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Development of collective behavior in newborn artificial agents
[article]
2021
arXiv
pre-print
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Specifically, when we raise our artificial agents in natural visual environments with groupmates, the agents spontaneously develop ego-motion, object recognition, and a preference for groupmates, rapidly ...
Here, we used deep reinforcement learning and curiosity-driven learning -- two learning mechanisms deeply rooted in psychological and neuroscientific research -- to build newborn artificial agents that ...
Wood for help designing the artificial agents and virtual worlds, and Linda Smith and Zoran Tiganj for helpful comments on the manuscript. ...
arXiv:2111.03796v1
fatcat:gfttrauwfbbzjjokfxaowmbgru
DeepNav: Learning to Navigate Large Cities
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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The DeepNav agent learns to reach its destination quickly by making the correct navigation decisions at intersections. ...
We propose 3 supervised learning approaches for the navigation task and show how A* search in the city graph can be used to generate supervision for the learning. ...
The agent takes a step in the direction that is predicted by the CNN to have the least distance estimate. ...
doi:10.1109/cvpr.2017.329
dblp:conf/cvpr/BrahmbhattH17
fatcat:jy5vomudsrezvp4u37wyddxwry
Introducing Long Term Memory in an ANN based Multilevel Darwinist Brain
[chapter]
2003
Lecture Notes in Computer Science
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This paper deals with the introduction of long term memory in a Multilevel Darwinist Brain (MDB) structure based on Artificial Neural Networks and its implications on the capability of adapting to new ...
The paper describes the mechanism, introduces the long term mermoy within it and provides some examples of its operation both in theoretical problems and on a real robot whose perceptual and actuation ...
The outputs are the predicted distance given by the nearest sonar, the predicted angular position of that sonar and the predicted boolean value. ...
doi:10.1007/3-540-44868-3_75
fatcat:vgzfaakdsncevklulnndr3hk7y
Embedding High-Level Knowledge into DQNs to Learn Faster and More Safely
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Deep reinforcement learning has been successfully applied in many decision making scenarios. However, the slow training process and difficulty in explaining limit its application. ...
In this framework, the rules dynamically effect the training progress, and accelerate the learning. ...
Formally, knowledge base in Space war is R aw = {r 3 , r 4 }, where η(r 3 ) = on lef t(nearest jet, agent) and δ(r 3 ) = {move lef t}, and η(r 4 ) = on right(nearest jet, agent) and δ(r 4 ) = {move lef ...
doi:10.1609/aaai.v34i09.7091
fatcat:yjwz3sej5ve6hlldzxucljvtb4
CTRANSPORT: Multi-agent-based simulation
2019
Advances in Distributed Computing and Artificial Intelligence Journal
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Big cities suffer from overcrowding which result in traffic congestion and a lot of air pollution. ...
KEYWORD ABSTRACT Electric vehicles; Multi-agent Systems;Ecofriendly Pollution nowadays is a really important issue that must be solved. ...
This follows the tonic of bicycle lane expansion of most cities in the world. In this case, the model has just nine stations. ...
doi:10.14201/adcaij2019811926
fatcat:zchdkv6lcvhppit42mgspk342m
Learning from hotlists and coldlists: towards a WWW information filtering and seeking agent
1995
Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence TAI-95
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We describe a software agent that learns to find information on the World Wide Web (WWW), deciding what new pages might interest a user. ...
By analyzing the information immediately accessible from each link, the agent learns the types of information the user is interested in. ...
Acknowledgments The research reported here was supported in part by NSF grant IRI-9310413 and ARPA grant F49620-92-J-0430 monitored by AFOSR. ...
doi:10.1109/tai.1995.479848
dblp:conf/ictai/PazzaniNM95
fatcat:j55spyuwubgmflzmsfbqhiqm3e
DeepNav: Learning to Navigate Large Cities
[article]
2017
arXiv
pre-print
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The DeepNav agent learns to reach its destination quickly by making the correct navigation decisions at intersections. ...
We propose 3 supervised learning approaches for the navigation task and show how A* search in the city graph can be used to generate supervision for the learning. ...
The agent takes a step in the direction that is predicted by the CNN to have the least distance estimate. ...
arXiv:1701.09135v2
fatcat:m7pekhaqdrb67lj44n6kyugddu
Two Faces of the Framework for Analysis and Prediction, Part 2 - Research
2018
Information Technology and Control
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research (in psychology, medicine, emotion recognition, and agent-based distributed computing). ...
Research applications include applications in data mining (development of a new time-series representation and various interactions between time-series distance measures and classification) and multidisciplinary ...
In order to utilize them for calculating distance matrices relying on the FAP library, we have implemented an agent-based distributed system [42] a b This application involved the processing of real-world ...
doi:10.5755/j01.itc.47.3.18747
fatcat:kqd3tu7r4nd5laetlbaef6xfyy
Multi-Agent Based Diagnostic Model for Breast Tumour Classification
2019
American Journal of Data Mining and Knowledge Discovery
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This study focused on developing a multi-agent based model for diagnosis of breast tumours using the k-Nearest Neighbor (k-NN) algorithm by classifying the nature of the tumours based on their associated ...
The experimental result of the prediction model shows a percentage accuracy score of 98.9%. ...
Calculate the distance between the query-instance and all the training samples ii. Sort the distance and determine the Nearest Neighbour based on the K-th minimum distance. iii. ...
doi:10.11648/j.ajdmkd.20190401.11
fatcat:6drcqxnmengr7nlcw7yloljjse
Many paths to the same goal: metaheuristic operation of brains during natural behavior
[article]
2019
bioRxiv
pre-print
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This ability is currently an area of active investigation in artificial intelligence. ...
findings set the foundation for new approaches to understand the neural substrates of natural behavior as well as the rational development of biologically inspired metaheuristic approaches for complex real-world ...
by distance from the agent. ...
doi:10.1101/697607
fatcat:kzfwitw6c5ea7fqsxsergadqqe
Precise atom manipulation through deep reinforcement learning
[article]
2022
arXiv
pre-print
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Atomic-scale manipulation in scanning tunneling microscopy has enabled the creation of quantum states of matter based on artificial structures and extreme miniaturization of computational circuitry based ...
These results demonstrate that state-of-the-art deep reinforcement learning can offer effective solutions to real-world challenges in nanofabrication and powerful approaches to increasingly complex scientific ...
In the RL training, a STM scan is performed • when the CNN prediction is positive; Figure 1 : 1 Figure 1: Atom manipulation with a DRL agent. ...
arXiv:2203.06975v1
fatcat:dxvfyho4fvcr7jfrzynkatfczi
Computer Prediction of Cardiovascular and Hematological Agents by Statistical Learning Methods
2007
Cardiovascular & Hematological Agents in Medicinal Chemistry
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These methods include partial least squares, multiple linear regressions, linear discriminant analysis, k-nearest neighbour, artificial neural networks and support vector machines. ...
Computational methods have been explored for predicting agents that produce therapeutic or adverse effects in cardiovascular and hematological systems. ...
k Nearest Neighbor (kNN) In kNN, the Euclidean distance between an unclassified vector x and each individual vector x i in the training set is measured [51, 52] . ...
doi:10.2174/187152507779315787
pmid:17266544
fatcat:hntce6ibjrew7lfc2tk37fxzrm
Crowd Anomaly Detection Using Standardized Modeled Input
2012
International Journal of Intelligent Information Systems
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Acknowledgements We wish to thank Captain Steven Siena and Deputy Chief Glenn Hoff for review and suggestions of the crowd anomaly scenarios depicted in this publication. ...
a certain distance averaged over frames Neighbor Distance -the averaged distance between each agent and their k-Nearest Neighbors averaged Relative Speed kDistance -the speed of each agent relve to all ...
Two basic parameters were calculated from the known position of all agents in each frame, distance and dire tion.For any given agent, their distance traveled between frame i and i+1 is given as x i+1 − ...
doi:10.11648/j.ijiis.20120101.11
fatcat:afn6ud4d4jgn5ge53jeeib3nji
Interface agents: A review of the field
[article]
2002
arXiv
pre-print
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A history of agent systems from their birth in the 1960's to the current day is described, along with the issues they try to address. ...
A taxonomy of interface agent systems is presented, and today's agent systems categorized accordingly. ...
Results: Constructive induction was most accurate but only on an artificial domain. CIMA [28] is a text prediction agent, which suggests completions of sentences in a text editor. ...
arXiv:cs/0203012v1
fatcat:7nzkypnpcjbczabewdo4ltj6xe
Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization under Uncertainty
2019
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
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temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning. ...
To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and ...
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ...
doi:10.24963/ijcai.2019/806
dblp:conf/ijcai/ChaseNSL19
fatcat:b46e7m7xjnb7jimx3dzllmanfa
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