People regularly use objects in the environment as tools to achieve their goals. In this paper we report extensions to the ICARUS cognitive architecture that let it create and use combinations of objects inthis manner. These extensions include the ability to represent virtual objects composed of simpler ones and to reason about their quantitative features. They also include revised modules for planning and execution that operate over this hybrid representation, taking into account both relational structures and numeric attributes. We demonstrate the extended architecture's behavior on a number of tasks that involve tool construction and use, after which we discuss related research and plans for future work.

This article describes the artificial intelligence (AI) divide, its social impact, and begins to prescribe policies to close this gap between those who benefit from AI data, algorithms, and hardware and those who are primarily exploited by them. Without a digitally aware, algorithm-literate public and an equitable public policy on AI, the AI divide will increasingly impact negatively those in lower socioeconomic classes in the U.S. and around the world.

As intelligent agents become more autonomous, sophisticated, and prevalent, it becomes increasingly important that humans interact with them effectively. Machine learning is now used regularly to acquire expertise, but common techniques produce opaque content whose behavior is difficult to interpret. Before they will be trusted by humans, autonomous agents must be able to explain their decisions and the reasoning that produced their choices. We will refer to this general ability as explainable agency. This capacity for explaining decisions is not an academic exercise. When a self-driving vehicle takes an unfamiliar turn, its passenger may desire to know its reasons. When a synthetic ally in a computer game blocks a player's path, he may want to understand its purpose. When an autonomous military robot has abandoned a high-priority goal to pursue another one, its commander may request justification. As robots, vehicles, and synthetic characters become more self-reliant, people will require that they explain their behaviors on demand. The more impressive these agents' abilities, the more essential that we be able to understand them.

In goal recognition, the basic problem domain consists of the following: Recent advances in robotics and artificial intelligence have brought a variety of assistive robots designed to help humans - a set E of environment fluents; accomplish their goals. However, many have limited autonomy and lack the ability to seamlessly integrate with - a state S that is a value assignment to those fluents; human teams. One capability that can facilitate such humanrobot - a set A of actions that describe potential transitions between teaming is the robot's ability to recognize its teammates' states (with preconditions and effects defined over goals, and react appropriately. This function permits E, and parameterized over a set of environment objects the robot to actively assist the team and avoid performing O); and redundant or counterproductive actions.

We introduce ActorSim, the Actor Simulator, a toolkit for studying situated autonomy. As background, we review three goal-reasoning projects implemented in ActorSim: one project that uses information metrics in foreign disaster relief and two projects that learn subgoal selection for sequential decision making in Minecraft. We then discuss how ActorSim can be used to address cross-disciplinary gaps in several ongoing projects. To varying degrees, the projects integrate concerns within distinct specializations of AI and between AI and other more human-focused disciplines. These areas include automated planning, learning, cognitive architectures, robotics, cognitive modeling, sociology, and psychology.

In this paper, we describe the approach of the Earth, Life and Semantic Web (ELSEWeb) project that facilitates the discovery and transformation of Earth observation data sources for the creation of species distribution models (data-to-model) transformations. ELSEWeb automates the discovery and processing of voluminous, heterogeneous satellite imagery and other geospatial data available at the Earth Data Analysis Center to be included in Lifemapper Species Distribution models by using AI knowledge representation and reasoning techniques developed by the Semantic Web community. The realization of the ELSEWeb semantic infrastructure provides the possibility of combinatoric explosions of scientific results, automatically generated by orchestrations of data mash-ups and service composition. We report on the key elements that contributed to the ELSEWeb project and the role of automated reasoning in streamlining the Species Distribution Model generation and execution.