Geoplotlib - is an open-source Python toolbox that serves to visualize geographical data. It's library supports the development of hardware-accelerated interactive visualizations and provides implementations of dot maps, kernel density estimation, spatial graphs, Voronoi tessellation, shapefiles and many other spatial visualizations.

Data Shapley: Equitable Valuation of Data for Machine Learning; Amirata Ghorbani, James Zou; As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this work, we develop a principled framework to address data valuation in the context of supervised machine learning. Given a learning algorithm trained on n data points to produce a predictor, we propose data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data Shapley value uniquely satisfies several natural properties of equitable data valuation.

Agent-based systems technology has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. This promise is particularly attractive for creating software that operates in environments that are distributed and open, such as the internet. Currently, the great majority of agent-based systems consist of a single agent. However, as the technology matures and addresses increasingly complex applications, the need for systems that consist of multiple agents that communicate in a peer-topeer fashion is becoming apparent. Central to the design and effective operation of such multiagent systems (MASs) are a core set of issues and research questions that have been studied over the years by the distributed AI community.

Search engines are among the most successful applications on the web today. So many search engines have been created that it is difficult for users to know where they are, how to use them, and what topics they best address. Metasearch engines reduce the user burden by dispatching queries to multiple search engines in parallel. Not too surprisingly then, the most successful applications on the web to date are search engines: tools that assist users in finding information on specific topics. The first decision requires reasoning about the available resources and the second about ranking the search engines.

The National Aeronautics and Space Administration (NASA) is being challenged to perform more frequent and intensive space-exploration missions at greatly reduced cost. Nowhere is this challenge more acute than among robotic planetary exploration missions that the Jet Propulsion Laboratory (JPL) conducts for NASA. This article describes recent and ongoing work on spacecraft autonomy and ground systems that builds on a legacy of existing success at JPL applying AI techniques to challenging computational problems in planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation. I research and technology development reached critical mass at the Jet Propulsion Laboratory (JPL) about five years ago. In the last three years, the effort has begun to bear fruit in the form of numerous JPL and National Aeronautics and Space Administration (NASA) applications of AI technology in the areas of planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation.

In this article, I describe several challenges facing the integration of two distinct lines of AI research: (1) decision-theoretic planning (DTP) and (2) multiagent systems. Both areas (especially the second) are attracting considerable interest, but work in multiagent systems often assumes either classical planning models or prespecified economic valuations on the part of the agents in question. By integrating models of DTP in multiagent systems research, more sophisticated multiagent planning scenarios can be accommodated, at the same time explaining precisely how agents determine their valuations for different sources or activities. I discuss several research challenges that emerge from this integration, involving the development of coordination protocols, the reasoning about lack of coordination, and the predicting of behavior in markets. I also briefly mention some opportunities afforded planning agents in multiagent settings and how these might be addressed.

Background and Overview The founding of a new College at Rutgers in 1969 became the occasion for building a strong Computer Scicncc presence in the University. Livingston College thus provided the home for the newly organized Department of Computer Science (DCS) and for the beginning of Computer Science research at Rutgers. 1 came to chair the depart,- ment after ten years at RCA Labs in Princeton, where 1 headed the Computer Theory Research group. My own work in the Labs concentrated mainly in Al. In the early 196Os, 1 became interested in problems of representation in problem solving, and in computer methods for building models and solving formation problems. As 1 continued working on these problems at, Rutgers, their central significance for Al became increasingly clear to me; so was their difficulty.

The solution is based on the replacement of standard modules of a program with more flexible computation elements that are called anytime algorithms (Dean and Boddy 1988; Horvitz 1989). In addition, the solution includes an offline compilation process and a run-time monitoring component that guarantee that the agent is performing the correct amount of thinking in a well-defined sense. Artificial agents must perform some real-time deliberation to solve such problems as path planning and task scheduling. An important aspect of intelligent behavior is the capability of agents to factor the cost of deliberation into the deliberation process. Two factors determine the cost of deliberation: (1) the resources consumed by the process, primarily computation time, and (2) constant change in the environment that might decrease the relevance of the outcome and, hence, reduce its value.

The system ensures a high standard of quality in customer service, airport safety, and use of stand resources. This article describes our experience in developing an AI system using standard off-the-shelf software components. Although there were some initial hitches when the new airport opened on 6 July 1998, operations quickly returned to normal within a week's time. Within a month, operational statistics surpassed those of the old airport--80 percent of all flights were on time or within 15 minutes of schedule, all passengers cleared immigration within 15 minutes, and average baggage waiting time was only 10 minutes. During the 1998 Christmas holiday, HKIA serviced about 100,000 passengers daily and maintained equally high service standards.

The Modified Antarctic Mapping Mission (MAMM) was conducted from September to November 2000 onboard RADARSAT. The mission plan consisted of more than 2400 synthetic aperture radar data acquisitions of Antarctica that achieved the scientific objectives and obeyed RADARSAT's resource and operational constraints. Mission planning is a time-and knowledge-intensive effort. It required over a workyear to manually develop a comparable plan for AMM-1, the precursor mission to MAMM. This article describes the design and use of the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks and enabled rapid generation of what-if scenarios for evaluating alternative mission designs.