Adaptive cognitive systems

model of adaptive communicating agents At an advanced level of multi-agent co-operation, as mentioned above, each agent has its own model of the environment. Thus, each agent has an individual interpretation for the relationship between the messages and the environment. These differences in the agents’ models motivate the development of methods which provide the agents with the ability to learn, including learning to interpret messages from other agents. The agents can perceive their environment, they are part of it, and possibly they can change it. The environment may be a computerized representation, constructed, or natural. The borderlines of these domains may, of course, be vague. A natural environment, in particular, is ever-changing, and consists of various continuous phenomena. We have considered the possibility of applying a natural, or near-natural language as the communication medium. The general properties of natural languages necessitate some capabilities that autonomous agents will need to have. These basic properties of natural languages and their interpretation include ambiguity, contextuality, open-endedness, vagueness, and subjectivity. Ambiguity or vagueness, then, can be considered a necessity when the communication medium is used in an open and changing environment in which having a distinct and a priori determined symbol, or combination of symbols, would be difficult, or even impossible. Finally, to ensure successful communication, both the sending and the receiving agent must share a similar enough framework of interpretation, and the message or the situation must contain enough information to activate a proper framework for the receiver. Adaptive communicating agents based on Self-Organizing Map We have developed a model of communicating agents based on the self-organizing map algorithm [5]. An agent has the following properties: it can perceive its simulated environment, it can move in its environment, it can perform some simple actions, and it can send and receive messages. The main components of its internal structure include a short-term and a long-term memory, and a decision making mechanism. Although these two memory types are closely interconnected, they have different implementations: episodic memory is dynamic and accurate in nature, whereas semantic memory is adaptive and approximative being based on the self-organizing map. The key idea is to provide the means for each agent to associate continuous-valued parameter spaces to sets of symbols, and furthermore, to “be aware” of the differences in this association and to learn those differences explicitly. 126 Adaptive cognitive systems These kinds of abilities are especially required by highly autonomous agents that need to communicate using an open set of symbols or constructs in the agent language. [2,4] The self-organizing map is especially suitable for the central processing element of autonomous agents because of the following reasons: • The self-organizing map algorithm modifies its internal presentation, i.e., the codebook vectors, according to the external input which enables the adaptation of the agents. • The self-organizing map is able to process natural language input to form “semantic maps” [6]. • Symbols and continuous variables may be combined in the input, and are associated by the self-organizing map. Continuous variables may be quantized, and a symbolic interpretation can be given for each section in the possibly very high-dimensional space of perceptual variables [1]. • Because the self-organizing map implements unsupervised learning, processing external input without any prior classifications is possible. The autonomous agent may form an individual model of the environment and of the relation between the expressions of the language and the environment [2]. In general, the basic approach is compatible with the framework of constructive learning theories [3].