Learning Generalized Reactive Policies using Deep Neural Networks

We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach significantly reduces the dependence of learning on handcrafted domain knowledge or feature selection. Instead, the GRP is trained from scratch using a set of successful execution traces. We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and show that our approach facilitates learning complex decision making policies and powerful heuristic functions with minimal human input. Videos of our results are available at goo.gl/Hpy4e3

Making intelligent systems team players: Overview for designers

This report is a guide and companion to the NASA Technical Memorandum 104738, 'Making Intelligent Systems Team Players,' Volumes 1 and 2. The first two volumes of this Technical Memorandum provide comprehensive guidance to designers of intelligent systems for real-time fault management of space systems, with the objective of achieving more effective human interaction. This report provides an analysis of the material discussed in the Technical Memorandum. It clarifies what it means for an intelligent system to be a team player, and how such systems are designed. It identifies significant intelligent system design problems and their impacts on reliability and usability. Where common design practice is not effective in solving these problems, we make recommendations for these situations. In this report, we summarize the main points in the Technical Memorandum and identify where to look for further information

Artificial Intelligence Research Branch future plans

This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems

A closer look at how Grade 9 Technology teachers incorporate critical thinking in their teaching of the design process: a case study in KwaSanti cluster.

Masters Degree. University of KwaZulu-Natal, Durban.The Technology Curriculum Assessment Policy Statement (DBE, 2011, p.11) states that Technology should promote critical thinking skills via the specific aims using the design process. The design process is regarded as the backbone of Technology (Ohemeng-Appiah, 2014; Mabaso, 2015) and should be used to structure all learning in the Technology classroom in order to promote critical thinking, problem solving and creativity (DBE, 2011). The purpose of this interpretivist study was to explore grade 9 Technology teachers’ understanding of the design process and critical thinking and establish how these teachers promote critical thinking during their teaching of the design process with two critical questions to be answered; 1. What are grade 9 Technology teachers’ understanding of the design process and critical thinking; 2. Do grade 9 Technology teachers promote critical thinking during their teaching of the design process? If so how and? If not, why?. The study sampled conveniently and purposively 5 Technology teachers in the area of KwaSanti as participants with questionnaires, focus group discussions, lesson observation, post-observation interview and document analysis were used to generate data from the participants. The findings of the study were that Technology teachers in KwaSanti understand the design process to be iterative and the process being more essential than the end product. Teachers’ understanding of critical thinking was different from that of the literature. However, it was found that the three teachers whose lessons were observed do promote critical thinking in their teaching of the design process. However, it is important for technology teachers to have a deeper understanding of critical thinking and its associated skills. This could enable learners to develop critical thinking skills that could be useful outside the classroom

High school students’ epistemological approaches to computer simulations of complex systems

The science of complex systems can provide not only scientist, but also professionals, policy-makers and citizens, with thinking resources to interpret and understand most of the modern global challenges. In this field, the widespread use of computational simulations, that are neither theoretical instruments nor laboratory experiments, has been contributing to the widening of the scientific skill gap between experts and citizens. The pilot study we present in this contribution aims at investigating high school students’ approaches towards simulations of complex systems, by searching for the criteria they use to evaluate their explanatory power and the reliability of their results. Preliminary analysis of the paired interviews has shown that (1) rarely students are able to elaborate explanations of the simulated complex phenomena, and (2) their critical attitude and trust towards simulations are strongly affected by their epistemological background. We argue that these findings deserve to be furtherly investigated, to understand in more details the sources of students’ difficulties in recognizing the epistemological and methodological value of simulations for scientific research and practice

A Unified Logical Model for CBR-based E-commerce Systems

This paper will examine new issues resulting from applying CBR in e-commerce and propose a unified logical model for CBR-based e-commerce systems (CECS) which consists of three cycles and covers almost all activities of applying CBR in e-commerce. This paper also decomposes case adaptation into problem adaptation and solution adaptation, which not only improves the understanding of case adaptation in the traditional CBR, but also facilitates the refinement of activity of CBR in e-commerce and intelligent support for e-commerce. It then investigates CBR-based product negotiation. This paper thus gives insight into how to use CBR in e-commerce and how to improve the understanding of CBR with its applications in e-commerce from a logical viewpoint