An architecture for rational agents interacting with complex environments

In this paper we sketch an agent architecture suitable to be used as a tool for exploring agent perception and multiagent interaction. Nowadays, there is no strict correspondence between the theoretical work in rational agents and their implementation. In this respect, it is our intention to reach a good trade-off between expressiveness and implementability.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Kleine Gaben für große Götter

We are working on the development and design of an approach to agents that can reason, react to the environment and are able to update their own knowledge as a result of new incoming information. In the resulting framework, rational, reactive agents can dynamically change their own knowledge bases as well as their own goals. An agent can make observations, learn new facts and new rules from the environment, and then update its knowledge accordingly. The knowledge base of an agent and its updating mechanism has been implemented in Logic Programming. The agent’s framework is implemented in Java. This aim of this thesis is to design and implement an architecture of a reactive, rational agent in both Java and Prolog and to test the interaction between the rational part and the reactive part of the agent. The agent architecture is called RR-agent and consists of six more or less components, four implemented in Java and the other two are implemented in XSB Prolog. The result of this thesis is the ground for the paper “An architecture of a rational, reactive agent” by P. DellAcqua, M. Engberg, L.M. Pereira that has been submitted

An architecture for rational agents interacting with complex environments

In this paper we sketch an agent architecture suitable to be used as a tool for exploring agent perception and multiagent interaction. Nowadays, there is no strict correspondence between the theoretical work in rational agents and their implementation. In this respect, it is our intention to reach a good trade-off between expressiveness and implementability.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Rational physical agent reasoning beyond logic

The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation

Conditional Partial Plans for Rational Situated Agents Capable of Deductive Reasoning and Inductive Learning

Rational, autonomous agents that are able to achieve their goals in dynamic, partially observable environments are the ultimate dream of Artificial Intelligence research since its beginning. The goal of this PhD thesis is to propose, develop and evaluate a framework well suited for creating intelligent agents that would be able to learn from experience, thus becoming more efficient at solving their tasks. We aim to create an agent able to function in adverse environments that it only partially understands. We are convinced that symbolic knowledge representations are the best way to achieve such versatility. In order to balance deliberation and acting, our agent needs to be emph{time-aware}, i.e. it needs to have the means to estimate its own reasoning and acting time. One of the crucial challenges is to ensure smooth interactions between the agent's internal reasoning mechanism and the learning system used to improve its behaviour. In order to address it, our agent will create several different conditional partial plans and reason about the potential usefulness of each one. Moreover it will generalise whatever experience it gathers and use it when solving subsequent, similar, problem instances. In this thesis we present on the conceptual level an architecture for rational agents, as well as implementation-based experimental results confirming that a successful lifelong learning of an autonomous artificial agent can be achieved using it

Know-How for Motivated BDI Agents (Extended Abstract)

ABSTRACT The BDI model is well accepted as an architecture for representing and realizing rational agents. The beliefs in this model are focused on the representation of beliefs about the world and other agents and are widely independent from the agents intentions. We argue that also the representation of know-how, which captures the beliefs about actions and procedures, has to be taken into account when modeling rational agents. Using the notion of know-how as introduced by Singh we formalize and implement a concrete and usable agent architecture that supports and benefits from this representation of procedural beliefs in multiple ways. It also supports the representation of motivations that influence the agent's behavior. We thus enable the agent to reason about its planning capabilities in the same way as it can reason about any other of its beliefs by extending a BDI-based agent architecture to allow the representation of procedural beliefs explicitly as part of the agent's logical beliefs which again influences and enhances the agent's behavior

Social Ethic Behavior Simulation Project

Ethics has usually been considered as the domain of the intrinsic personal belief. Some even claimed that no objective knowledge of ethics is possible. We propose a quite new way of approaching the problem. Although ethics as a part of the personal belief cannot be examined scientifically, the claim that it is not possible to study ethical rules as means of strategy choice is false. The model we bring forward handles the role of ethical rules from the perspective of evolutionary fitnes