The Experts below are selected from a list of 57 Experts worldwide ranked by ideXlab platform
Pieter Spronck - One of the best experts on this subject based on the ideXlab platform.
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Improving Air-to-Air Combat Behavior through Transparent Machine Learning
2014Co-Authors: Armon Toubman, Pieter Spronck, Jan Joris Roessingh, Aske Plaat, H.j. Van Den HerikAbstract:Training simulations, especially those for tactical training, require properly behaving computer generated forces (CGFs) in the opponent role for an effective training experience. Traditionally, the behavior of such CGFs is controlled through scripts. There are two main problems with the use of scripts for controlling the behavior of CGFs: (1) building an effective script requires expert knowledge, which is costly, and (2) costs further increase with the number of ‘learning events’ in a scenario (e.g. a new opponent tactic). Machine learning Techniques may offer a solution to these two problems, by automatically generating, evaluating and improving CGF behavior. In this paper we describe an application of the dynamic Scripting Technique to the generation of CGF behavior for training simulations. Dynamic Scripting is a machine learning Technique that searches for effective scripts by combining rules from a rule base with predefined behavior rules. Although dynamic Scripting was initially developed for artificial intelligence (AI) in commercial video games, its computational and functional qualities are also desirable in military training simulations. Among other qualities, dynamic Scripting generates behavior in a transparent manner. Also, dynamic Scripting’s learning method is robust: a minimum level of effectiveness is guaranteed through the use of domain knowledge in the initial rule base. In our research, we investigate the application of dynamic Scripting for generating behaviors of multiple cooperating aircraft in air-to-air combat. Coordination in multi-agent systems remains a non-trivial problem. We enabled explicit team coordination through communication between team members. This coordination method was tested in an air combat simulation experiment, and compared against a baseline that consisted of a similar dynamic Scripting setup, without explicit coordination. In terms of combat performance, the team using the explicit team coordination was 20% more effective than the baseline. Finally, the paper will discuss the application of dynamic Scripting in a practical setting.
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Centralized Versus Decentralized Team Coordination Using Dynamic Scripting
2014Co-Authors: Armon Toubman, Pieter Spronck, Jan Joris Roessingh, Aske Plaat, H.j. Van Den HerikAbstract:Computer generated forces (CGFs) must display realistic behavior for tactical training simulations to yield an effective training experience. Tradionally, the behavior of CGFs is scripted. However, there are three drawbacks, viz. (1) Scripting limits the adaptive behavior of CGFs, (2) creating scripts is difficult and (3) it requires scarce domain expertise. A promising machine learning Technique is the dynamic Scripting of CGF behavior. In simulating air combat scenarios, team behavior is important, both with and without communication. While dynamic Scripting has been reported to be effective in creating behavior for single fighters, it has not often been used for team coordination. The dynamic Scripting Technique is sufficiently flexible to be used for different team coordination methods. In this paper, we report the first results on centralized coordination of dynamically scripted air combat teams, and compare these results to a decentralized approach from earlier work. We find that using the centralized approach leads to higher performance and more efficient learning, although creativity of the solutions seems bounded by the reduced complexity.
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Adaptive game AI with dynamic Scripting
Machine Learning, 2006Co-Authors: Pieter Spronck, Ida Sprinkhuizen-kuyper, Marc Ponsen, Erik PostmaAbstract:Online learning in commercial computer games allows computer-controlled opponents to adapt to the way the game is being played. As such it provides a mechanism to deal with weaknesses in the game AI, and to respond to changes in human player tactics. We argue that online learning of game AI should meet four computational and four functional requirements. The computational requirements are speed, effectiveness, robustness and efficiency. The functional requirements are clarity, variety, consistency and scalability. This paper investigates a novel online learning Technique for game AI called 'dynamic Scripting', that uses an adaptive rulebase for the generation of game AI on the fly. The performance of dynamic Scripting is evaluated in experiments in which adaptive agents are pitted against a collection of manually-designed tactics in a simulated computer roleplaying game. Experimental results indicate that dynamic Scripting succeeds in endowing computer-controlled opponents with adaptive performance. To further improve the dynamic-Scripting Technique, an enhancement is investigated that allows scaling of the difficulty level of the game AI to the human player's skill level. With the enhancement, dynamic Scripting meets all computational and functional requirements. The applicability of dynamic Scripting in state-of-the-art commercial games is demonstrated by implementing the Technique in the game Neverwinter Nights. We conclude that dynamic Scripting can be successfully applied to the online adaptation of game AI in commercial computer games.
Armon Toubman - One of the best experts on this subject based on the ideXlab platform.
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Improving Air-to-Air Combat Behavior Through Transparent Machine Learning
2016Co-Authors: Armon Toubman, Aske Plaat, Jan Joris, Roessingh Pieter Spronck, Jaap Van Den HerikAbstract:Training simulations, especially those for tactical training, require properly behaving computer generated forces (CGFs) in the opponent role for an effective training experience. Traditionally, the behavior of such CGFs is controlled through scripts. There are two main problems with the use of scripts for controlling the behavior of CGFs: (1) building an effective script requires expert knowledge, which is costly, and (2) costs further increase with the number of ‘learning events ’ in a scenario (e.g. a new opponent tactic). Machine learning Techniques may offer a solution to these two problems, by automatically generating, evaluating and improving CGF behavior. In this paper we describe an application of the dynamic Scripting Technique to the generation of CGF behavior for training simulations. Dynamic Scripting is a machine learning Technique that searches for effective scripts by combining rules from a rule base with predefined behavior rules. Although dynamic Scripting was initially developed for artificial intelligence (AI) in commercial video games, its computational and functional qualities are also desirable in military training simulations. Among other qualities, dynamic Scripting generates behavior in a transparent manner. Also, dynamic Scripting’s learning method is robust: a minimum level of effectiveness is guaranteed through the use of domain knowledge in the initial rule base. In our research, we investigate the application of dynamic Scripting for generating behaviors of multiple cooperating aircraft in air-to-air combat. Coordination i
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© EUROSIS-ETI CENTRALIZED VERSUS DECENTRALIZED TEAM COORDINATION USING DYNAMIC Scripting
2016Co-Authors: Armon Toubman, Jan Joris, Roessingh Pieter, Spronck Aske Plaat, Jaap Van Den HerikAbstract:Computer generated forces (CGFs) must display realistic behavior for tactical training simulations to yield an effective training experience. Tradionally, the behavior of CGFs is scripted. However, there are three drawbacks, viz. (1) Scripting limits the adaptive behavior of CGFs, (2) creating scripts is difficult and (3) it requires scarce domain expertise. A promising machine learning Technique is the dynamic Scripting of CGF behavior. In simulating air combat scenarios, team behavior is important, both with and without communication. While dynamic Scripting has been reported to be effective in creating behavior for single fighters, it has not often been used for team coordination. The dynamic Scripting Technique is sufficiently flexible to be used for different team coordination methods. In this paper, we report the first results on centralized coordination of dynamically scripted air combat teams, and compare these results to a decentralized approach from earlier work. We find that using the centralized approach leads to higher performance and more efficient learning, although creativity of the solutions seems bounded by the reduced complexity
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Centralized Versus Decentralized Team Coordination Using Dynamic Scripting
2014Co-Authors: Armon Toubman, Pieter Spronck, Jan Joris Roessingh, Aske Plaat, H.j. Van Den HerikAbstract:Computer generated forces (CGFs) must display realistic behavior for tactical training simulations to yield an effective training experience. Tradionally, the behavior of CGFs is scripted. However, there are three drawbacks, viz. (1) Scripting limits the adaptive behavior of CGFs, (2) creating scripts is difficult and (3) it requires scarce domain expertise. A promising machine learning Technique is the dynamic Scripting of CGF behavior. In simulating air combat scenarios, team behavior is important, both with and without communication. While dynamic Scripting has been reported to be effective in creating behavior for single fighters, it has not often been used for team coordination. The dynamic Scripting Technique is sufficiently flexible to be used for different team coordination methods. In this paper, we report the first results on centralized coordination of dynamically scripted air combat teams, and compare these results to a decentralized approach from earlier work. We find that using the centralized approach leads to higher performance and more efficient learning, although creativity of the solutions seems bounded by the reduced complexity.
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Improving Air-to-Air Combat Behavior through Transparent Machine Learning
2014Co-Authors: Armon Toubman, Pieter Spronck, Jan Joris Roessingh, Aske Plaat, H.j. Van Den HerikAbstract:Training simulations, especially those for tactical training, require properly behaving computer generated forces (CGFs) in the opponent role for an effective training experience. Traditionally, the behavior of such CGFs is controlled through scripts. There are two main problems with the use of scripts for controlling the behavior of CGFs: (1) building an effective script requires expert knowledge, which is costly, and (2) costs further increase with the number of ‘learning events’ in a scenario (e.g. a new opponent tactic). Machine learning Techniques may offer a solution to these two problems, by automatically generating, evaluating and improving CGF behavior. In this paper we describe an application of the dynamic Scripting Technique to the generation of CGF behavior for training simulations. Dynamic Scripting is a machine learning Technique that searches for effective scripts by combining rules from a rule base with predefined behavior rules. Although dynamic Scripting was initially developed for artificial intelligence (AI) in commercial video games, its computational and functional qualities are also desirable in military training simulations. Among other qualities, dynamic Scripting generates behavior in a transparent manner. Also, dynamic Scripting’s learning method is robust: a minimum level of effectiveness is guaranteed through the use of domain knowledge in the initial rule base. In our research, we investigate the application of dynamic Scripting for generating behaviors of multiple cooperating aircraft in air-to-air combat. Coordination in multi-agent systems remains a non-trivial problem. We enabled explicit team coordination through communication between team members. This coordination method was tested in an air combat simulation experiment, and compared against a baseline that consisted of a similar dynamic Scripting setup, without explicit coordination. In terms of combat performance, the team using the explicit team coordination was 20% more effective than the baseline. Finally, the paper will discuss the application of dynamic Scripting in a practical setting.
H.j. Van Den Herik - One of the best experts on this subject based on the ideXlab platform.
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Centralized Versus Decentralized Team Coordination Using Dynamic Scripting
2014Co-Authors: Armon Toubman, Pieter Spronck, Jan Joris Roessingh, Aske Plaat, H.j. Van Den HerikAbstract:Computer generated forces (CGFs) must display realistic behavior for tactical training simulations to yield an effective training experience. Tradionally, the behavior of CGFs is scripted. However, there are three drawbacks, viz. (1) Scripting limits the adaptive behavior of CGFs, (2) creating scripts is difficult and (3) it requires scarce domain expertise. A promising machine learning Technique is the dynamic Scripting of CGF behavior. In simulating air combat scenarios, team behavior is important, both with and without communication. While dynamic Scripting has been reported to be effective in creating behavior for single fighters, it has not often been used for team coordination. The dynamic Scripting Technique is sufficiently flexible to be used for different team coordination methods. In this paper, we report the first results on centralized coordination of dynamically scripted air combat teams, and compare these results to a decentralized approach from earlier work. We find that using the centralized approach leads to higher performance and more efficient learning, although creativity of the solutions seems bounded by the reduced complexity.
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Improving Air-to-Air Combat Behavior through Transparent Machine Learning
2014Co-Authors: Armon Toubman, Pieter Spronck, Jan Joris Roessingh, Aske Plaat, H.j. Van Den HerikAbstract:Training simulations, especially those for tactical training, require properly behaving computer generated forces (CGFs) in the opponent role for an effective training experience. Traditionally, the behavior of such CGFs is controlled through scripts. There are two main problems with the use of scripts for controlling the behavior of CGFs: (1) building an effective script requires expert knowledge, which is costly, and (2) costs further increase with the number of ‘learning events’ in a scenario (e.g. a new opponent tactic). Machine learning Techniques may offer a solution to these two problems, by automatically generating, evaluating and improving CGF behavior. In this paper we describe an application of the dynamic Scripting Technique to the generation of CGF behavior for training simulations. Dynamic Scripting is a machine learning Technique that searches for effective scripts by combining rules from a rule base with predefined behavior rules. Although dynamic Scripting was initially developed for artificial intelligence (AI) in commercial video games, its computational and functional qualities are also desirable in military training simulations. Among other qualities, dynamic Scripting generates behavior in a transparent manner. Also, dynamic Scripting’s learning method is robust: a minimum level of effectiveness is guaranteed through the use of domain knowledge in the initial rule base. In our research, we investigate the application of dynamic Scripting for generating behaviors of multiple cooperating aircraft in air-to-air combat. Coordination in multi-agent systems remains a non-trivial problem. We enabled explicit team coordination through communication between team members. This coordination method was tested in an air combat simulation experiment, and compared against a baseline that consisted of a similar dynamic Scripting setup, without explicit coordination. In terms of combat performance, the team using the explicit team coordination was 20% more effective than the baseline. Finally, the paper will discuss the application of dynamic Scripting in a practical setting.
Jaap Van Den Herik - One of the best experts on this subject based on the ideXlab platform.
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Improving Air-to-Air Combat Behavior Through Transparent Machine Learning
2016Co-Authors: Armon Toubman, Aske Plaat, Jan Joris, Roessingh Pieter Spronck, Jaap Van Den HerikAbstract:Training simulations, especially those for tactical training, require properly behaving computer generated forces (CGFs) in the opponent role for an effective training experience. Traditionally, the behavior of such CGFs is controlled through scripts. There are two main problems with the use of scripts for controlling the behavior of CGFs: (1) building an effective script requires expert knowledge, which is costly, and (2) costs further increase with the number of ‘learning events ’ in a scenario (e.g. a new opponent tactic). Machine learning Techniques may offer a solution to these two problems, by automatically generating, evaluating and improving CGF behavior. In this paper we describe an application of the dynamic Scripting Technique to the generation of CGF behavior for training simulations. Dynamic Scripting is a machine learning Technique that searches for effective scripts by combining rules from a rule base with predefined behavior rules. Although dynamic Scripting was initially developed for artificial intelligence (AI) in commercial video games, its computational and functional qualities are also desirable in military training simulations. Among other qualities, dynamic Scripting generates behavior in a transparent manner. Also, dynamic Scripting’s learning method is robust: a minimum level of effectiveness is guaranteed through the use of domain knowledge in the initial rule base. In our research, we investigate the application of dynamic Scripting for generating behaviors of multiple cooperating aircraft in air-to-air combat. Coordination i
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© EUROSIS-ETI CENTRALIZED VERSUS DECENTRALIZED TEAM COORDINATION USING DYNAMIC Scripting
2016Co-Authors: Armon Toubman, Jan Joris, Roessingh Pieter, Spronck Aske Plaat, Jaap Van Den HerikAbstract:Computer generated forces (CGFs) must display realistic behavior for tactical training simulations to yield an effective training experience. Tradionally, the behavior of CGFs is scripted. However, there are three drawbacks, viz. (1) Scripting limits the adaptive behavior of CGFs, (2) creating scripts is difficult and (3) it requires scarce domain expertise. A promising machine learning Technique is the dynamic Scripting of CGF behavior. In simulating air combat scenarios, team behavior is important, both with and without communication. While dynamic Scripting has been reported to be effective in creating behavior for single fighters, it has not often been used for team coordination. The dynamic Scripting Technique is sufficiently flexible to be used for different team coordination methods. In this paper, we report the first results on centralized coordination of dynamically scripted air combat teams, and compare these results to a decentralized approach from earlier work. We find that using the centralized approach leads to higher performance and more efficient learning, although creativity of the solutions seems bounded by the reduced complexity
Aske Plaat - One of the best experts on this subject based on the ideXlab platform.
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Improving Air-to-Air Combat Behavior Through Transparent Machine Learning
2016Co-Authors: Armon Toubman, Aske Plaat, Jan Joris, Roessingh Pieter Spronck, Jaap Van Den HerikAbstract:Training simulations, especially those for tactical training, require properly behaving computer generated forces (CGFs) in the opponent role for an effective training experience. Traditionally, the behavior of such CGFs is controlled through scripts. There are two main problems with the use of scripts for controlling the behavior of CGFs: (1) building an effective script requires expert knowledge, which is costly, and (2) costs further increase with the number of ‘learning events ’ in a scenario (e.g. a new opponent tactic). Machine learning Techniques may offer a solution to these two problems, by automatically generating, evaluating and improving CGF behavior. In this paper we describe an application of the dynamic Scripting Technique to the generation of CGF behavior for training simulations. Dynamic Scripting is a machine learning Technique that searches for effective scripts by combining rules from a rule base with predefined behavior rules. Although dynamic Scripting was initially developed for artificial intelligence (AI) in commercial video games, its computational and functional qualities are also desirable in military training simulations. Among other qualities, dynamic Scripting generates behavior in a transparent manner. Also, dynamic Scripting’s learning method is robust: a minimum level of effectiveness is guaranteed through the use of domain knowledge in the initial rule base. In our research, we investigate the application of dynamic Scripting for generating behaviors of multiple cooperating aircraft in air-to-air combat. Coordination i
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Centralized Versus Decentralized Team Coordination Using Dynamic Scripting
2014Co-Authors: Armon Toubman, Pieter Spronck, Jan Joris Roessingh, Aske Plaat, H.j. Van Den HerikAbstract:Computer generated forces (CGFs) must display realistic behavior for tactical training simulations to yield an effective training experience. Tradionally, the behavior of CGFs is scripted. However, there are three drawbacks, viz. (1) Scripting limits the adaptive behavior of CGFs, (2) creating scripts is difficult and (3) it requires scarce domain expertise. A promising machine learning Technique is the dynamic Scripting of CGF behavior. In simulating air combat scenarios, team behavior is important, both with and without communication. While dynamic Scripting has been reported to be effective in creating behavior for single fighters, it has not often been used for team coordination. The dynamic Scripting Technique is sufficiently flexible to be used for different team coordination methods. In this paper, we report the first results on centralized coordination of dynamically scripted air combat teams, and compare these results to a decentralized approach from earlier work. We find that using the centralized approach leads to higher performance and more efficient learning, although creativity of the solutions seems bounded by the reduced complexity.
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Improving Air-to-Air Combat Behavior through Transparent Machine Learning
2014Co-Authors: Armon Toubman, Pieter Spronck, Jan Joris Roessingh, Aske Plaat, H.j. Van Den HerikAbstract:Training simulations, especially those for tactical training, require properly behaving computer generated forces (CGFs) in the opponent role for an effective training experience. Traditionally, the behavior of such CGFs is controlled through scripts. There are two main problems with the use of scripts for controlling the behavior of CGFs: (1) building an effective script requires expert knowledge, which is costly, and (2) costs further increase with the number of ‘learning events’ in a scenario (e.g. a new opponent tactic). Machine learning Techniques may offer a solution to these two problems, by automatically generating, evaluating and improving CGF behavior. In this paper we describe an application of the dynamic Scripting Technique to the generation of CGF behavior for training simulations. Dynamic Scripting is a machine learning Technique that searches for effective scripts by combining rules from a rule base with predefined behavior rules. Although dynamic Scripting was initially developed for artificial intelligence (AI) in commercial video games, its computational and functional qualities are also desirable in military training simulations. Among other qualities, dynamic Scripting generates behavior in a transparent manner. Also, dynamic Scripting’s learning method is robust: a minimum level of effectiveness is guaranteed through the use of domain knowledge in the initial rule base. In our research, we investigate the application of dynamic Scripting for generating behaviors of multiple cooperating aircraft in air-to-air combat. Coordination in multi-agent systems remains a non-trivial problem. We enabled explicit team coordination through communication between team members. This coordination method was tested in an air combat simulation experiment, and compared against a baseline that consisted of a similar dynamic Scripting setup, without explicit coordination. In terms of combat performance, the team using the explicit team coordination was 20% more effective than the baseline. Finally, the paper will discuss the application of dynamic Scripting in a practical setting.