Trust learning is a crucial aspect of information exchange, negotiation, and any other kind of social interaction among autonomous agents in open systems. But most current probabil...
As the reach of multiagent reinforcement learning extends to more and more complex tasks, it is likely that the diverse challenges posed by some of these tasks can only be address...
This paper presents the twelve most significant lessons the CeBASE community has learned across a wide variety of projects, domains, and organizations about COTS-Based Systems (CBS...
Donald J. Reifer, Victor R. Basili, Barry W. Boehm...
In the context of probabilistic verification, we provide a new notion of trace-equivalence divergence between pairs of Labelled Markov processes. This divergence corresponds to the...
This chapter presents a generic internal reward system that drives an agent to increase the complexity of its behavior. This reward system does not reinforce a predefined task. It...