This paper describes an approach to scheduling under uncertainty that achieves scalability through a coupling of deterministic and probabilistic reasoning. A class of oversubscribed scheduling problems is considered where the goal is to maximize the reward earned by a team of agents in a distributed execution environment. There is uncertainty in both the duration and outcomes of executed activities, and activities are subject to deadlines. To ensure scalability, the approach takes as its starting point an initial deterministic schedule for the agents, computed using expected duration reasoning. This initial agent schedule is probabilistically analyzed to find likely points of failure, and then selectively strengthened based on this analysis. Experimental results obtained in a multi-agent simulation environment demonstrate that coupling probabilistic and deterministic reasoning in this way results in significantly higher rewards than are achieved by relying on deterministic reasoning alone. In the future, the approach will be extended to include probability-driven meta-level management of execution.

We present an implementation of a plan adaptation system, BioPlanner, built for biological pathway prediction across species. BioPlanner formulates a pathway discovery problem as a Hierarchical Task Network (HTN) planning problem and solves it by adapting a plan solution of another well-studied pathway. BioPlanner provides the following functionalities: It automatically builds HTN planning models for a biological pathway domain from the semantic web biological knowledge bases (KBs). It retrieves plan cases from the biological KBs. It generates hypothetical pathways using plan adaptation strategies with the aid of biological domain knowledge. It evaluates the hypothetical plan candidates, ranks them, and recommends the most likely hypotheses to users. It employs an information gathering multi-agent system to capture knowledge from heterogeneous sources to help the hypothetical plan generation process. We utilize BioPlanner to predict Signaling Transduction pathways for Mus musculus, Gallus gallus, and Drosophila melanogaster from Homo sapiens.

In this work we aim to narrow the gap between plan recognition and planning by exploiting the power and generality of recent planning algorithms for recognizing the set G ∗ of goals G that explain a sequence of observations given a domain theory. After providing a crisp definition of this set, we show by means of a suitable problem transformation that a goal G belongs to G ∗ if there is an action sequence π that is an optimal plan for both the goal G and the goal G extended with extra goals representing the observations. Exploiting this result, we show how the set G ∗ can be computed exactly and approximately by minor modifications of existing optimal and suboptimal planning algorithms, and existing polynomial heuristics. Experiments over several domains show that the suboptimal planning algorithms and the polynomial heuristics provide good approximations of the optimal goal set G ∗ while scaling up as well as state-of-the-art planning algorithms and heuristics.

This paper presents a new algorithm for plan recognition called ELEXIR (Engine for LEXicalized Intent Recognition).  ELEXIR represents the plans to be recognized with a grammatical formalism called Combinatory Categorial Grammar(CCG).  We show that representing plans with CCGs can allow us to prevent early commitment to plan goals and thereby reduce runtime.

In this paper, we investigate the hypothesis that plan recognition can significantly improve the performance of a case-based reinforcement learner in an adversarial action selection task. Our environment is a simplification of an American football game. The performance task is to control the behavior of a quarterback in a pass play, where the goal is to maximize yardage gained. Plan recognition focuses on predicting the play of the defensive team. We modeled plan recognition as an unsupervised learning task, and conducted a lesion study. We found that plan recognition was accurate, and that it significantly improved performance. More generally, our studies show that plan recognition reduced the dimensionality of the state space, which allowed learning to be conducted more effectively. We describe the algorithms, explain the reasons for performance improvement, and also describe a further empirical comparison that highlights the utility of plan recognition for this task.

We propose a memory-based approach to the problem of goal-schema recognition. We use a generic episodic memory module to perform incremental goal schema recognition and to build the plan library. Unlike other case-based plan recognizers it does not require complete knowledge of the planning domain or the ability to record intermediate planning states. Similarity of plans is computed incrementally using a semantic matcher that considers the type and parameters of the observed actions.  We evaluate this approach on two datasets and show that it is able to achieve similar or better performance compared to a statistical approach, but offers important advantages: plan library is acquired incrementally and the memory structure it builds is multi-functional and can be used for other tasks such as plan generation or classification.

We describe the application of plan-recognition techniques to support human intelligence analysts in processing national security alerts. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application.

We describe the application of plan-recognition techniques to support human intelligence analysts in processing national security alerts. Our approach is designed to take the noisy results of traditional data-mining tools and exploit causal knowledge about attacks to relate activities and uncover the intent underlying them. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application. In particular, we outline the need to bring the analysts into the process and for heuristic improvements to the plan-recognition algorithm.

We are pleased to publish this special selection of articles from the Sixteenth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-04), which occurred July 27-29, 2004 in San Jose, California. IAAI is the premier venue for learning about AI's impact through deployed applications and emerging AI technologies. Case studies of deployed applications with measurable benefits arising from the use of AI technology provide clear evidence of the impact and value of AI technology to today's world. The emerging applications track features technologies that are rapidly maturing to the point of application. The seven articles selected for this special issue are extended versions of the papers that appeared at the conference. Four of the articles describe deployed applications that are already in use in the field. The other three articles, which are from the emerging technology track, were selected because they are particularly innovative and show great potential for deployment.

This article surveys previous work on combining planning techniques with expressive representations of knowledge in description logics to reason about tasks, plans, and goals. Description logics can reason about the logical definition of a class and automatically infer class-subclass subsumption relations as well as classify instances into classes based on their definitions. Descriptions of actions, plans, and goals can be exploited during plan generation, plan recognition, or plan evaluation. These techniques should be of interest to planning practitioners working on knowledge-rich application domains. Another emerging use of these techniques is the semantic web, where current ontology languages based on description logics need to be extended to reason about goals and capabilities for web services and agents.