Watching A Language Evolve Among Robotic Agents

In yesterday's post, I described a solution to the "gavagai problem" (which states, according to one interpretation, that word learning is an intractable task because any word can in principle have an infinite number of referents, even when learned in the context of direct instruction) by Landauer & Dumais, who used a machine learning technique (called latent semantic analysis, or LSA) to extract the meaning of words by plotting a similarity relationship between each word and every other word ever encountered by the progam, as a function of the contexts in which they appear.

Although showing impressive results, this approach seems to miss a fundamental and intuitive aspect of language learning: words seem to be defined primarily as relating to objects in the real world, and only secondarily as relating to other words. In other words, LSA word meanings are not "grounded" in real-world experience ... so to what extent can we think of it as truly understanding the meanings of words, human-competitive data notwithstanding?

A more intuitively fulfilling solution to the gavagai problem might explore the way in which speakers come to understand the real-world objects to which a given term refers, rather than developing a solely recursive understanding of word meaning. In their chapter in Linguistic evolution through language acquisition: formal and computational models, authors Steels and Kaplan describe experiments with two robotic agents whose job is to communicate about objects in their environment by developing their own language.

Steels & Kaplan implement this task as follows: two robots face an environment populated only by 2-D shapes of different colors on a whiteboard. Each robot consists of a camera, a catagorization system, and a verbalization system, which in combination are capable of segmenting a visual image into objects, with each object defined by its spatial position and RGB color values. The robots take turns playing the roles of speaker and hearer in what the authors call "the guessing game", in which a speaker first picks an object from the environment, and then communicates to the hearer a series of syllables that best categorize that object uniquely among all the other objects in the environment (where "best categorize" is defined in terms of weight, as discussed below). For example, the speaker may use the phrase wovota to indicate the object of interest is in the upper left corner. The hearer will then use its database of terms and their associated meanings to pick the object it believes the speaker is referring to. The speaker then verifies whether the picked object is actually the one to which it was referring; if the hearer was correct, both the speaker and hearer increase the "weight" of the relationship between that term (wovota) and the internal representations (upper left) while decreasing the weight of all competing associations. If the hearer picked the wrong referent, the relationship between wovota and the internal representation is decreased in weight. Such games are played thousands of times in a row, in which the agents playing speaker and hearer are rotated so that all agents in the environment have played the games with other agents. By the conclusion of training, the arrive at a set of consistent - or mostly consistent - terms for objects in their environment.

However, consider what would happen if the speaker had used the term wovota to indicate "very red," but this very red object was located in the upper left corner of the environment - in this case, the hearer would have been correct, but for the wrong reason: the term wovota was actually intended to reflect redness, but was interpreted as reflecting spatial position. In this case, future uses of the term wovota by the speaker would be necessary in order for the hearer to correctly reinterpret the phrase.

Consequently, yet another type of misunderstanding is possible: if the speaker uses the term wovota to indicate the very red object in an environment consisting of both one red and one blue objects in the upper left hand corner, the hearer will not be able to successfully identify the referent of wovota. In this case, the hearer will not be able to pick an object, and so the speaker will identify the referent of wovota. Now a new meaning for wovota will be stored (one involving redness, most likely), and will begin to compete with the previous interpretation of "upper left."

These types of miscommunications are prototypical examples of the "gavagai problem." And yet, the communicative performance of these robotic agents is in many ways very language-like and actually quite impressive (even more so, when you consider that they begin with no shared terminology at all!). For example:

• Synonyms tend to emerge during training, such that one agent prefers one term for something, while the other agent may prefer another. As the authors note, this situation arises when one agent invents a new term after having incorrectly interpreted a term that is already in existence.
  
• Likewise, homonyms also emerge during training, in which one word can have multiple meanings )
• Some meanings of words fall out of usage among the agent population, just as in human language
• When new objects were added to the environment, thus making them more complex, success in the guessing game dipped sharply, but quickly rebounded as agents derived new words or differentiated the possible meanings of old words.

Above and beyond these surface similarities to human language, what is most impressive is that semantics can spontaneously emerge from a population of simple agents, whose behavior is guided only by a few simple rules. These rules involve uniquely identifying objects in the environment, and maintaining representations of word meaning that compete with each other for predictiveness. One cannot quite call this a "language" since it lacks so many other aspects of linguistic experience - phonology and syntax to name but a few. Nonetheless, Steels and Kaplan seem to have solved the gavagai problem in a completely different way than the approach embodied by LSA. The extent to which these approaches can be integrated, and extended to account for other data, is yet to be determined.