Inductive logic programming at 30

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal of ILP is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we survey recent work in the field. In this survey, we focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs that generalise from few examples, (iii) new approaches for predicate invention, and (iv) the use of different technologies, notably answer set programming and neural networks. We conclude by discussing some of the current limitations of ILP and discuss directions for future research.Comment: Extension of IJCAI20 survey paper. arXiv admin note: substantial text overlap with arXiv:2002.11002, arXiv:2008.0791

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Proceedings of the NASA Conference on Space Telerobotics, volume 4

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotic technology to the space systems planned for the 1990's and beyond. Volume 4 contains papers related to the following subject areas: manipulator control; telemanipulation; flight experiments (systems and simulators); sensor-based planning; robot kinematics, dynamics, and control; robot task planning and assembly; and research activities at the NASA Langley Research Center

Kinematic and dynamic analyses of general robots by applying the C-B notation-RaMIP (Robot and Mechanism Integrated Program)

In this thesis, a new symbolic representation based on 4x4 homogeneous matrices, C-B (Cylindrical Coordinates - Bryant Angles) notation, has been applied to the kinematic and dynamic analyses of general robots, and a computer algorithm named RaMIP (Robot and Mechanism Integrated Program) has been developed on a Sun workstation for the design and analysis of robots and mechanisms. RaMIP can be used to model most industrial robots currently in use. It performs three-dimensional kinematic and dynamic analyses and takes advantage of the computational efficiency of C-B notation. The C-B notation allows the user to model an arbitrary mechanism consisting of any combination of revolute, prismatic and spherical joints. RaMIP has the capability of presenting results in the form of two- and three-dimensional plots of colored contours, as well as tables of numerical data. The algorithm is examined and tested by analyzing several commercial robots. Kinematic and dynamic results are computed and presented in two- and three-dimensional graphs and compared with known data to probe the validity and accuracy of RaMIP. It should be noticed that the efforts completed in this thesis present only the first step towards the implementation of a general purpose computer algorithm -RaMIP- for the automated design and analysis of open- and closed-chain mechanisms utilizing C-B notation

Norm Optimal Iterative Learning Control with Application to Problems in Accelerator based Free Electron Lasers and Rehabilitation Robotics

This paper gives an overview of the theoretical basis of the norm optimal approach to iterative learning control followed by results that describe more recent work which has experimentally benchmarking the performance that can be achieved. The remainder of then paper then describes its actual application to a physical process and a very novel application in stroke rehabilitation