Real time context learning by software agents

Abstract

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

Description

BACKGROUND OF THE INVENTION [0001] 1. Field of the Invention [0002] The invention relates to context learning by software agents, such as for example might be used by embedded game agents in a gaming system, such as for example during play of the game. [0003] 2. Related Art [0004] In a computer simulation, especially in a computer game that attempts to present a fantasy world (i.e., to create and simulate a variant world in which there are at least some fantasy or fictional aspects), there might be numerous agents within the game, each of which has independent actions they can perform. One set of those agents are controlled by players (herein sometimes called “player characters,” and herein sometimes called “human users”), while other sets of those agents are controlled by the game software (herein sometimes called “non-player characters”). It is also possible that the selection of which of those agents are player characters and the selection of which of those agents are non-player ...

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