Signifiers for Affordance-driven Multi-Agent Systems

The ecological psychologist James J. Gibson defined the notion of affordances to refer to what action possibilities environments offer
to animals. In this paper, we show how (artificial) agents can discover and use affordances in a Multi-Agent System (MAS) environment to achieve their goals. To indicate to agents what affordances are present in their environment and whether it is likely that these may help the agents to achieve their objectives, the environment may expose signifiers while taking into account the current situation of the environment and of the agent. On this basis, we define a Signifier Exposure Mechanism that is used by the environment to compute which signifiers should be exposed to agents in order to permit agents to only perceive signifiers that are likely to be relevant to them, and thereby increase their efficiency. If this
is successful, agents can interact with partially observable environments more efficiently because the signifiers indicate the affordances they can use towards which purposes. Signifiers thereby facilitate the exploration and the exploitation of MAS environments. An implementation of signifiers and of a Signifier Exposure Mechanism is presented within the context of a Hypermedia Multi-Agent System and the utility and efficiency of this model is presented through the development of a scenario.