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Autonomously Generating Hints by Inferring Problem Solving Policies
2015
Proceedings of the Second (2015) ACM Conference on Learning @ Scale - L@S '15
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In this paper we autonomously generate hints for the Code.org 'Hour of Code,' (which is to the best of our knowledge the largest online course to date) using historical student data. ...
Such predictions can form the basis for effective hint generation systems. ...
Chris is supported by NSF-GRFP grant number DGE-114747. ...
doi:10.1145/2724660.2724668
dblp:conf/lats/PiechSHG15
fatcat:lhqhyalz6vg6hla3glwv2us22y
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
[article]
2022
arXiv
pre-print
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To address this problem, we propose Symphony, which greatly improves realism by combining conventional policies with a parallel beam search. ...
The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator. ...
INTRODUCTION Simulation is a crucial tool for accelerating the development of autonomous driving software because it can generate adversarial interactions for training autonomous driving policies, play ...
arXiv:2205.03195v1
fatcat:422oic2qjzcdxkh2224byebjhu
An architectural design and evaluation of an affective tutoring system for novice programmers
2018
International Journal of Educational Technology in Higher Education
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more effective tutoring as compared to the version with the affective function disabled and the students are positive on their learning experience with the ATS with the fill in the gap exercises and hints ...
Both quantitative and qualitative techniques were used for evaluation of the effectiveness of the ATS and its usability and acceptance by student participants. ...
The first instance when students request for hint, a generic hint relating to the topic would be displayed. For subsequent help requests, detailed hints would be provided to the students. ...
doi:10.1186/s41239-018-0121-2
fatcat:27lqzzixpbbz7e67xbzu7wqblu
Collaborative Driving: Learning- Aided Joint Topology Formulation and Beamforming
[article]
2022
arXiv
pre-print
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Currently, autonomous vehicles are able to drive more naturally based on the driving policies learned from millions of driving miles in real environments. ...
Finally, we discuss several potential open research problems for the proposed collaborative driving scheme. ...
Acknowledgments This work was supported by the National Key R&D Program of China (2019YFB1600100), National Natural Science Foundation of China (U1801266 and 62101401), the Youth Innovation Team of Shaanxi ...
arXiv:2203.09915v1
fatcat:p3iwevz4urc6dlr7n5iyahnziu
Inverse Reinforce Learning with Nonparametric Behavior Clustering
[article]
2017
arXiv
pre-print
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Further, to improve the computation efficiency, we remove the need of completely solving multiple IRL problems for multiple clusters during the iteration steps and introduce a resampling technique to avoid ...
from autonomous robot cars using the Gazebo robot simulator. ...
Let T be the set of trajectories generated by the Markov chain M π where π is the maximum entropy policy, probability of a trajectory ζ under this policy is given by, P (ζ|θ) = exp(r θ (ζ)) τ ∈T exp(r ...
arXiv:1712.05514v1
fatcat:rvdvcwnoojf33px47amedqta7q
A Formal Framework for Trust Policy Negotiation in Autonomic Systems: Abduction with Soft Constraints
[chapter]
2010
Lecture Notes in Computer Science
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As a running application example throughout the paper, we reason with access control policies and credentials. ...
In this way, we can associate the level of preference defined by the "softness" of the constraint with a "level" of trust. ...
This problem is of interest when performing type inference involving generalized algebraic data types. ...
doi:10.1007/978-3-642-16576-4_20
fatcat:plikin6mbbc7vnieeou77kyxua
Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning
2018
Robotics and Autonomous Systems
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complex control problems for autonomous systems. ...
Low-level control of autonomous underwater vehicles (AUVs) has been extensively addressed by classical control techniques. ...
In addition, an actor-critic goal-oriented architecture was developed to aid the deep agent to achieve a more generalized policy and therefore solve a bigger range of dynamic problems. ...
doi:10.1016/j.robot.2018.05.016
fatcat:jtlf3pofpbgj5p32rpn6tevary
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints
[article]
2022
arXiv
pre-print
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Robotic navigation has been approached as a problem of 3D reconstruction and planning, as well as an end-to-end learning problem. ...
These models are used by a heuristic planner to identify the best waypoint in order to reach the final destination. ...
ACKNOWLEDGMENTS This research was partially supported by DARPA Assured Autonomy, ARL DCIST CRA W911NF-17-2-0181, and DARPA RACER. ...
arXiv:2202.11271v2
fatcat:a3e3mi6ffzgdpdmsu3ss5x2blu
A globally optimal algorithm for TTD-MDPs
2007
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems - AAMAS '07
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We improve on the existing algorithm for solving TTD-MDPs by deriving a greedy algorithm that finds a policy that provably minimizes the global KL-divergence from the target distribution. ...
the use of Targeted Trajectory Distribution Markov Decision Processes (TTD-MDPs)-a variant of MDPs in which the goal is to realize a specified distribution of trajectories through a state space-as a general ...
ORISE is managed by Oak Ridge Associated Universities under DOE contract number DE-AC05-06OR23100. ...
doi:10.1145/1329125.1329367
dblp:conf/atal/BhatRNIM07
fatcat:frohevtkxbbupa67tdv45b2mri
What does my knowing your plans tell me?
[article]
2018
arXiv
pre-print
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Privacy constraints are specified as the stipulations on what can be inferred during plan execution. ...
The divulged plan, which can be represented by a procrustean graph, is shown to undermine privacy precisely to the extent that it can eliminate action-observation sequences that will never appear in the ...
ACKNOWLEDGEMENTS This work was supported by the NSF through awards IIS-1453652, IIS-1527436, and IIS-1526862. We thank the anonymous reviewers for their time and valuable comments. ...
arXiv:1810.03873v1
fatcat:qxy2ko6fzbalpadr2hcvaal5ba
Towards Teachable Autotelic Agents
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2105.11977v1 fatcat:kct7sh64fzhcvc6v25asyxfgwa
It also shows the way forward by highlighting key research directions towards the design or autonomous agents that can be taught by ordinary people via natural pedagogy. ...
In the field of Artificial Intelligence, these extremes respectively map to autonomous agents learning from their own signals and interactive learning agents fully taught by their teachers. ...
Teachable versus Autonomous Agents Reinforcement learning (RL) is a process by which an agent learns to solve sequential decision problems from a reward signal Sutton et al. (1998) . ...
arXiv:2105.11977v2
fatcat:w37mjxeaafefnko3a64on7l3uu
Simulating SQL injection vulnerability exploitation using Q-learning reinforcement learning agents
2021
Journal of Information Security and Applications
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solve an individual challenge but a more generic policy that may be applied to perform SQL injection attacks against any system instantiated randomly by our problem generator. ...
We consider a simplification of the dynamics of SQL injection attacks by casting this problem as a security capturethe-flag challenge. ...
Literature overview Machine learning has recently found application in many fields in order to solve problems via induction and inference, including security [12] . ...
doi:10.1016/j.jisa.2021.102903
fatcat:ku74n2vm6jc7nkza3cfzoa4lfe
Shared Autonomy via Deep Reinforcement Learning
2018
Robotics: Science and Systems XIV
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In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal. ...
We balance these two needs by discarding actions whose values fall below some threshold, then selecting the remaining action closest to the user's input. ...
ACKNOWLEDGEMENTS We would like to thank Oleg Klimov for open-sourcing his implementation of the Lunar Lander game, which was originally developed by Atari in 1979. ...
doi:10.15607/rss.2018.xiv.005
dblp:conf/rss/ReddyDL18
fatcat:pd5wcvxrn5f5tayv7zyjhe54l4
Learning structured reactive navigation plans from executing MDP navigation policies
2001
Proceedings of the fifth international conference on Autonomous agents - AGENTS '01
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XFRMLEARN is implemented and extensively evaluated on an autonomous mobile robot. ...
Concurrent plans are represented in a transparent and modular form so that automatic planning techniques can make inferences about them and revise them. ...
The research reported in this paper is partly funded by the Deutsche Forschungsgemeinschaft (DFG) under contract number BE 2200/3-1. ...
doi:10.1145/375735.375795
dblp:conf/agents/BeetzB01
fatcat:ovln7czxjvd7bbcuxfhdxmxrzy
Learning sequential tasks interactively from demonstrations and own experience
2013
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
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Using a Gaussian Process approximation of the state-action sequence value function, our approach generalizes values observed from demonstrated and autonomously generated action sequences to unknown inputs ...
In this paper, we propose an intuitive learning method for a robot to acquire sequences of motions by combining learning from human demonstrations and reinforcement learning. ...
Generalization is performed by analyzing multiple demonstrations of the same task, or by interactively asking the teacher to relax preconditions by hinting at irrelevant task features. ...
doi:10.1109/iros.2013.6696816
dblp:conf/iros/GraveB13
fatcat:cjqqkkr6s5enlb7i6kvn6w5lqm
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