Name
Abstract | ||
|---|---|---|
This paper introduces “micro-scalability” as a novel design objective for social reasoning architectures operating in open multiagent systems. Micro-scalability is based on the idea that social reasoning algorithms should be devised in a way that allows for social complexity reduction, and that this can be achieved by operationalising principles of interactionist sociology. We first present a formal model of InFFrA agents called m2InFFrA that utilises two cornerstones of micro-scalability, the principles of social abstraction and transient social optimality. Then, we exemplify the usefulness of these concepts by presenting experimental results with a novel opponent classification heuristic AdHoc that has been developed using the InFFrA social reasoning architecture. These results prove that micro-scalability deserves further investigation as a useful aspect of socionic research. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11594116_9 | Socionics |
Keywords | Field | DocType |
inffra agent,agent experience,transient social optimality,novel opponent classification heuristic,novel design objective,social complexity reduction,inffra social reasoning architecture,social reasoning,social abstraction,social reasoning algorithm,social cognition,complexity reduction | Heuristic,Interactionism,Abstraction,Computer science,Agent-based social simulation,Social complexity,Multi-agent system,Artificial intelligence,Social cognition,Open system (systems theory) | Conference |
Volume | ISSN | ISBN |
3413 | 0302-9743 | 3-540-30707-9 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Rovatsos | 1 | 767 | 73.71 |
| Kai Paetow | 2 | 4 | 1.13 |