Modelling Rational Inquiry in Non-ideal Agents

The construction of rational agents is one of the goals that has been pursued in Artiicial Intelligence (AI). In most of the architectures that have been proposed for this kind of agents, its behaviour is guided by its set of beliefs. In our work, rational agents are those systems that are permanently engaged in the process of rational inquiry; thus, their beliefs keep evolving in time, as a consequence of their internal inference procedures and their interaction with the environment. Both AI researchers and philosophers are interested in having a formal model of this process, and this is the main topic in our work. Beliefs have been formally modelled in the last decades using doxastic logics. The possible worlds model and its associated Kripke semantics provide an intuitive semantics for these logics, but they seem to commit us to model agents that are logically omniscient and perfect reasoners. We avoid these problems by replacing possible worlds by conceivable situations, which are all the situations that the modelled agent is capable of considering. In this document we show how this notion of conceivable situations may be used to model the process of rational inquiry in which a non-ideal rational agent is engaged. We deene a wide class of agents, called rational inquirers, which are a general abstraction of any kind of non-ideal agent. We show how the beliefs of this kind of agents evolve in time as a consequence of a multi-dimensional belief analysis, and we use the framework of conceivable situations in order to model this evolution. 1 Aim of the work 1.1 Introduction Artiicial Intelligence (AI) has pursued the goal of constructing rational agents for a long time. This kind of agents may be deened ((RuNo95]) as systems that have some kind of perception and try to act upon the environment so as to achieve their goals, given their beliefs; thus, beliefs somehow guide their behaviour (e.g. they may be used to choose between alternative courses of action available to the agent). The architecture of such agents usually includes a knowledge base (that stores relevant facts about the agent and its environment) and modules that may perform inferences from those facts, interact with the agent's environment, create and evaluate diierent plans, etc. In the last decade it has been argued that, if the agent is to display a rational behaviour, it needs to have a module capable of handling …