WO2006014560A3 - Real time context learning by software agents - Google Patents

Real time context learning by software agents Download PDF

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Publication number
WO2006014560A3
WO2006014560A3 PCT/US2005/024139 US2005024139W WO2006014560A3 WO 2006014560 A3 WO2006014560 A3 WO 2006014560A3 US 2005024139 W US2005024139 W US 2005024139W WO 2006014560 A3 WO2006014560 A3 WO 2006014560A3
Authority
WO
WIPO (PCT)
Prior art keywords
features
learner
raw
software agents
learners
Prior art date
Application number
PCT/US2005/024139
Other languages
French (fr)
Other versions
WO2006014560A2 (en
Inventor
John Funge
Ron Musick
Daniel Dobson
Nigel Duffy
Michael Mcnally
Xiaoyuan Tu
Ian Wright
Wei Yen
Brian Cabral
Original Assignee
Ailive Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ailive Inc filed Critical Ailive Inc
Publication of WO2006014560A2 publication Critical patent/WO2006014560A2/en
Publication of WO2006014560A3 publication Critical patent/WO2006014560A3/en

Links

Classifications

    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/60—Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor
    • A63F13/67—Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/60—Methods for processing data by generating or executing the game program
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/60—Methods for processing data by generating or executing the game program
    • A63F2300/6027—Methods for processing data by generating or executing the game program using adaptive systems learning from user actions, e.g. for skill level adjustment

Abstract

Providing dynamic learning for software agents (140) in a simulation. Software agents (140) with learners (165) are capable of learning from examples. When a non-player character (142) queries the learner (165), it can provide a next action similar to the player character (141). The game designer provides program code, from which compile-time steps determine a set of raw features (150). The code might identify a function (like computing distances). At compile-time steps, determining these raw features (150) in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features (160), responsive to the raw features (150), might be relatively simple, more complex, or determined in response to a learner (165). The set of such raw (150) and derived features (160) form a context for a learner (165). Learners (165) might be responsive to (more basic) learners, to results of state machines, to calculated derived features (160), or to raw features (150). The learner (165) includes a machine learning technique.
PCT/US2005/024139 2004-07-06 2005-07-06 Real time context learning by software agents WO2006014560A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US10/885,495 2004-07-06
US10/885,495 US7296007B1 (en) 2004-07-06 2004-07-06 Real time context learning by software agents

Publications (2)

Publication Number Publication Date
WO2006014560A2 WO2006014560A2 (en) 2006-02-09
WO2006014560A3 true WO2006014560A3 (en) 2009-04-09

Family

ID=35787655

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2005/024139 WO2006014560A2 (en) 2004-07-06 2005-07-06 Real time context learning by software agents

Country Status (2)

Country Link
US (2) US7296007B1 (en)
WO (1) WO2006014560A2 (en)

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Publication number Publication date
US7296007B1 (en) 2007-11-13
US7636701B2 (en) 2009-12-22
WO2006014560A2 (en) 2006-02-09
US20070260567A1 (en) 2007-11-08
US20080097948A1 (en) 2008-04-24

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