This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation.
In this agent-based system, there are four communities of agents: specification community of agents, function community of agents, physical solution community of agents and production constraint community of agents.
Similarly, we may extend the diversity sets for communities of agents to include support functions that cover the ranges of likelihood ratio values (along with ranges of prior comparative support strengths for hypotheses) drawn from the vagueness sets of members of the scientific community.
The description of the multi-agent framework emphasizes the use of negotiation mechanism to improve the scheduling flexibility, as well as the aspects of information integration, communication and coordination in a community of agents.
For a software information agent, operating on behalf of a human owner and belonging to a community of agents, the choice of communicating or not with another agent becomes a decision to take, since communication generally implies a cost.
The goals will be twofold: to enlarge the community of ?agents for change?
They have devised a mathematical description of that task in which a community of agents is interconnected by social links between individuals, each of whom consults its own personal network in one-to-one interactions to make a decision.
A community of agents is assigned to the task of monitoring a network of sensors in order to assess water quality in a distribution system, and to fire alarms in emergency situations.
Both the vagueness of prior plausibility ratio values for individual agents and the diversity of values among the community of agents can be represented formally by sets of probabilistic support functions, {Pα, Pβ, …}, that agree on the values for the likelihoods but encompass a range of values for the (ratios of) prior probabilities of hypotheses.
So, support functions in collections representing vague prior plausibilities for an individual agent (i.e., a vagueness set) and representing the diverse range of priors for a community of agents (i.e., a diversity set) will very likely come to agree on the near 0 posterior probability of empirically distinct false rivals of a true hypothesis.
At the end of the series?in a final section of this report there is an invocation to you, the reader, to join this small community of agents for change so that we can, together, make the reading of this report, the tipping point for the empowerment of Europe?s youngest researchers and, therein, of Europe?s own global competitiveness.
