept mutation and crossover, hoping to trigger some useful concept formation."
As noted above, this component of Novamente views the whole AtomSpace as a kind of genetic algorithm — but the fitness function is "ecological" rather than fixed, and of course the crossover and mutation operators are highly specialized. Most of the concepts produced through evolutionary operations are going to be useless nonsense, but will be recognized by the importance updating process and subsequently forgotten from the system. The useful ones will link into other concepts and become ongoing aspects of the system's mind. The importance updating process amounts to fitness evaluation, and it depends implicitly on the sum total of the cognitive processes going on in Novamente.
To ensure that importance updating properly functions as fitness evaluation, it is critical that evolutionarily-created concepts (and other speculatively created Atoms) always comprise a small percentage of the total concepts in the system. This guarantees that importance will serve as a meaningful "fitness function" for newly created ConceptNodes. The reason for this is that the importance measures how useful the newly created node is, in the context of the previously existing Atoms. If there are too many speculative, possibly useless new ConceptNodes in the system at once, the importance becomes an extremely noisy fitness measure, as it's largely measuring the degree to which instances of new nonsense fit in with other instances of new nonsense. One may find interesting self-organizing phenomena in this way, but in an AGI context we are not interested in undirected spontaneous pattern-formation, but rather in harnessing self-organizing phenomena toward system goals. And the latter is achieved by having a modest but not overwhelming amount of speculative new nodes entering into the system.
Finally, as discussed earlier, evolutionary operations on maps may occur naturally and automatically as a consequence of other cognitive operations. Maps are continually mutated due to fluctuations in system dynamics; and maps may combine with other maps with which they overlap, as a consequence of the nonlinear properties of activation spreading and importance updating. Map-level evolutionary operations are not closely tied to their Atom-level counterparts (a difference from e.g. the close correspondence between map-level logical operations and underlying Atom-level logical operations).