| paper | d4-conf-ijcai-2015-0731 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
The named theorems establish closure and semantic translations, not an algorithm, complexity bound, hardness result, or solver formulation used computationally. The NP-complete knapsack and TSP tasks motivate an example but are not results about the combined preference-based system; the paper explicitly leaves computational issues for future work. Therefore bit (a) fails, regardless of whether a cohort logistics problem could be devised.
fails bit a — no named computational result to mirror
No result is covered by a continuous mirror. Theorem 1 gives a closure property, Theorems 2 and 3 give semantic correspondences, and the NP-complete knapsack and TSP tasks are motivating examples rather than results about the combined preference-based system.
I don’t see an eligible computational anchor, so I can’t make a compliant positive case from this paper. It gives no numbered result classifying the complexity of a problem as NP-hard, polynomial-time solvable, FPT, or similar; the conclusion says computational issues are future work.
The nearest numbered results are semantic:
The paper does mention that the Multiple Knapsack and TSP components in Example 1 are NP-complete. But that is an unnumbered motivating example, not a result about the combined preference-based problem. It also cites a prior algorithm for modular systems without stating a complexity result for it.
A plausible setting to explore would be a logistics provider serving many recurring cohorts of similar orders: a type would include the complete order, route-network, and preference data; mass would be the fraction of deployments of that type; decisions would assign plans to the cohorts. But the paper specifies no population-level objective or aggregate constraints for that problem. Adding them would create a new computational question, not mirror a result proved here.
The weak point is therefore decisive: the paper studies how to represent and combine modules and preferences for a search problem, not a computational problem over a population. There is no lead continuous mirror anchored in a named computational result of this paper.
The proponent’s central point is decisive under ChoCo’s paper-grounded test: the paper proves no computational complexity result to continuize. Theorem 1 is closure of the P-MS representation under module operations. Theorems 2 and 3 translate preference relations from cp-nets and preference-based planning into that representation. None classifies the complexity of finding a preferred expansion or solving a combined system; the paper explicitly leaves computational issues for future work. Those three results therefore supply no computational anchors.
Example 1 does mention NP-complete Multiple Knapsack and TSP components. The strongest rescue would be a logistics provider serving many recurring cohorts of similar orders, with a per-capita fleet and a mass-based objective for choosing feasible, preferred plans. That could be a worthwhile continuous optimization problem. But the paper gives no population-level objective, shared-resource model, or rule for aggregating its strict partial-order preferences. If each deployment is independent, mass just repeats separate search tasks. If deployments compete for shared resources, the coupling and objective create a new problem. Either way, this would not continuize a result the paper established.
The model’s possible infinite domains do not supply the missing population axis: each task still starts with an instance structure and seeks an expansion. Likewise, its references to several agents or users concern combining modules, not representing a distribution of interchangeable people whose preferences must be aggregated.
So the negative case is strong for a ChoCo mirror anchored in this paper. It is weaker if read as the absolute claim that no worthwhile follow-on could ever be built: a cohort-based logistics problem might be worth studying. But that would be new computational work inspired by the example, not a continuous mirror of the paper’s results.
The adversarial triple: the proponent anchors on up to three named results; the opponent sees that case and must defeat every anchor; the judge decides which case convinced it. These are the pipeline’s own outputs, generated by tools/triple_run.py — no human edited them. The paper’s own text is not reproduced here beyond the quoted statement above.