A Trust-Based Multiagent System

Seymour, R. S.; Peterson, Gilbert L.

doi:10.1109/cse.2009.297

Cited by 13 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such scalability is critically needed because I-POMDPs cover an important portion of the multiagent planning problem space (Doshi, 2012;Durfee & Zilberstein, 2013). Applications in diverse areas such as security (Seymour & Peterson, 2009;Ng, Meyers, Boakye, & Nitao, 2010), robotics (Woodward & Wood, 2012;Wang, 2013), ad hoc teams (Chandrasekaran, Doshi, Zeng, & Chen, 2014) and human behavior modeling (Doshi, Qu, Goodie, & Young, 2010;Wunder, Kaisers, Yaros, & Littman, 2011;Hula, Montague, & Dayan, 2015) testify to the wide appeal of I-POMDPs while motivating better scalability.…”

Section: Introductionmentioning

confidence: 99%

Decision-Theoretic Planning Under Anonymity in Agent Populations

Sonu¹,

Chen²,

Doshi³

2017

jair

View full text Add to dashboard Cite

We study the problem of self-interested planning under uncertainty in settings shared with more than a thousand other agents, each of which plans at its own individual level. We refer to such large numbers of agents as an agent population. The decision-theoretic formalism of interactive partially observable Markov decision process (I-POMDP) is used to model the agent's self-interested planning. The first contribution of this article is a method for drastically scaling the finitely-nested I-POMDP to certain agent populations for the first time. Our method exploits two types of structure that is often exhibited by agent populations -- anonymity and context-specific independence. We present a variant called the many-agent I-POMDP that models both these types of structure to plan efficiently under uncertainty in multiagent settings. In particular, the complexity of the belief update and solution in the many-agent I-POMDP is polynomial in the number of agents compared with the exponential growth that challenges the original framework. While exploiting structure helps mitigate the curse of many agents, the well-known curse of history that afflicts I-POMDPs continues to challenge scalability in terms of the planning horizon. The second contribution of this article is an application of the branch-and-bound scheme to reduce the exponential growth of the search tree for look ahead. For this, we introduce new fast-computing upper and lower bounds for the exact value function of the many-agent I-POMDP. This speeds up the look-ahead computations without trading off optimality, and reduces both memory and run time complexity. The third contribution is a comprehensive empirical evaluation of the methods on three new problems domains -- policing large protests, controlling traffic congestion at a busy intersection, and improving the AI for the popular Clash of Clans multiplayer game. We demonstrate the feasibility of exact self-interested planning in these large problems, and that our methods for speeding up the planning are effective. Altogether, these contributions represent a principled and significant advance toward moving self-interested planning under uncertainty to real-world applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Decision-Theoretic Planning Under Anonymity in Agent Populations

Sonu¹,

Chen²,

Doshi³

2017

jair

View full text Add to dashboard Cite

show abstract

“…An extension of HMMs, partially observable Markov decision processes (POMDPs), provide a framework that does account for these actions and enables the design and synthesis of a policy to choose optimal actions based on a desired reward function. POMDPs have been used in HMI for automatically generating robot explanations to improve performance [59] and estimating trust in agent-agent interactions [52]. Recent work has demonstrated the use of a POMDP model with human trust dynamics to improve human-robot performance [3][4][5]11].…”

Section: Related Workmentioning

confidence: 99%

Toward Adaptive Trust Calibration for Level 2 Driving Automation

Akash

Jain

Misu

2020

Proceedings of the 2020 International Conference on Multimodal Interaction

View full text Add to dashboard Cite

show abstract

“…Recent work has demonstrated the use of a POMDP model with human trust dynamics to improve human-robot performance [52]. POMDPs have also been used in HMI for automatically generating robot explanations to improve performance [28]- [30] and estimating trust in agent-agent interactions [53]. For example, the POMDP model in [28]- [30] is used to simulate only the dynamics of the robot's decisions and generates recommendations of different transparency levels.…”

Section: Modeling Human Trust and Workloadmentioning

confidence: 99%

Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactions

Akash,

McMahon,

Reid

et al. 2020

Preprint

View full text Add to dashboard Cite

Human trust in automation plays an essential role in interactions between humans and automation. While a lack of trust can lead to a human's disuse of automation, over-trust can result in a human trusting a faulty autonomous system which could have negative consequences for the human. Therefore, human trust should be calibrated to optimize human-machine interactions with respect to context-specific performance objectives. In this article, we present a probabilistic framework to model and calibrate a human's trust and workload dynamics during his/her interaction with an intelligent decision-aid system. This calibration is achieved by varying the automation's transparency-the amount and utility of information provided to the human. The parameterization of the model is conducted using behavioral data collected through human-subject experiments, and three feedback control policies are experimentally validated and compared against a non-adaptive decision-aid system. The results show that human-automation team performance can be optimized when the transparency is dynamically updated based on the proposed control policy. This framework is a first step toward widespread design and implementation of real-time adaptive automation for use in human-machine interactions.

show abstract

A Trust-Based Multiagent System

Cited by 13 publications

References 9 publications

Decision-Theoretic Planning Under Anonymity in Agent Populations

Decision-Theoretic Planning Under Anonymity in Agent Populations

Toward Adaptive Trust Calibration for Level 2 Driving Automation

Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactions

Contact Info

Product

Resources

About