Topic selection strategies in multi-agent naming game

Communication is a key capability of autonomous agents in a multiagent system to exchange information about their environment. It requires a naming convention that typically involves a set of predefined names for all objects in the environment, which the agents share and understand. However, when the agents are heterogeneous, highly distributed, and situated in an unknown environment, it is very unrealistic to assume that all the objects can be foreseen in advance, and therefore their names cannot be defined beforehand. In such a case, each individual agent needs to be able to introduce new names for the objects it encounters and align them with the naming convention used by the other agents. A language game is a prospective mechanism for the agents to learn and align the naming conventions between them. In this paper we extend the language game model by proposing novel strategies for selecting topics, i.e. attracting agent's attention to different objects during the learning process. Using a simulated multi-agent system we evaluate the process of name alignment in the case of the least restrictive type of language game, the naming game without feedback. Utilising proposed strategies we study the dynamic character of formation of coherent naming conventions and compare it with the behaviour of commonly used random selection strategy. The experimental results demonstrate that the new strategies improve the overall convergence of the alignment process, limit agent's overall demand on memory, and scale with the increasing number of the interacting agents.