Features of the information available to an agent through its sensors can be used to construct concepts, also called meanings. Constructing concepts based on the requirements posed by the environment is a more flexible approach than fixing the concepts of agents at design time, and may be necessary when the agents are to function in unknown or changing environments.
In this thesis, a particular type of concepts is described, called situation concepts. Situation concepts consist of features in the history of interaction between the agent and its environment, which consists of sensor data, actions, and subsequent evaluative feedback. A defining criterion of a situation concept is that it predicts some aspect of the future evolution of the state of the environment, possibly conditioned on the actions the agent may take. Several existing methods, particularly from the field of reinforcement learning, can be viewed as constructing a form of situation concepts. A particular method for constructing a specific type of situation concepts, called the adaptive subspace method, is described. The method uses the current sensor values of an agent as features and develops concepts that specify an interval for each sensor. These concepts predict the value of actions the agent can take when its current sensor values are within the specified intervals. The meanings thus formed represent situations, and are especially appropriate for use in communication, since they convey information about the environment.
The development of communication is viewed as the formation of associations between words and the meanings formed by the agents in a population, in such a way that agents tend to use the same word in the same situation. When agents autonomously construct concepts, a consequence is that they may not possess identical concepts. Additional constraints that are respected, such as the commitment that agents have no direct access to the meanings formed by other agents (they can not 'look inside each other's head'), and that no single agent may decide on the system of communication, further complicate the problem of how such a system of associations may come about.
Rather than viewing communication as fixed, it is viewed as a dynamical system. A dynamical system is a mathematical model of a system that changes over time. The variables of this system are the strengths of the associations between words and the meanings of the agents in a population. An algorithm is described in detail that, when used by each individual agent to adapt its associations between words and the situation concepts it has formed, leads to a shared system of communication.
The necessity of different components of the algorithm is shown with statistical significance. Associations are linear combinations of use (the frequency with which a word is observed in a situation) and success (the degree to which the word correctly indicates that its associated situation is the current situation in the environment). Analysis of the success component of the algorithm showed that not the success information itself, but the lateral inhibition between competing associations is crucial for the development of communication. It is experimentally demonstrated how the development of communication can compensate for differences in conceptual systems.
Systematic measures have been introduced to determine the quality of conceptual systems and communication systems. The measures require knowledge of the ideal concepts, called referents; although such knowledge is not available in general, simulation experiments often do provide the opportunity for such referents to be determined.
The specificity measure for communication is based on the principle that knowledge of a word should yield information (i.e. reduce uncertainty) about the referent (the current situation in the environment), and vice versa for the consistency measure. In cases of maximal specificity, the information a word yields is complete, and thus identifies the situation, whereas in the worst case, the word does not yield any information at all. The measure quantifies these and all intermediate cases. The consistency measure is computed as the extent to which a referent identifies a word, and thus expresses whether for each a referent the agent consistently uses the same word. If both specificity and consistency are high, each agent consistently uses a unique word for each referent. A population measure called coherence is used to determine whether different agents use the same words. In combination with the specificity and consistency measures, the experimenter can determine to what degree a perfect system of communication, consistently linking each referent to a unique word, is approximated.
Interestingly, the same principle can be used to evaluate the quality of a conceptual system. Ideally, each concept an agent has formed identifies a single referent in the environment; this is expressed by the distinctiveness measure, calculated as the degree to which the meanings an agent possesses distinguish between the different referents. Conversely, parsimony expresses the degree to which a referent identifies a meaning. It thus reflects whether the agent has not generated more meanings than necessary. Together, high distinctiveness and high parsimony imply that a conceptual system is ideal in the sense that it approximates a one-to-one relation between meanings and referents.
A contribution is made to the viewpoint of communication as a dynamical system by considering the attractors in the communication system that has been described. A deterministic version of the system is proved mathematically and demonstrated experimentally to have point attractors that correspond to perfect communication. An operational definition of pseudo-attractors is used to demonstrate that the stochastic system has points that play a similar role. Stochasticity is found to be a useful ingredient in the development of communication, in that it avoids deadlocks and results in communication more consistently and under a wider variety of parameter settings. This finding is confirmed by a systematic investigation of the effect of different amounts of stochasticity, regulated by the temperature parameter that governs word production. The analysis provides evidence that a dynamical systems perspective on the development of communication is valuable.