Multi-Agent Distributed Epistemic Reasoning in Ambient Intelligence Environments

In Ambient Intelligence environments, there exist many different entities, called agents, which collect, process and exchange information about these environments. All agents coexist in the same environment and share the same context. However, each agent faces it from a different aspect according to its role, capabilities, authority and goals. Every agent comes up with its own viewpoint about the environment, acting as an individual entity but cooperating with other agents to accomplish its goals and thus forming an agent system or network. The global consistency of the whole system has introduced new research challenges in the Ambient Intelligence (AmI) field. As agents are sensing the environment variables, incorrect information can arise from missing facts and ambiguous information among the different agents’ perceptions. In this thesis, we model agents as nodes in a peer-to-peer network, considering the conflicts that may arise during the integration of the knowledge distribution. We propose a proof of evidence mechanism to resolve the situations that may arise which is based on a grading mechanism, called certainty degree, and on share theories, which are the combined sub-theories of the participating agents. We examine consistency matters about both the individual theories and the share theories that may be constructed. When we implement an intelligent system we must be able to model several cases of real world states and problems. We present a process of real time distributed reasoning for ambient environments. We use the Event Calculus (EC) as a logic language to model the AMI environment, the agents’ theories about these environments and the events that can occur. We have extended the basic reasoning process of EC and resolve problems where reasoning about real system time must be considered, like “Turn off the oven in 20 minutes” or “if somebody enters the room in the next 5 minutes, send me a message”. Thus, problems like the conditional ‘n>m’ or the ‘n occurrence’ can be encountered in the real time space. We even enrich the expressiveness of our tool by enabling the modeling of contexts with knowledge, preferences and priorities.