A Four-Label-Based Algorithm for Solving Stable Extension Enumeration in Abstract Argumentation Frameworks

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Authors presented an algorithm for stable extensions enumeration in abstract argumentation framework. The topic of the manuscript is in the area of intensive development efforts for natural language processing maybe most useful in AI applications. Authors presented a detailed description of the algorithm and experimental results. The manuscript is well-written and this review finds that there is not much to be revised. The manuscript can be accepted after minor revision.

My comments are as follows.

1. Lines 4```17 and 418:

"From Table 1, it can be concluded that the four-label algorithm outperforms the other two algorithms across all three benchmarks."

1) In Table 1, there are only two benchmarks. Please include experiments on ICCMA 2021.

2) For consistency, please do the same for Table 2.

2. Figures 3 and 4:

1) Most of the solved instances in ICCMA 2023 experiments have been solved very quickly. The performance curve gets quickly saturated with time. In my opinion, the proposed algorithm is not suitable (or practical) for those instances that take lots of time to be solved (those on the flattened region of the curve), and so one has to use (or invent) a different (or new) algorithm for these instances. Please add some comments.

2) Please add some explanation on the nature of instances that take an excessive amount of time to be solved.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors The research presented in this paper concerned the computation of stable extensions in abstract argumentation frameworks. The authors have proposed a four-label enumeration algorithm for stable extensions. The relevant research works have been reviewed and on that basis the research problem was formulated. The authors have conducted comprehensive analysis of the existing two-label and three-label enumeration stable extensions methods. Their limitations were analyzed and on that basis, a new four-label method was proposed. The conducted experiments were aimed at efficacy verification of the proposed approach and comparison with the results obtained by other methods. Publicly available benchmarks were used during experiments. Please address the following issues: 1. Which limitations of the proposed algorithm caused that only 6 instances of the ICCMA 2021 dataset were solved? 2. In the Conclusions section, the authors mentioned that "there is still a gap compared to popular reduction-based methods". Were such experimental comparisons carried out? Why were the results not presented? 3. If there is still a gap as compared to reduction-based methods, so what are the advantages of the proposed approach?

 

Comments on the Quality of English Language

Please improve the English language, especially when it comes to grammar and style.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The work presented by the authors is quite interesting and very understandable, I have few suggestions,

1. In conclusion, it is stated that the results demonstrated improved algorithm efficiency, if you can justify with the percentage of improvement in efficiency will be more effective and supportive in this context.

2. If you can elaborate indetail about the ICCMA data sets, will be quite reflective in section 6. Kindly also mention, why these specific datasets are used?

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf