We consider the problem of constructing abstract representations for planning in high-dimensional, continuous environments. We assume an agent equipped with a collection of high-level actions, and construct representations provably capable of evaluating plans composed of sequences of those actions. We first consider the deterministic planning case, and show that the relevant computation involves set operations performed over sets of states. We define the specific collection of sets that is necessary and sufficient for planning, and use them to construct a grounded abstract symbolic representation that is provably suitable for deterministic planning. The resulting representation can be expressed in PDDL, a canonical high-level planning domain language; we construct such a representation for the Playroom domain and solve it in milliseconds using an off-the-shelf planner. We then consider probabilistic planning, which we show requires generalizing from sets of states to distributions over states. We identify the specific distributions required for planning, and use them to construct a grounded abstract symbolic representation that correctly estimates the expected reward and probability of success of any plan. In addition, we show that learning the relevant probability distributions corresponds to specific instances of probabilistic density estimation and probabilistic classification. We construct an agent that autonomously learns the correct abstract representation of a computer game domain, and rapidly solves it. Finally, we apply these techniques to create a physical robot system that autonomously learns its own symbolic representation of a mobile manipulation task directly from sensorimotor data---point clouds, map locations, and joint angles---and then plans using that representation. Together, these results establish a principled link between high-level actions and abstract representations, a concrete theoretical foundation for constructing abstract representations with provable properties, and a practical mechanism for autonomously learning abstract high-level representations.

Doina Precup, an associate professor of computer science at McGill University, sees this research as having broad implications for teaching artificial intelligence ways to approach complex tasks with limited information. She says this novel method of machine learning has great similarities with how our human brains work, and claims this research brings us one step closer to AI achieving a type of "general intelligence".

The debate over what to do about the approximately 700,000 Dreamers – immigrants who were brought to the U.S. illegally as children – has often been conducted at a playground level. Democrats are threatening to shut down the government at midnight Friday night unless the Dreamer issue is resolved on their terms. President Trump is reported to have used some kind of obscenity to describe his view of some of the countries Dreamers and other immigrants come from. Immigration activists have freely and openly accused the president of being a "racist" or worse. In this kind of a toxic environment, it will be amazing if both sides can bridge the enormous gap between them.

Chan Zuckerberg Initiative awards $5.5 million to UMass for artificial intelligence project … The project's goal is to create an intelligent and navigable map of scientific knowledge using a branch of artificial intelligence called "knowledge representation and reasoning," according to a UMass press release.

The two bought a generic cube puzzle, since it was looser and would slide easier. They then placed different textured items on each side. One side was left smooth and the other had plastic squares. Another side had scratchy Velcro and the opposite had soft Velcro. The final two sides had squishy craft dots and hard plastic dots.

This new conference series promotes multidisciplinary research on tools and methodologies for efficiently capturing knowledge from a variety of sources and creating representations that can be (or eventually can be) useful for reasoning. The conference attracted researchers from diverse areas of AI, including knowledge representation, knowledge acquisition, intelligent user interfaces, problem solving and reasoning, planning, agents, text extraction, and machine learning. Knowledge acquisition has been a challenging area of research in AI, with its roots in early work to develop expert systems. Driven by the modern internet culture and knowledge-based industries, the study of knowledge capture has a renewed importance. Although there has been considerable work over the years in the area, activities have been distributed across several distinct research communities.

In this paper, the problem of maximizing a black-box function $f:\mathcal{X} \to \mathbb{R}$ is studied in the Bayesian framework with a Gaussian Process (GP) prior. In particular, a new algorithm for this problem is proposed, and high probability bounds on its simple and cumulative regret are established. The query point selection rule in most existing methods involves an exhaustive search over an increasingly fine sequence of uniform discretizations of $\mathcal{X}$. The proposed algorithm, in contrast, adaptively refines $\mathcal{X}$ which leads to a lower computational complexity, particularly when $\mathcal{X}$ is a subset of a high dimensional Euclidean space. In addition to the computational gains, sufficient conditions are identified under which the regret bounds of the new algorithm improve upon the known results. Finally an extension of the algorithm to the case of contextual bandits is proposed, and high probability bounds on the contextual regret are presented.

I. Introduction Two premises, reflected in the title, underlie the perspective from which I will consider research in natural language processing in this paper.* First, progress on building computer systems that process natural languages in any meaningful sense (i.e., systems that interact reasonably with people in natural language) requires considering language as part of a larger communicative situation. In this larger situation, the participants in a conversation and their states of mind are as important to the interpretation of an utterance as the linguistic expressions from which it is formed. A central concern when language is considered as communication is its function in building and using shared models of the world. Indeed, the notion of a shared model is inherent in the word "communicate," which is derived from the Latin communi Preparation of this paper was supported by the National Science Foundation under Grant No. MCS76-220004, and the Defense Advanced Research Projects Agency under Contract N00039-79C0118 with the Naval Electronic Systems Command.

What are the powerful new ideas in knowledge based design? What important research issues require further investigation? Perhaps the key research problem in AIbased design for the 1980's is to develop better models of the design process. A comprehensive model of design should address the following aspects of the design process: the state of the design; the goal structure of the design process; design decisions; rationales for design decisions; control of the design process; and the role of learning in design This article presents some of the most important ideas emerging from current AI research on design, especially ideas for better models of design It is organized into sections dealing with each of the aspects of design listed above What is design? Why should we study it?

The Fourth International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-03) took place at the Stanford University Center for the Study of Language and Information in Stanford, California, on 23 to 25 June 2003. Like the previous conferences, CONTEXT-03 fulfilled its aim of bringing together representatives of many different research areas, spanning the whole range of the cognitive and information sciences, and with interests ranging from the use of context in specific, commercial applications to highly general philosophical, psychological, and logical theories. The conference chair was Fausto Giunchiglia, University of Trento. The program chairs were Patrick Blackburn, INRIA Lorraine; Chiara Ghidini, the Centre for Scientific and Technological Research in Trento; and Roy Turner, University of Maine. There were 77 submissions, from which 31 papers and 14 posters were selected. One of the aims of the CONTEXT conferences is to bring together representatives of ...