Coordination chemistry for information acquisition and processing

In the 21st century, information has become the most valuable resource that is available to modern societies. Thus, great efforts have been made to develop new information processing and storage techniques. Chemistry can offer a wide variety of computing paradigms that are closely related to the natural processes found in living organisms (e.g., in the nervous systems of animals). Moreover, these phenomena cannot be reproduced easily by solely using silicon-based technology. Other great advantages of molecular-scale systems include their simplicity and the diversity of interactions that occur among them. Thus, devices constructed using chemical entities may be programmed to deal with different information carriers (photons, electrons, ions, and molecules), possibly surpassing the capabilities of classic electronic circuits.

Can(Plan)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information

The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, provides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisition (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of Can(Plan) into Can(Plan)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent's beliefs. These epistemic states are stratified to make them commensurable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient semantics. Finally, we examine how primitive actions are affected by uncertainty and we define an appropriate form of lookahead planning.