A SNePS Approach to The Wumpus World Agent or Cassie Meets the Wumpus
Bibliographic record
Abstract
We demonstrate the use of SNeRE, the acting component of the SNePS knowledge representation, reasoning, and acting system, by showing its use to implement a wumpus world agent [Russell and Norvig, 1995]1. For this purpose, we use SNePS 2.6.2, which consists of SNePS 2.6.1 [Shapiro et al., 2004] plus some patch files. We usually name our SNePSbased agents Cassie [Shapiro, 1989; 1998; Shapiro and Ismail, 2003; Shapiro et al., 2000; Shapiro and Rapaport, 1987; 1991]. To distinguish Cassie in the role of the wumpus world agent, we will call her CassieW. Our main motivation in developing intelligent systems is to model general human-level intelligence, not to maximize the use of computing power to optimize problem solving. CassieW has been developed accordingly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.023 |
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 source (direct Gemma or distilled Codex), 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".