The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques

Smith, Robert E.; Dike, B. A.; Ravichandran, B.; El-Fallah, A.; Mehra, Raman K.

doi:10.1007/3-540-45027-0_15

Cited by 16 publications

(5 citation statements)

References 6 publications

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“…Interestingly, however, there are several examples of applying LCS to real-world systems, for example, controlling or action planning for a physical robot (15) (16) , performing aircraft maneuvers (17) (18) , and trading in the stock market (19) . This is possibly due to LCS's rule-based representation.…”

Section: How the Results Contributes To Lcsmentioning

confidence: 99%

Comparing Learning Classifier System and Reinforcement Learning with Function Approximation

et al. 2004

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MemberAs a first step toward an analysis of the capabilities of adaptive systems, including learning and evolution, we focus on the Learning Classifier System (LCS) and compare it with Reinforcement Learning (RL) that adopts the Function Approximation (FA) method. An analysis of this comparison found an equivalence of learning processes between both the two models, which brings the mathematical framework of the LCS's learning process to the level of RL with FA. Our analysis also clarified the limitations of the results.

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Section: How the Results Contributes To Lcsmentioning

confidence: 99%

Comparing Learning Classifier System and Reinforcement Learning with Function Approximation

et al. 2004

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“…A battle consists of a set of soldier-to-soldier fights running in parallel, each ending with the death of one combatant. A key to the success of the system is the fairly general nature of the possible actions [3]: pursue a named enemy squad, flank a named enemy squad, flee from a named enemy squad, follow the friendly squad. Unlike in the original LCS, the reward system is invoked immediately after the environment string is built and evaluates the squad's previous action.…”

Section: The Game and The Classifier Systemmentioning

confidence: 99%

Dynamic Strategies in a Real-Time Strategy Game

Falke

Ross

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. Most modern real-time strategy computer games have a sophisticated but fixed 'AI' component that controls the computer's actions. Once the user has learned how such a game will react, the game quickly loses its appeal. This paper describes an example of how a learning classifier system (based on Wilson's ZCS [1]) can be used to equip the computer with dynamically-changing strategies that respond to the user's strategies, thus greatly extending the games playability for serious gamers. The Game and the Classifier SystemReal-time strategy (RTS) games typically involve fighting a battle against a computer opponent. This opponent typically has a very sophisticated but essentially static strategy and once the user has learned how it behaves, the game loses its playability. To get round this limitation we have used a learning classifier system (LCS) to provide the computer with the capability to dynamically change its strategy. LCSs are sometimes criticized for being slow to learn. In this example, the LCS has short conditions so as to fit in the real-time loop; success also depends on a careful choice of the types of actions.The game we implemented, using the publicly available version of the Auran Jet game engine, involves two opposing armies each consisting of two squads of 20 soldiers, moving on hilly terrain. A squad defaults to moving in a 6-6-6-2 formation but the soldiers are individually governed by a flocking algorithm [2] so that squad shapes are very fluid in battles. The essence of the game play is to jockey for position, because it pays for a squad to attack the flank of the enemy or for two squads to attack a third if its companion squad is too far away to offer support quickly enough. Figure 1 illustrates the idea. The rectangles are identifying battle-flags. The large diamond is the cursor that the user employs to command his squads, by placing the cursor somewhere and then left-or rightclicking to summon a particular squad towards that location.The classifier system sends commands to the computer's squads, at randomlychosen times within a two-to four-second interval (experimentally determined to offer good playability). The condition part has 14 bits as follows, the values depend on the squad to be commanded. For enemy squad 1, two bits describe how far away it is; one bit indicates whether that squad is engaged in a fight; E. Cantú-Paz et al. (Eds.): GECCO

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“…These include LCS with GP as described in [1], as well as the classifier system works of [2], [3], [4], [5], [6], [7], [8] and many others. The GP-LCS system was constructed using a mobile agent-based architecture, to better enable it to operate in a Complex Adaptive System (CAS) type of environment [9].…”

Section: Introductionmentioning

confidence: 99%

Genetically programmed learning classifier system description and results

Harrison

Worden²

2007

Proceedings of the 9th Annual Conference Companion on Genetic and Evolutionary Computation

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An agent population can be evolved in a complex environment to perform various tasks and optimize its job performance using Learning Classifier System (LCS) technology. Due to the complexity and knowledge content of some real-world systems, having the ability to use genetic programming, GP, to represent the LCS rules provides a great benefit. Methods have been created to extend LCS theory into operation across the power-set of GP-enabled rule content. This system uses a full bucketbrigade system for GP-LCS learning. Using GP in the LCS system allows the functions and terminals of the actual problem environment to be used internally directly in the rule set, enabling more direct interpretation of the operation of the LCS system. The system was designed and built, and underwent independent testing at an advanced technology research laboratory. This paper describes the top-level operation of the system, and includes some of the results of the testing effort, and performance figures.

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The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques

Cited by 16 publications

References 6 publications

Comparing Learning Classifier System and Reinforcement Learning with Function Approximation

Comparing Learning Classifier System and Reinforcement Learning with Function Approximation

Dynamic Strategies in a Real-Time Strategy Game

Genetically programmed learning classifier system description and results

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