This paper introduces differential graphical games for continuous-time non-linear systems and proposes an online adaptive learning framework. The error dynamics and the user-defined performance indices of each agent depend only on local information and the proposed cooperative learning algorithm learns the solution to the cooperative coupled Hamilton–Jacobi equations. In the proposed algorithm, each one of the agents uses an actor/critic neural network (NN) structure with appropriate tuning laws in order to guarantee closed-loop stability and convergence of the policies to the Nash equilibrium. Finally, a simulation example verifies the effectiveness of the proposed approach.
In this paper, we present an agent-based system for distributed risk assessment of breast cancer development employing fuzzy and probabilistic computing. The proposed fuzzy multi agent system consists of multiple fuzzy agents that benefit from fuzzy set theory to demonstrate their soft information (linguistic information). Fuzzy risk assessment is quantified by two linguistic variables of high and low. Through fuzzy computations, the multi agent system computes the fuzzy probabilities of breast cancer development based on various risk factors. By such ranking of high risk and low risk fuzzy probabilities, the multi agent system (MAS) decides whether the risk of breast cancer development is high or low. This information is then fed into an insurance premium adjuster in order to provide preventive decision making as well as to make appropriate adjustment of insurance premium and risk. This final step of insurance analysis also provides a numeric measure to demonstrate the utility of the approach. Furthermore, actual data are gathered from two hospitals in Mashhad during 1 year. The results are then compared with a fuzzy distributed approach.
This paper develops a relative output-feedback-based solution to the containment control of linear heterogeneous multiagent systems. A distributed optimal control protocol is presented for the followers to not only assure that their outputs fall into the convex hull of the leaders' output but also optimizes their transient performance. The proposed optimal solution is composed of a feedback part, depending of the followers' state, and a feed-forward part, depending on the convex hull of the leaders' state. To comply with most real-world applications, the feedback and feed-forward states are assumed to be unavailable and are estimated using two distributed observers. That is, a distributed observer is designed to measure each agent's states using only its relative output measurements and the information that it receives by its neighbors. Another adaptive distributed observer is designed, which uses exchange of information between followers over a communication network to estimate the convex hull of the leaders' state.The proposed observer relaxes the restrictive requirement of having access to the complete knowledge of the leaders' dynamics by all the followers. An off-policy reinforcement learning algorithm on an actor-critic structure is next developed to solve the optimal containment control problem online, using relative output measurements and without requiring the leaders' dynamics. Finally, the theoretical results are verified by numerical simulations.
KEYWORDSadaptive distributed observer, cooperative output regulation, optimal control, output containment control, reinforcement learning 262
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.