There's More to Life Than Making Plans For many years, research in AI plan generation was governed by a number of strong, simplifying assumptions: The planning agent is omniscient, its actions are deterministic and instantaneous, its goals are fixed and categorical, and its environment is static. More recently, researchers have developed expanded planning algorithms that are not predicated on such assumptions, but changing the way in which plans are formed is only part of what is required when the classical assumptions are abandoned. The demands of dynamic, uncertain environments mean that in addition to being able to form plans--even probabilistic, uncertain plans--agents must be able to effectively manage their plans. In this article, which is based on a talk given at the 1998 AAAI Fall Symposium on Distributed, Continual Planning, we first identify reasoning tasks that are involved in plan management, including commitment management, environment monitoring, alternative assessment, plan elaboration, metalevel control, and coordination with other agents. We next survey approaches we have developed to many of these tasks and discuss a plan-management system we are building to ground our theoretical work, by providing us with a platform for integrating our techniques and exploring their value in a realistic problem.

People often act together with a shared purpose; they collaborate. Collaboration enables them to work more efficiently and to complete activities they could not accomplish individually. An increasing number of computer applications also require collaboration among various systems and people. Thus, a major challenge for AI researchers is to determine how to construct computer systems that are able to act effectively as partners in collaborative activity. Collaborative activity entails participants forming commitments to achieve the goals of the group activity and requires group decision making and group planning procedures.

J. Sainsbury has extensive assets, with subsidiaries such as Shaws in the United States and the Savacentre and Homebase chains in the United Kingdom. Given J. Sainsbury's position in the retail market, the efficient and effective running of the supply chain for J. Sainsbury is critical to the mission of the organization. The J. Sainsbury logistics purpose statement is to manage the flow of goods from supplier to shelf, ensuring that the customer has the right product in the right place at the right time. To these ends, J. Sainsbury's Logistics Group is committed to being world class. The group's direction principle is to be seen as the world's best logistics team.

Other difficult tasks, more generally, are how to obtain a robust performance in teamworks (Cohen and Levesque 1990); how to prevent agents from dropping their commitments; or better, how to regulate agents dropping their commitments to a joint action to not disrupt the common activity and preclude the common goal being achieved (Jennings 1995; Singh 1995; Kinny and Georgeff 1991). These tasks have now entered the MAS field's common knowledge. Other problems are perhaps less obvious. The Second International Conference on Multiagent Systems (ICMAS '96) Workshop on Norms, Obligations, and Conventions was held in Kyoto, Japan, from 10 to 13 December 1996. Participants included scientists from deontic logic, database framework, decision theory, agent architecture, cognitive modeling, and legal expert systems.

A recurring requirement for human-level artificial intelligence is the incorporation of vast amounts of knowledge into a software agent that can use the knowledge in an efficient and organized fashion. This article discusses representations and processes for agents and behavior models that integrate large, diverse knowledge stores, are long-lived, and exhibit high degrees of competence and flexibility while interacting with complex environments. There are many different approaches to building such agents, and understanding the important commonalities and differences between approaches is often difficult. We introduce a new approach to comparing frameworks based on the notions of commitment, reconsideration, and a categorization of representations and processes. We review four agent frameworks, concentrating on the major representations and processes each directly supports.

The construction of computer systems that are intelligent, collaborative problem-solving partners is an important goal for both the science of AI and its application. From the scientific perspective, the development of theories and mechanisms to enable building collaborative systems presents exciting research challenges across AI subfields. From the applications perspective, the capability to collaborate with users and other systems is essential if large-scale information systems of the future are to assist users in finding the information they need and solving the problems they have. In this address, it is argued that collaboration must be designed into systems from the start; it cannot be patched on. Key features of collaborative activity are described, the scientific base provided by recent AI research is discussed, and several of the research challenges posed by collaboration are presented.

We describe an application of a dynamic replanning technique in a highly dynamic and complex domain: the military aeromedical evacuation of patients to medical treatment facilities. U.S. Transportation Command (USTRANSCOM) is the U.S. Department of Defense (DoD) agency responsible for evacuating patients during wartime and peace. Doctrinally, patients requiring extended treatment must be evacuated by air to a suitable medical treatment facility. The Persian Gulf War was the first significant armed conflict in which this concept was put to a serious test. The results were far from satisfactory--about 60 percent of the patients ended up at the wrong destinations.

This article introduces a generic framework for constraint-directed search. The research literature in constraint-directed scheduling is placed within the framework both to provide insight into, and examples of, the framework and to allow a new perspective on the scheduling literature. We show how a number of algorithms from the constraintdirected-scheduling research can be conceptualized within the framework. This conceptualization allows us to identify and compare variations of components of our framework and provides new perspective on open research issues. We discuss the prospects for an overall comparison of scheduling strategies and show that firm conclusions visa-vis such a comparison are not supported by the literature.

While tech superpowers Uber, Google, Tesla, and Lyft dominate the news cycles, traditional car manufacturers are running expensive Silicon Valley research centers, expanding self-driving fleets, forging partnerships, and plowing billions into acquisitions. With artificial intelligence (AI) playing an increasing role in AV technology, self-driving vehicles will inevitably make "wrong" choices when presented with options of who will live and who will die. Over time, more segments of society will need to contribute to these morally and legally challenging decisions. To manage the risk of a more destructive hack that could weaponize automated vehicles, significant advances in cybersecurity will be required, along with commitment from the entire connected automotive ecosystem to adopt state-of-the-art security technology.

Furthermore, in order to get their job done and fulfill their commitments, a typical employee must interact with multiple applications, each with its own learning curve. While enterprise apps make sense for tasks that are performed frequently or on a regular basis (like submitting your weekly time sheet or approving expenses), the valuable real estate they occupy on the already cluttered home screen makes less sense for the long-tail of tasks that are accessed less frequently. They have access to all the corporate information needed to get the job done and can perform complex tasks across multiple systems. Because bots rely on natural language processing (NLP) -- the ability of humans to interact with computers using free-form language -- workbots can help an employee get to the starting point quickly and without any training, in the same way a search engine would, and then help guide the user through the task in a step-by-step fashion.