Decision Making Under Uncertainty

Kochenderfer, Mykel J.; Amato, Christopher; Chowdhary, Girish; How, Jonathan P.; Reynolds, Hayley J. Davison; Thornton, Jason R.; Torres-Carrasquillo, Pedro A.; Üre, N. Kemal; Vian, John

doi:10.7551/mitpress/10187.001.0001

Cited by 373 publications

(247 citation statements)

References 2 publications

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“…Decision-making using noisy sensory information is a well-studied topic (Kochenderfer, 2015; Chittka et al, 2009; Kepecs et al, 2008). However, even in the absence of noise, cognitive or sensory limitations filter those aspects of the environment that are perceived by an organism (Von Uexkull, 1957).…”

Section: Introductionmentioning

confidence: 99%

A locally-blazed ant trail achieves efficient collective navigation despite limited information

Fonio

Heyman

Boczkowski

et al. 2016

eLife

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Any organism faces sensory and cognitive limitations which may result in maladaptive decisions. Such limitations are prominent in the context of groups where the relevant information at the individual level may not coincide with collective requirements. Here, we study the navigational decisions exhibited by Paratrechina longicornis ants as they cooperatively transport a large food item. These decisions hinge on the perception of individuals which often restricts them from providing the group with reliable directional information. We find that, to achieve efficient navigation despite partial and even misleading information, these ants employ a locally-blazed trail. This trail significantly deviates from the classical notion of an ant trail: First, instead of systematically marking the full path, ants mark short segments originating at the load. Second, the carrying team constantly loses the guiding trail. We experimentally and theoretically show that the locally-blazed trail optimally and robustly exploits useful knowledge while avoiding the pitfalls of misleading information.DOI: http://dx.doi.org/10.7554/eLife.20185.001

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Section: Introductionmentioning

confidence: 99%

A locally-blazed ant trail achieves efficient collective navigation despite limited information

Fonio

Heyman

Boczkowski

et al. 2016

eLife

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“…Markov decision processes (MDPs) are a framework for formulating sequential decision making problems [11]. In an MDP an agent chooses an action a in state s. The agent receives a reward according to the reward function R(s, a).…”

Section: A Markov Decision Processmentioning

confidence: 99%

Efficient Autonomy Validation in Simulation with Adaptive Stress Testing

Koren

Kochenderfer

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure. Adaptive Stress Testing (AST) provides a way to search for the mostlikely failure scenario as a Markov decision process (MDP). Our previous work used a deep reinforcement learning (DRL) solver to identify likely failure scenarios. However, the solver's use of a feed-forward neural network with a discretized space of possible initial conditions poses two major problems. First, the system is not treated as a black box, in that it requires analyzing the internal state of the system, which leads to considerable implementation complexities. Second, in order to simulate realistic settings, a new instance of the solver needs to be run for each initial condition. Running a new solver for each initial condition not only significantly increases the computational complexity, but also disregards the underlying relationship between similar initial conditions. We provide a solution to both problems by employing a recurrent neural network that takes a set of initial conditions from a continuous space as input. This approach enables robust and efficient detection of failures because the solution generalizes across the entire space of initial conditions. By simulating an instance where an autonomous car drives while a pedestrian is crossing a road, we demonstrate the solver is now capable of finding solutions for problems that would have previously been intractable.

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“…In general, computing the exact optimal utility function for a POMDP is intractable and must rely on approximation techniques instead. Two approaches are ued to compute the optimal utility function: offline and online methods [10]. In this paper, we use an offline QMDP [11] approach to compute the optimal policy.…”

Section: A Backgroundmentioning

confidence: 99%

Pedestrian Collision Avoidance System for Scenarios with Occlusions

Schraner

Bouton

Kochenderfer

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with other traffic participants with limited perception ability. Current deployed approaches relying on Autonomous Emergency Braking (AEB) systems are often overly conservative. In this work, we formulate the problem as a partially observable Markov decision process (POMDP), to derive a policy robust to uncertainty in the pedestrian location. We investigate how to integrate such a policy with an AEB system that operates only when a collision is unavoidable. In addition, we propose a rigorous evaluation methodology on a set of well defined scenarios. We show that combining the two approaches provides a robust autonomous braking system that reduces unnecessary braking caused by using the AEB system on its own.

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Decision Making Under Uncertainty

Cited by 373 publications

References 2 publications

A locally-blazed ant trail achieves efficient collective navigation despite limited information

A locally-blazed ant trail achieves efficient collective navigation despite limited information

Efficient Autonomy Validation in Simulation with Adaptive Stress Testing

Pedestrian Collision Avoidance System for Scenarios with Occlusions

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