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Principles of NoologyApplication to the StarCraft Game Environment

Principles of Noology: Application to the StarCraft Game Environment [The StarCraft game environment provides an ideal computational platform to test and illustrate the various principles of noology that have been described in the previous chapters. In this chapter we describe an implemented AI program that plays against the StarCraft built-in game engine. Causal learning is applied successfully to rapidly learn the causal rules to engage and attack enemy agents. Scripts are learned along the way to accelerate problem solving. Counterfactual information associated with scripts that were alluded to in previous chapters are shown here to play a critical role in providing information for the planning of battle strategies. Affective competition is implemented as a high-level goal prioritizing mechanism for the agent involved. As in the previous chapter, the learning of heuristics is shown here to assist in reducing the search space needed for problem solving. Also, as in Chap. 5, it is illustrated here how the grounded conceptual representations used enable the system to learn problem solving methods rapidly through language.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Principles of NoologyApplication to the StarCraft Game Environment

Part of the Socio-Affective Computing Book Series (volume 3)
Principles of Noology — Jun 29, 2016

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-32111-0
Pages
283 –342
DOI
10.1007/978-3-319-32113-4_7
Publisher site
See Chapter on Publisher Site

Abstract

[The StarCraft game environment provides an ideal computational platform to test and illustrate the various principles of noology that have been described in the previous chapters. In this chapter we describe an implemented AI program that plays against the StarCraft built-in game engine. Causal learning is applied successfully to rapidly learn the causal rules to engage and attack enemy agents. Scripts are learned along the way to accelerate problem solving. Counterfactual information associated with scripts that were alluded to in previous chapters are shown here to play a critical role in providing information for the planning of battle strategies. Affective competition is implemented as a high-level goal prioritizing mechanism for the agent involved. As in the previous chapter, the learning of heuristics is shown here to assist in reducing the search space needed for problem solving. Also, as in Chap. 5, it is illustrated here how the grounded conceptual representations used enable the system to learn problem solving methods rapidly through language.]

Published: Jun 29, 2016

Keywords: StarCraft; Causal learning; Script; Counterfactual information; Affective computing; Affective competition; Heuristic; Heuristic generalization; Learning of heuristic; Problem solving; Battle strategy; Learning through language

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