Involving the Human User in the Control Architecture of an Autonomous Agent

Abstract. The paper presents an architecture for an autonomous robotic agent, which carries on a plan in a partially observable environment. A Supervisor module is in charge of assuring the correct execution of the plan, possibly by inferring alternative recovery plans when unexpected contingencies occur. In the present paper we describe a control strategy where a human user is directly involved in the control loop, and plays the role of advisor by helping the robotic agent both for reducing ambiguity in the robot's observations, and for selecting the preferred recovery plan

Involving the Human User in the Control Architecture of an Autonomous Agent

Abstract. The paper presents an architecture for an autonomous robotic agent, which carries on a plan in a partially observable environment. A Supervisor module is in charge of assuring the correct execution of the plan, possibly by inferring alternative recovery plans when unexpected contingencies occur. In the present paper we describe a control strategy where a human user is directly involved in the control loop, and plays the role of advisor by helping the robotic agent both for reducing ambiguity in the robot's observations, and for selecting the preferred recovery plan

Improving the Efficiency on Decision Making Process via BDD

For a qualitatively and quantitatively analysis of a complex Decision Mak- ing (DM) process is critical to employ a correct method due to the large number of operations required. This paper presents an approach employing Binary Decision Diagram (BDD) applied to the Logical Decision Tree. LDT allows addressing a Main Problem (MP) by establishing different causes, called Basic Causes (BC) and their interrelations. The cases that have a large number of BCs generate important computational costs because it is a NP-hard type problem.. This paper presents a new approach in order to analyze big LDT. A new approach to reduce the complex- ity of the problem is hereby presented. It makes use of data derived from simpler problems that requires less computational costs for obtaining a good solution. An exact solution is not provided by this method but the approximations achieved have a low deviation from the exact

A hierarchical task-network planner based on symbolic model checking

Although several approaches have been developed for planning in nondeterministic domains, solving large planning problems is still quite difficult. In this work, we present a novel algorithm, called YoYo, for planning in nondeterministic domains under the assumption of full observability. This algorithm enables us to combine the power of search-control strategies as in Planning with Hierarchical Task Networks (HTNs) with techniques from the Planning via Symbolic Model-Checking (SMC). Our experimental evaluation confirms the potentialities of our approach, demonstrating that it combines the advantages of these paradigms

Metodología de síntesis de autómatas para controlar sistemas de navegación autónoma terrestre

In this work we propose a methodology for the synthesis of automatons in autonomous ground navigationsystems with a global task. Our method is based on the design of automatons using regular grammars allowing for the generation of control policies for autonomous driving in a partially controlled environment where information is extracted using sensors. Furthermore, we showcase the problems that arise whenapproaching this problem with traditional synthesis of finite nondeterministic automatons. Finally, in the results section, we present the validation of the proposed method with simulations using MATLAB© and the Toolbox for Virtual Reality (V-Realm Builder).En este trabajo se plantea una metodología para la síntesis de autómatas en aplicaciones de navegación autónoma terrestre con una tarea global. La metodología propuesta se basa en el diseño de autómatas por gramáticas regulares y permite generar una política de control para la conducción autónoma de un vehículo en un ambiente parcialmente controlado mientras se extrae información del entorno a través de sensores. Además, se plantean los inconvenientes que se presentan al tratar de abordar esta aplicación con enfoques tradicionales que implican la síntesis de autómatas finitos no deterministas. Por último, se presentan losautómatas obtenidos al validar la metodología propuesta a través de simulaciones utilizando el software MATLAB con el Toolbox integrado de realidad virtual (V-Realm Builder)