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Teaching Game AI as an Undergraduate Course in Computational Media
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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We need to teach AI to students in and outside of traditional computer science degree programs, including those designer-engineer hybrid students who will design and implement games or engage in technical ...
This course sets up computer science and computer game design students to transform practices in the game industry as well as create new forms of media that were previously unreachable. ...
Conclusion This experience report contributes the design of a new Game AI course. ...
doi:10.1609/aaai.v34i09.7064
fatcat:uh6nejyjqjc4jnc2jmajm5pvdm
Page 340 of Psychological Abstracts Vol. 92, Issue 1
[page]
2005
Psychological Abstracts
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
86: 2997-3006
other jurisdictions with assessment programs. Crash involvement rates have been calculated using two denominators: per population and per number of li- censed drivers. ...
using a multiple baseline design. ...
Evolutionary computation in zoology and ecology
2017
Current Zoology
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The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. ...
Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. ...
The fitness of each tree is assessed using training data (or cross-validation), judging how close the result from each program is matching the data. ...
doi:10.1093/cz/zox057
pmid:29492029
pmcid:PMC5804223
fatcat:nz7ufmfeb5aftpxoappnxcag5q
SIMULATING AN EVOLUTIONARY MULTI-AGENT BASED MODEL OF THE STOCK MARKET
2015
Ecoforum
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The multi-agent model uses evolutionary techniques such as genetic programming in order to generate an adaptive and evolving population of agents. ...
Each artificial agent is endowed with wealth and a genetic programming induced trading strategy. ...
Genetic programming was first developed by Koza (1992) , while the special type of genetic programming used in the hereto paper is the Strongly Typed Genetic Programming technique which was introduced ...
doaj:ade8cc9e04a94f62830a79e48cbfaf5c
fatcat:vuzmetn5mfh75inllp3m37z5zu
Chapter 19 Agent-Based Models and Human Subject Experiments
[chapter]
2006
Handbook of Computational Economics
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Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in "computational laboratories" and vice versa. ...
Abstract This chapter examines the relationship between agent-based modeling and economic decision-making experiments with human subjects. ...
assess whether players' behavior changes with experience. ...
doi:10.1016/s1574-0021(05)02019-8
fatcat:3sdkbfigsrh5lhfcxspzor6hky
A Framework for Learning Behavior Trees in Collaborative Robotic Applications
[article]
2023
arXiv
pre-print
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In this paper we propose a framework that combines a method that learns Behavior Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications ...
We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot. ...
The contributions of this paper is a framework that combines a method that evolves Behavior Trees (BTs) with Genetic Programming (GP) with a method that learns BTs from demonstration. ...
arXiv:2303.11026v1
fatcat:b4gizi5jpjbuniizd5bm5hyjgm
Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
2023
Education Sciences
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This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, ...
Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. ...
Deep belief networks are a type of artificial neural network with multiple layers of hidden units, capable of unsupervised learning. ...
doi:10.3390/educsci13121216
fatcat:nzoipepnszgtjktkknmae5nz3u
Continuous On-line Evolution of Agent Behaviours with Cartesian Genetic Programming
[article]
2014
arXiv
pre-print
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In this paper, we present an on-line evolutionary programming algorithm that searches in the agent design space for the appropriate behavioural policies to cope with the underlying environment. ...
Evolutionary Computation has been successfully used to synthesise controllers for embodied agents and multi-agent systems in general. ...
Using supervised learning techniques to synthesise agent behaviour policies is more appropriate for off-line learning scenarios where a proper assessment can be made about the state of the environment ...
arXiv:1407.0698v1
fatcat:j7ennihu5jdh5mnz5dgxz4v3cy
Evolutionary optimization of cooperative heterogeneous teams
2007
Evolutionary and Bio-inspired Computation: Theory and Applications
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However, heterogeneous teams, teams of units with specialized roles and/or specialized capabilities, have received relatively little attention. ...
Units with specialized roles or capabilities require specialized software that take into account the role and capabilities of both itself and its neighbors. ...
ACKNOWLEDGMENTS This unnumbered section is used to identify those who have aided the authors in understanding or accomplishing the work presented and to acknowledge sources of funding. ...
doi:10.1117/12.724018
fatcat:72hodxhuijfhjaadbc2ridewca
Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games
[article]
2022
arXiv
pre-print
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In comparison with the state-of-the-art RLlib, we empirically demonstrate the unique advantages of Lamarckian on benchmark tests with up to 6000 CPU cores: i) both the sampling efficiency and training ...
Moreover, we also present two use cases: i) how Lamarckian is applied to generating behavior-diverse game AI; ii) how Lamarckian is applied to game balancing tests for an asynchronous commercial game. ...
The basic idea of EMOGI is to guide the AI agent to learn towards desired behaviors automatically by tailoring a reward function with multiple objectives, where a multiobjective optimization algorithm ...
arXiv:2209.10055v1
fatcat:d54gqez7rzfzfnsa2udei2xdce
Towards Realizing Intelligent Coordinated Controllers for Multi-USV Systems Using Abstract Training Environments
2021
Journal of Marine Science and Engineering
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Then, a behavior-driven artificial immune-inspired fuzzy classifier systems approach that is capable of optimizing agents' behaviors and action selection in a multi-agent environment is presented. ...
Machine learning approaches have been largely employed in simplified simulations to acquire intelligent control systems in multi-agent settings. ...
For instance, intelligent decision-making for multiple unmanned vehicles using genetic fuzzy trees is presented by the authors in [13] . ...
doi:10.3390/jmse9060560
fatcat:3f57l7ko45ddbfkvkh62b6rmzq
Data Mining for Education Decision Support: A Review
2014
International Journal of Emerging Technologies in Learning (iJET)
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In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. ...
Management of higher education must continue to evaluate on an ongoing basis in order to improve the quality of institutions. ...
The interactions that students have with each other, with the instructors, and with educational resources are valuable indicators of the effectiveness of a learning experience. ...
doi:10.3991/ijet.v9i6.3950
fatcat:yk4kyrr2onclnhgaevpuhmkrlq
Evolutionary model discovery of causal factors behind the socio-agricultural behavior of the Ancestral Pueblo
2020
PLoS ONE
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We introduce evolutionary model discovery, a framework that combines genetic programming and random forest regression to evaluate the importance of a set of causal factors hypothesized to affect the individual's ...
Agent-based modeling of artificial societies allows for the validation and analysis of human-interpretable, causal explanations of human behavior that generate society-scale phenomena. ...
The syntax tree representation is perhaps the most common representation used in genetic programming, and arranges the primitives and terminals into a tree structure, a representation compatible with b ...
doi:10.1371/journal.pone.0239922
pmid:33338054
fatcat:hbgecapphbfb7jwoj6vly3ovhi
A.P.G.: An Intelligent Automatic Generator of Presentations for Tour-Guide Robots
2011
International Journal of Computational Intelligence Systems
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It uses a genetic algorithm for the evolution of the rules. ...
The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. ...
Acknowledgements This work is founded by the Spanish Ministry of Science and Innovation (ROBONAUTA: DPI 2007-66846-C02-01 and ARABOT: DPI 2010-21247-C02-01) and supervised by CACSA whose kindness we gratefully ...
doi:10.1080/18756891.2011.9727803
fatcat:uqoewp72b5hancizzwhxdagnk4
Recent advances on artificial intelligence and learning techniques in cognitive radio networks
2015
EURASIP Journal on Wireless Communications and Networking
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Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. ...
Then, it introduces artificial intelligence and machine-learning techniques and emphasizes the role of learning in cognitive radios. ...
Genetic algorithms Genetic algorithms (GA) are originated in the work of Friedberg (1958), who attempted to produce learning by mutating small FORTRAN programs. ...
doi:10.1186/s13638-015-0381-7
fatcat:dq6aba75obc5vlxnbaqrerlsii
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