Publications
Probabilistic coordination of heterogeneous teams from capability temporal logic specifications
April 1, 2022
Journal Article
Published in:
IEEE Robot. Autom. Lett., Vol. 7, No. 2, April 2022, pp. 1190-7.
Summary
This letter explores coordination of heterogeneous teams of agents from high-level specifications. We employ Capability Temporal Logic (CaTL) to express rich, temporal-spatial tasks that require cooperation between many agents with unique capabilities. CaTL specifies combinations of tasks, each with desired locations, duration, and set of capabilities, freeing the user from considering specific agent trajectories and their impact on multi-agent cooperation. CaTL also provides a quantitative robustness metric of satisfaction based on availability of required capabilities for each task. The novelty of this letter focuses on satisfaction of CaTL formulas under probabilistic conditions. Specifically, we consider uncertainties in robot motion (e.g., agents may fail to transition between regions with some probability) and local probabilistic workspace properties (e.g., if there are not enough agents of a required capability to complete a collaborative task). The proposed approach automatically formulates amixed-integer linear program given agents, their dynamics and capabilities, an abstraction of the workspace, and a CaTL formula. In addition to satisfying the given CaTL formula, the optimization considers the following secondary goals (in decreasing order of priority): 1) minimize the risk of transition failure due to uncertainties; 2) maximize probabilities of regional collaborative satisfaction (if there is an excess of agents); 3) maximize the availability robustness of CaTL for potential agent attrition; 4) minimize the total agent travel time. We evaluate the performance of the proposed framework and demonstrate its scalability via numerical simulations.
Summary
This letter explores coordination of heterogeneous teams of agents from high-level specifications. We employ Capability Temporal Logic (CaTL) to express rich, temporal-spatial tasks that require cooperation between many agents with unique capabilities. CaTL specifies combinations of tasks, each with desired locations, duration, and set of capabilities, freeing the user from...
READ MORE
Fast decomposition of temporal logic specifications for heterogeneous teams
April 1, 2022
Journal Article
Published in:
IEEE Robot. Autom. Lett., Vol. 7, No. 2, April 2022, pp. 2297-2304.
Summary
We focus on decomposing large multi-agent path planning problems with global temporal logic goals (common to all agents) into smaller sub-problems that can be solved and executed independently. Crucially, the sub-problems' solutions must jointly satisfy the common global mission specification. The agents' missions are given as Capability Temporal Logic (CaTL) formulas, a fragment of Signal Temporal Logic (STL) that can express properties over tasks involving multiple agent capabilities (i.e., different combinations of sensors, effectors, and dynamics) under strict timing constraints. We jointly decompose both the temporal logic specification and the team of agents, using a satisfiability modulo theories (SMT) approach and heuristics for handling temporal operators. The output of the SMT is then distributed to subteams and leads to a significant speed up in planning time compared to planning for the entire team and specification. We include computational results to evaluate the efficiency of our solution, as well as the trade-offs introduced by the conservative nature of the SMT encoding and heuristics.
Summary
We focus on decomposing large multi-agent path planning problems with global temporal logic goals (common to all agents) into smaller sub-problems that can be solved and executed independently. Crucially, the sub-problems' solutions must jointly satisfy the common global mission specification. The agents' missions are given as Capability Temporal Logic (CaTL)...
READ MORE
Tools and practices for responsible AI engineering
January 14, 2022
Journal Article
Published in:
arXiv, 14 January 2022.
Summary
Responsible Artificial Intelligence (AI)—the practice of developing, evaluating, and maintaining accurate AI systems that also exhibit essential properties such as robustness and explainability—represents a multifaceted challenge that often stretches standard machine learning tooling, frameworks, and testing methods beyond their limits. In this paper, we present two new software libraries—hydra-zen and the rAI-toolbox—that address critical needs for responsible AI engineering. hydra-zen dramatically simplifies the process of making complex AI applications configurable, and their behaviors reproducible. The rAI-toolbox is designed to enable methods for evaluating and enhancing the robustness of AI-models in a way that is scalable and that composes naturally with other popular ML frameworks. We describe the design principles and methodologies that make these tools effective, including the use of property-based testing to bolster the reliability of the tools themselves. Finally, we demonstrate the composability and flexibility of the tools by showing how various use cases from adversarial robustness and explainable AI can be concisely implemented with familiar APIs.
Summary
Responsible Artificial Intelligence (AI)—the practice of developing, evaluating, and maintaining accurate AI systems that also exhibit essential properties such as robustness and explainability—represents a multifaceted challenge that often stretches standard machine learning tooling, frameworks, and testing methods beyond their limits. In this paper, we present two new software libraries—hydra-zen and...
READ MORE
Selective network discovery via deep reinforcement learning on embedded spaces
December 1, 2021
Conference Paper
Published in:
Appl. Netw. Sci., Vol. 6, No.1, December 2021, Art. No. 24.
Summary
Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream learning tasks given resource collection constraints are of great interest. In this paper, we formulate the task-specific network discovery problem as a sequential decision-making problem. Our downstream task is selective harvesting, the optimal collection of vertices with a particular attribute. We propose a framework, called network actor critic (NAC), which learns a policy and notion of future reward in an offline setting via a deep reinforcement learning algorithm. The NAC paradigm utilizes a task-specific network embedding to reduce the state space complexity. A detailed comparative analysis of popular network embeddings is presented with respect to their role in supporting offline planning. Furthermore, a quantitative study is presented on various synthetic and real benchmarks using NAC and several baselines. We show that offline models of reward and network discovery policies lead to significantly improved performance when compared to competitive online discovery algorithms. Finally, we outline learning regimes where planning is critical in addressing sparse and changing reward signals.
Summary
Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream...
READ MORE
Scalable and Robust Algorithms for Task-Based Coordination From High-Level Specifications (ScRATCHeS)
November 2, 2021
Journal Article
Published in:
IEEE Trans. Robot., early access, 2021.
Summary
Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We define a specification language, capability temporal logic, to describe rich, temporal properties involving tasks requiring the participation of multiple agents with multiple capabilities, e.g., sensors or end effectors. Arbitrary missions and team dynamics are jointly encoded as constraints in a mixed integer linear program, and solved efficiently using commercial off-the-shelf solvers. ScRATCHeS optionally allows optimization for maximal robustness to agent attrition at the penalty of increased computation time.We include an online replanning algorithm that adjusts the plan after an agent has dropped out. The flexible specification language, fast solution time, and optional robustness of ScRATCHeS provide a first step toward a multipurpose on-the-fly planning tool for tasking large teams of agents with multiple capabilities enacting missions with multiple tasks. We present randomized computational experiments to characterize scalability and hardware demonstrations to illustrate the applicability of our methods.
Summary
Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We...
READ MORE
Development of a field artifical intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds
June 1, 2021
Journal Article
Published in:
J. Trauma Acute Care Surg., Vol. 90, No. 6, 1 June 2021.
Topic:
R&D area:
Summary
BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome these limitations by accurately and confidently making predictions to support in-field triage in the first hours after traumatic injury. METHODS: Using an American College of Surgeons Trauma Quality Improvement Program-derived database of truncal and junctional gunshot wound (GSW) patients (aged 1~0 years), we trained an information-aware Dirichlet deep neural network (field artificial intelligence triage). Using supervised training, field artificial intelligence triage was trained to predict shock and the need for major hemorrhage control procedures or early massive transfusion (MT) using GSW anatomical locations, vital signs, and patient information available in the field. In parallel, a confidence model was developed to predict the true-dass probability ( scale of 0-1 ), indicating the likelihood that the prediction made was correct, based on the values and interconnectivity of input variables.
Summary
BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome these limitations by accurately and confidently making predictions to support in-field triage in...
READ MORE
Principles for evaluation of AI/ML model performance and robustness, revision 1
March 30, 2021
Project Report
Published in:
MIT Lincoln Laboratory Report MD-62
Summary
The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs. While there are numerous AI/ML successes in the academic and commercial sectors, many of these systems have also been shown to be brittle and nonrobust. In a complex and ever-changing national security environment, it is vital that the DoD establish a sound and methodical process to evaluate the performance and robustness of AI/ML models before these new capabilities are deployed to the field. Without an effective evaluation process, the DoD may deploy AI/ML models that are assumed to be effective given limited evaluation metrics but actually have poor performance and robustness on operational data. Poor evaluation practices lead to loss of trust in AI/ML systems by model operators and more frequent--often costly--design updates needed to address the evolving security environment. In contrast, an effective evaluation process can drive the design of more resilient capabilities, ag potential limitations of models before they are deployed, and build operator trust in AI/ML systems. This paper reviews the AI/ML development process, highlights common best practices for AI/ML model evaluation, and makes the following recommendations to DoD evaluators to ensure the deployment of robust AI/ML capabilities for national security needs: -Develop testing datasets with sufficient variation and number of samples to effectively measure the expected performance of the AI/ML model on future (unseen) data once deployed, -Maintain separation between data used for design and evaluation (i.e., the test data is not used to design the AI/ML model or train its parameters) in order to ensure an honest and unbiased assessment of the model's capability, -Evaluate performance given small perturbations and corruptions to data inputs to assess the smoothness of the AI/ML model and identify potential vulnerabilities, and -Evaluate performance on samples from data distributions that are shifted from the assumed distribution that was used to design the AI/ML model to assess how the model may perform on operational data that may differ from the training data. By following the recommendations for evaluation presented in this paper, the DoD can fully take advantage of the AI/ML revolution, delivering robust capabilities that maintain operational feasibility over longer periods of time, and increase warfighter confidence in AI/ML systems.
Summary
The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs. While there are numerous AI/ML successes in the academic and commercial sectors, many of these systems have also been shown to...
READ MORE
Multimodal representation learning via maximization of local mutual information [e-print]
March 7, 2021
Journal Article
Published in:
Intl. Conf. on Medical Image Computing and Computer Assisted Intervention, MICCAI, 27 September-1 October 2021.
Topic:
R&D area:
Summary
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image. Our method learns image and text encoders by encouraging the resulting representations to exhibit high local mutual information. We make use of recent advances in mutual information estimation with neural network discriminators. We argue that, typically, the sum of local mutual information is a lower bound on the global mutual information. Our experimental results in the downstream image classification tasks demonstrate the advantages of using local features for image-text representation learning.
Summary
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image...
READ MORE
Learning emergent discrete message communication for cooperative reinforcement learning
February 24, 2021
Conference Paper
Published in:
37th Conf. on Uncertainty in Artificial Intelligence, UAI 2021, early access, 26-30 July 2021.
Topic:
R&D area:
Summary
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase the interpretability for human designers and other agents. This paper proposes a method to generate discrete messages analogous to human languages, and achieve communication by a broadcast-and-listen mechanism based on self-attention. We show that discrete message communication has performance comparable to continuous message communication but with much a much smaller vocabulary size. Furthermore, we propose an approach that allows humans to interactively send discrete messages to agents.
Summary
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase...
READ MORE
Information Aware max-norm Dirichlet networks for predictive uncertainty estimation
January 1, 2021
Journal Article
Published in:
Neural Netw., Vol. 135, 2021, pp. 105–114.
Topic:
R&D group:
Summary
Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone to over-confident predictions. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction confidence, we propose a novel method, Information Aware Dirichlet networks, that learn an explicit Dirichlet prior distribution on predictive distributions by minimizing a bound on the expected max norm of the prediction error and penalizing information associated with incorrect outcomes. Properties of the new cost function are derived to indicate how improved uncertainty estimation is achieved. Experiments using real datasets show that our technique outperforms, by a large margin, state-of-the-art neural networks for estimating within-distribution and out-of-distribution uncertainty, and detecting adversarial examples.
Summary
Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone to over-confident predictions. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction confidence, we propose a...
READ MORE