By Trinder P.
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Additional info for A functional database
Edu/~sandip) for a list of our papers which would provide a more thorough and representative overview of our research. We will overview the following subset of our research areas: • In Section 2 we present results from our work on trusted, reciprocal relationship maintenance in agent communities and its applications in P2P networks. • In Section 3 we present a multiagent learning scheme that solve well-known social dilemma problems like the Prisoner’s dilemma, • In Section 4 we present a framework by which one agent in the community can teach classiﬁcation knowledge to another agent without knowledge of the latter’s knowledge representation or learning algorithms.
Hence, each agent can only develop a single pair of policies, one as a row player and the other as a column player, to play against any other player from the agent population. In our initial experiments, any two agents in the population had an equal probability of interaction. We observed that social learning was successful in generating consistent norms in the population. The main conclusions of this study was as follows: 36 S. Sen et al. • The number of interactions required to evolve a consistent norm increases with the population size and the number of actions, m , available to each agent.
Sen et al. Experimental results: 1. There exists a lower bound of the number of resources N ∗ for eﬀective system performance. For any agent type, convergence speed is optimal when N = N ∗ . 2. , for N ≥ N ∗ . 3. , in the range N ≥ 100: RT converges faster than NR. 4. , only NR leads to convergence within the iteration limit for N ≤ 20. , for more challenging environments. Referrals, however, do facilitate convergence when there are a signiﬁcant number of providers. 10 Detecting Malfunctioning Nodes in Sensor Networks Sensor network applications often require remote, distributed monitoring of inaccessible and hostile locations.
A functional database by Trinder P.