We describe a system for specifying the effects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the effects of actions using domain rules, which are state constraints that can entail new action effects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of logical theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed.

The PROCESS SPECIFICATION language (PSL) has been designed to facilitate correct and complete exchange of process information among manufacturing systems, such as scheduling, process modeling, process planning, production planning, simulation, project management, work flow, and business-process reengineering. We give an overview of the theories within the PSL ontology, discuss some of the design principles for the ontology, and finish with examples of process specifications that are based on the ontology.

Recently, planning based on answer set programming has been proposed as an approach towards realizing declarative planning systems. In this paper, we present the language Kc, which extends the declarative planning language K by action costs. Kc provides the notion of admissible and optimal plans, which are plans whose overall action costs are within a given limit resp. minimum over all plans (i.e., cheapest plans). As we demonstrate, this novel language allows for expressing some nontrivial planning tasks in a declarative way. Furthermore, it can be utilized for representing planning problems under other optimality criteria, such as computing ``shortest'' plans (with the least number of steps), and refinement combinations of cheapest and fastest plans. We study complexity aspects of the language Kc and provide a transformation to logic programs, such that planning problems are solved via answer set programming. Furthermore, we report experimental results on selected problems. Our experience is encouraging that answer set planning may be a valuable approach to expressive planning systems in which intricate planning problems can be naturally specified and solved.

This article reports on an extensive survey and analysis of research work related to machine learning as it applies to automated planning over the past 30 years. Major research contributions are broadly characterized by learning method and then descriptive subcategories. Survey results reveal learning techniques that have extensively been applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community.

This article reports on an extensive survey and analysis of research work related to machine learning as it applies to automated planning over the past 30 years. Major research contributions are broadly characterized by learning method and then descriptive subcategories. Survey results reveal learning techniques that have extensively been applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community.

Unary operator domains -- i.e., domains in which operators have a single effect -- arise naturally in many control problems. In its most general form, the problem of STRIPS planning in unary operator domains is known to be as hard as the general STRIPS planning problem -- both are PSPACE-complete. However, unary operator domains induce a natural structure, called the domain's causal graph. This graph relates between the preconditions and effect of each domain operator. Causal graphs were exploited by Williams and Nayak in order to analyze plan generation for one of the controllers in NASA's Deep-Space One spacecraft. There, they utilized the fact that when this graph is acyclic, a serialization ordering over any subgoal can be obtained quickly. In this paper we conduct a comprehensive study of the relationship between the structure of a domain's causal graph and the complexity of planning in this domain. On the positive side, we show that a non-trivial polynomial time plan generation algorithm exists for domains whose causal graph induces a polytree with a constant bound on its node indegree. On the negative side, we show that even plan existence is hard when the graph is a directed-path singly connected DAG. More generally, we show that the number of paths in the causal graph is closely related to the complexity of planning in the associated domain. Finally we relate our results to the question of complexity of planning with serializable subgoals.

This article discusses our experience building and running an intelligent control system during a three-year period for a National Aeronautics and Space Administration advanced life support (ALS) system. The system under test was known as the Integrated Water-Recovery System (IWRS). We used the 3T intelligent control architecture to produce software that operated autonomously, 24 hours a day, 7 days a week, for 16 months. The article details our development approach, the successes and failures of the system, and our lessons learned. We conclude with a summary of spin-off benefits to the AI community and areas of AI research that can be useful for future ALS systems.

There is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents, both human and machine. We characterize the domain-independent challenges posed by this problem, and describe how properties of domains influence the challenges and their solutions. We will concentrate on dynamic, data-rich domains where humans are ultimately responsible for team behavior. Thus, the automated aid should interactively support effective and timely decision making by the human. We present a domain-independent categorization of the types of alerts a plan-based monitoring system might issue to a user, where each type generally requires different monitoring techniques. We describe a monitoring framework for integrating many domain-specific and task-specific monitoring techniques and then using the concept of value of an alert to avoid operator overload. We use this framework to describe an execution monitoring approach we have used to implement Execution Assistants (EAs) in two different dynamic, data-rich, real-world domains to assist a human in monitoring team behavior. One domain (Army small unit operations) has hundreds of mobile, geographically distributed agents, a combination of humans, robots, and vehicles. The other domain (teams of unmanned ground and air vehicles) has a handful of cooperating robots. Both domains involve unpredictable adversaries in the vicinity. Our approach customizes monitoring behavior for each specific task, plan, and situation, as well as for user preferences. Our EAs alert the human controller when reported events threaten plan execution or physically threaten team members. Alerts were generated in a timely manner without inundating the user with too many alerts (less than 10 percent of alerts are unwanted, as judged by domain experts).

The staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. This article describes DIRECTOR, a staff scheduling system for contact centers. DIRECTOR is a constraint-based system that uses AI search techniques to generate schedules that satisfy and optimize a wide range of constraints and service-quality metrics. DIRECTOR has successfully been deployed at more than 800 contact centers, with significant measurable benefits, some of which are documented in case studies included in this article.

The Fourteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2002) was held from 28 July to 1 August in Edmonton, Alberta, Canada, in conjunction with the Seventeenth National Conference on Artificial Intelligence (AAAI-2002). As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed application papers describe systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are technologies and systems that are close to deployment and clearly show an innovative implementation of AI technologies. These papers are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the unique technical challenges provided by real-world problems.