Belief Revision for Physical Robots: Opportunities and Challenges
Bibliographic record
Abstract
We are interested in groups of autonomous agents with partial beliefs about the state of the world, who are able to receives alerts and orders through broadcast messaging. Moreover, our focus is on implementing robot controllers that are based on formal models of belief change developed in the Knowledge Representation community. Hence, we develop robots that use sets of propositional formulas to represent their beliefs and AGM revision operators to change their beliefs when they receive new information. The only way to communicate with the robots is through broadcast messaging; hence all robots receive every message at the same time. In this setting, there is a computational challenge in finding the right announcement to get each robot to perform the right tasks; this is known as the announcement problem. This problem has been explored in a theoretical setting and even in the setting of virtual agents in a two dimensional world. In the present paper, our goal is to move this investigation to the physical world. We set up an experimental environment involving several robots that each are capable of performing belief revision. We implement announcement finding in the usual way, but communicate the announcements through sound that each robot senses independently. We evaluate our framework as a proof of concept for more sophisticated robot control problems. Our goal is to demonstrate how explicit formal models of belief can be effectively used to create interesting behaviour in a multi-robot setting; hence, we are trying to connect two research communities working on similar problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".