Real time context learning by software agents
US7296007B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 6, 2004 |
| Grant date | Nov 13, 2007 |
| Priority date | — |
| Expiry date | Nov 7, 2025 |
Classification
- Technology area (CPC A)Human Necessities
- CPC primaryA63F2300/6027
- WIPO fieldFurniture, games
- WIPO sectorOther fields
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.