Abstract:
Emergent processes are business processes whose execution is determined by the prior knowledge of the agents
involved and by the knowledge that emerges during a process instance. The amount of process knowledge that
is relevant to a knowledge-drivenprocess can be enormous and may include common sense knowledge. If
a process' knowledge can not be represented feasibly then that process can not be managed; although its
execution may be partially supported. In an e-market domain, the majority of transactions, including trading
orders, requests for advice and information, are knowledge-driven processes for which the knowledge base is
the Internet, and so representing the knowledge is not at issue. Multiagent systems are an established platform
for managing complex business processes. What is needed for emergent process management is an intelligent
agent that is driven not by a process goal, but by an in-flow of knowledge, where each chunk of knowledge
may be uncertain. These agents should assess the extent to which it chooses to believe that the information is
correct, and so they require an inference mechanism that can cope with information of differing integrity. An
agent is described that achievesthis by using ideas from information theory, and by using maximum entropy
logic to derive integrity estimates for knowledge about which it is uncertain. Emergent processes are managed
by these agents that extract the process knowledge from this knowledge base - the Internet - using a suite
of data mining bots. The agents make no assumptions about the internals of the other agents in the system
including their motivations, logic, and whether they are conscious of a utility function. These agents focus
only on the information in the signals that they receive.
