Collaborating Authors

Buying Time for Resource-Bounded Planning

AAAI Conferences

Achieving safe, fully-automated control of a dynamic system requires fast, accurate responses to maintain safety while also driving the system toward its objectives. Two basic approaches to this problem include using prebuilt reactive plans and employing online planning. Ideally, a comprehensive set of plans could be built and scheduled offline, allowing the system to precompute and guarantee its response time to any runtime situation. However, a prebuilt set of reactive plans may not provide appropriate responses to all possible situations, particularly for complex problems in which domain knowledge may be either incomplete or imprecise. Conversely, online planning may be used to search for appropriate reactions to situations as they arise, but online deliberation must be bounded such that it terminates before the available resource limits are exceeded.

A Sampling Approach for Proactive Project Scheduling under Generalized Time-dependent Workability Uncertainty

Journal of Artificial Intelligence Research

In real-world project scheduling applications, activity durations are often uncertain. Proactive scheduling can effectively cope with the duration uncertainties, by generating robust baseline solutions according to a priori stochastic knowledge. However, most of the existing proactive approaches assume that the duration uncertainty of an activity is not related to its scheduled start time, which may not hold in many real-world scenarios. In this paper, we relax this assumption by allowing the duration uncertainty to be time-dependent, which is caused by the uncertainty of whether the activity can be executed on each time slot. We propose a stochastic optimization model to find an optimal Partial-order Schedule (POS) that minimizes the expected makespan. This model can cover both the time-dependent uncertainty studied in this paper and the traditional time-independent duration uncertainty. To circumvent the underlying complexity in evaluating a given solution, we approximate the stochastic optimization model based on Sample Average Approximation (SAA). Finally, we design two efficient branch-and-bound algorithms to solve the NP-hard SAA problem. Empirical evaluation confirms that our approach can generate high-quality proactive solutions for a variety of uncertainty distributions.

Time-Dependent Simple Temporal Networks: Properties and Algorithms

AAAI Conferences

Simple Temporal Networks (STNs) allow minimum and maximum distance constraints between time-points to be represented. They are often used when tackling planning and scheduling problems that involve temporal aspects. This paper is a summary of the journal article "Time-dependent Simple Temporal Networks: Properties and Algorithms" published in RAIRO - Operations Research. This journal article introduces an extension of STN called Time-dependent STN (TSTN), which covers temporal constraints for which the temporal distance required between two time-points is not necessarily constant. Such constraints are useful to model time-dependent scheduling problems, in which the duration of an activity may depend on its starting time. The paper introduces the TSTN framework, its properties, resolution techniques, as well as examples of applications.

Time-Dependent Utility and Action Under Uncertainty Artificial Intelligence

We discuss representing and reasoning with knowledge about the time-dependent utility of an agent's actions. Time-dependent utility plays a crucial role in the interaction between computation and action under bounded resources. We present a semantics for time-dependent utility and describe the use of time-dependent information in decision contexts. We illustrate our discussion with examples of time-pressured reasoning in Protos, a system constructed to explore the ideal control of inference by reasoners with limit abilities.

Siamese Survival Analysis with Competing Risks Machine Learning

Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiability, or inability to estimate true cause-specific survival curves from empirical data, further complicates competing risk survival analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep learning architecture for estimating personalized risk scores in the presence of competing risks. SSPN circumvents the nonidentifiability problem by avoiding the estimation of cause-specific survival curves and instead determines pairwise concordant time-dependent risks, where longer event times are assigned lower risks. Furthermore, SSPN is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics which are unable to capture the unique requirements of survival analysis with competing risks.