Learning and Reasoning for Robot Sequential Decision Making under Uncertainty

Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.Comment: In proceedings of 34th AAAI conference on Artificial Intelligence, 202

An Augmented Reality Human-Robot Collaboration System

InvitedThis article discusses an experimental comparison of three user interface techniques for interaction with a remotely located robot. A typical interface for such a situation is to teleoperate the robot using a camera that displays the robot's view of its work environment. However, the operator often has a difficult time maintaining situation awareness due to this single egocentric view. Hence, a multimodal system was developed enabling the human operator to view the robot in its remote work environment through an augmented reality interface, the augmented reality human-robot collaboration (AR-HRC) system. The operator uses spoken dialogue, reaches into the 3D representation of the remote work environment and discusses intended actions of the robot. The result of the comparison was that the AR-HRC interface was found to be most effective, increasing accuracy by 30%, while reducing the number of close calls in operating the robot by factors of ~3x. It thus provides the means to maintain spatial awareness and give the users the feeling of working in a true collaborative environment

GIS Application to Support Land Administration Services in Ghana: Institutional Factors and Software Developments

In June 1999, the Ghanaian Government launched a new land policy document that sought to address some fundamental problems associated with land administration and management in the country. The document identified the weak land administration system as a particular problem and recommended the introduction of computer-aided information systems in the ‘lands sector’. In 2001, the Government made further proposals to prepare and implement a Land Administration Programme (LAP) to provide a better platform for evolving an efficient land administration that would translate the ‘National Land Policy’ into action. Thus, an up-to-date land information system (LIS), supporting efficient management of land records, is to be constructed, which provides a context for the research reported in this paper. We document two aspects of our research on the adoption of GIS by the Lands Commission Secretariat (LCS) which form part of a pilot project in GIS diffusion. Part one of the paper mainly outlines the empirical results arising from fieldwork undertaken during 2001 to determine the information and GIS requirements of the LCS in relation to their routine administrative processes and to identify the critical factors that are required to ensure that any new GIS applications are successfully embraced. Part two explains the prototype software system developed using ArcView 3.2 and Access that provides the LCS with a means to automate some of the routine administrative tasks that they are required to fulfil. The software has been modified and upgraded following an initial evaluation by LCS employees also conducted as part of the fieldwork in Accra

Probabilistic Human-Robot Information Fusion

This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability

Genetic algorithms

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Pilot interministerial operation for remote sensing

Advantages and disadvantages of traditional methods of obtaining required information for land and resources management and the possibilities of remote sensing are discussed. The services available, organization and objectives of the pilot operation are presented. Emphasis is placed on multidisciplinary dialog among designers, builders, operators, interpreters and users in all phases. The principles, operation and practical applications of remote sensing systems and processing systems under the pilot operation are presented

Cross-Border Policies and Spatial and Social Integration: Between Challenges and Problems

The article is discussing both challenges and problems that emerge from an intensified cross-border integration, particularly in Europe, which is creating a sort of ‘cross-border regionalism’ that might be sought as a new constituent part of a complex, multi-level system of governance incorporating not only national, but also local/regional agents. Cross-border regionalism is thus not only a system of government, but also a system of ‘grass-rooted’ social and spatial (re)integration of borderlands. This process is closely related to the question of changing territoriality, preserving on the one hand the regional control and on the other hand re-acting societal and territorial co-dependence