Practical, object-based knowledge representation for knowledge-based systems
Information Systems - Knowledge engineering
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Rational Coordination in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
IEEE Internet Computing
Mutual online concept learning for multiple agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
A Distributed Multi-agent Model for Value Nets
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
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Suppose some autonomous shopbot agents had been representing us by dealing with a vendor's pricebot, and suppose they didn't share an agent communication language (ACL). What should they know at a fundamental level, what could each point to, and how could they establish a common language? Recent research at the University of Texas at Arlington has shown that agents first establish a common vocabulary, progress to a primitive language similar to human pidgin, then enrich the language's grammar to develop a creole, and eventually arrive at a full-blown ACL. During this process, the vocabulary and grammatical structures most important to the agents' task at hand appear first. Thus, shopbots and pricebots will first learn to communicate about various types of goods and money, while softbots that deal with, say, stock market investing will likely develop a different language. However, we must make some assumptions about the agents. First, the agents must be knowledge based. Second, the agents must be purposeful, with well-defined goals, that is, precise descriptions of the states of the world they are to bring about. Third, the agents must be rational. This means they act so as to further their goals, given what they know