Publications

Refine Results

(Filters Applied) Clear All

By

Human-machine collaborative optimization via apprenticeship scheduling

September 1, 2018
  |
Journal Article
Author:
Matthew C. Gombolay
…
Published in:
J. Artif. Intell. Res., Vol. 63, 2018, pp. 1-49.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes. We propose a new approach for capturing this decision-making process through counterfactual reasoning in pairwise comparisons. Our approach is model-free and does not require iterating through the state space. We demonstrate that this approach accurately learns multifaceted heuristics on a synthetic and real world data sets. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of schedule optimization. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates optimal solutions up to 9.5 times faster than a state-of-the-art optimization algorithm.
READ LESS

Summary

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale...

READ MORE
Human-machine collaborative optimization via apprenticeship scheduling

Learning to tutor from expert demonstrators via apprenticeship scheduling

February 4, 2017
  |
Conference Paper
Author:
Matthew C. Gombolay
…
Published in:
AAAI-17 Workshop on Human-Machine Collaborative Learning, 4 February 2017.
Topic:
machine learning
R&D area:
R&D group:

Summary

We have conducted a study investigating the use of automated tutors for educating players in the context of serious gaming (i.e., game designed as a professional training tool). Historically, researchers and practitioners have developed automated tutors through a process of manually codifying domain knowledge and translating that into a human-interpretable format. This process is laborious and leaves much to be desired. Instead, we seek to apply novel machine learning techniques to, first, learn a model from domain experts' demonstrations how to solve such problems, and, second, use this model to teach novices how to think like experts. In this work, we present a study comparing the performance of an automated and a traditional, manually-constructed tutor. To our knowledge, this is the first investigation using learning from demonstration techniques to learn from experts and use that knowledge to teach novices.
READ LESS

Summary

We have conducted a study investigating the use of automated tutors for educating players in the context of serious gaming (i.e., game designed as a professional training tool). Historically, researchers and practitioners have developed automated tutors through a process of manually codifying domain knowledge and translating that into a human-interpretable...

READ MORE
Learning to tutor from expert demonstrators via apprenticeship scheduling

Apprenticeship scheduling: learning to schedule from human experts

July 9, 2016
  |
Conference Paper
Author:
Matthew C. Gombolay
…
Published in:
Proc. of the Int. Joint Conf. Artificial Intelligence (IJCAI), 9-15 July 2016.
Topic:
machine learning
R&D area:
R&D group:

Summary

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state-space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on both a synthetic data set incorporating job-shop scheduling and vehicle routing problems and a real-world data set consisting of demonstrations of experts solving a weapon-to-target assignment problem.
READ LESS

Summary

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale...

READ MORE
Apprenticeship scheduling: learning to schedule from human experts

Dwell scheduling algorithms for phased array antenna

January 1, 2013
  |
Journal Article
Author:
Zoran Spasojevic
…
Published in:
IEEE Trans. Aerosp. Electron. Syst., Vol. 49, No. 1, January 2013, pp. 42-54.
Topic:
radar
R&D area:
R&D group:

Summary

In a multifunctional radar performing searching and tracking operations, the maximum number of targets that can be managed is an important measure of performance. One way a radar can maximize tracking performance is to optimize its dwell scheduling. The problem of designing efficient dwell scheduling algorithms for various tracking and searching scenarios with respect to various objective functions has been considered many times in the past and many solutions have been proposed. We consider the dwell scheduling problem for two different scenarios where the only objective is to maximize the number of dwells scheduled during a scheduling period. We formulate the problem as a distributed and a nondistributed bin packing problem and present optimal solutions using an integer programming formulation. Obtaining an optimal solution gives the limit of radar performance. We also present a more computationally friendly but less optimal solution using a greedy approach.
READ LESS

Summary

In a multifunctional radar performing searching and tracking operations, the maximum number of targets that can be managed is an important measure of performance. One way a radar can maximize tracking performance is to optimize its dwell scheduling. The problem of designing efficient dwell scheduling algorithms for various tracking and...

READ MORE
Dwell scheduling algorithms for phased array antenna
1-4 of 4