From its inception, AI has had a rather ambivalent relationship with humans -- swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human-AI interaction is that the AI systems be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. Drawing from several years of research in our lab, we will discuss how the AI agent can use these mental models to either conform to human expectations, or change those expectations through explanatory communication. While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception. Although the book is primarily driven by our own research in these areas, in every chapter, we will provide ample connections to relevant research from other groups.

Procedural Content Generation (PCG) has seen heavy focus on the generation of levels for video games, aesthetic content, and on rule creation, but has seen little use in other domains. Recently, the ready availability of Long Short Term Memory Recurrent Neural Networks (LSTM RNNs) has seen a rise in text based procedural generation, including card designs for Collectible Card Games (CCGs) like Hearthstone or Magic: The Gathering. In this work we present a mixed-initiative design tool, Mystical Tutor, that allows a user to type in a partial specification for a card and receive a full card design. This is achieved by using sequence-to-sequence learning as a denoising sequence autoencoder, allowing Mystical Tutor to learn how to translate from partial specifications to full.

The use of machine learning (ML) is on the rise in many sectors of software development, and automotive software development is no different. In particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are two areas where ML plays a significant role. In automotive development, safety is a critical objective, and the emergence of standards such as ISO 26262 has helped focus industry practices to address safety in a systematic and consistent way. Unfortunately, these standards were not designed to accommodate technologies such as ML or the type of functionality that is provided by an ADS and this has created a conflict between the need to innovate and the need to improve safety. In this report, we take steps to address this conflict by doing a detailed assessment and adaption of ISO 26262 for ML, specifically in the context of supervised learning. First we analyze the key factors that are the source of the conflict. Then we assess each software development process requirement (Part 6 of ISO 26262) for applicability to ML. Where there are gaps, we propose new requirements to address the gaps. Finally we discuss the application of this adapted and extended variant of Part 6 to ML development scenarios.

Related Problems: Rubik's cube, N-Puzzle and Misspelling Type: Utility Description: A Pattern Database stores a collection of solutions to sub-problems that must be achieved to solve the problem. While we normally think of a heuristic as a function computed by an algorithm, any function can also be computed by a table lookup, given sufficient memory. In fact, for reasons of efficiency, heuristic functions are commonly precomputed and stored in memory. In the case of the N-Puzzle, the tiles occupying certain locations are unspecified (blank). A pattern database (PDB): is the set of all patterns which can be obtained by permutations of a target pattern.

Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a full specification of a joint probability distribution is available, and conclude that it is not suitable for application in knowledge-based systems. Probability intervals, however, constitute a means for expressing incompleteness of information. We present a method for computing such probability intervals for probabilities of interest from a partial specification of a joint probability distribution. Our method improves on earlier approaches by allowing for independency relationships between statistical variables to be exploited.