PONE-D-23-31194An Appraisal-Based Chain-Of-Emotion Architecture for Affective Language Model Game AgentsPLOS ONE

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper is based on An Appraisal-Based Chain-Of-Emotion Architecture for Affective Language Model Game Agents. Thus, this paper is directly related to the theme of this journal. Overall, the paper is organized properly; the concept and future research directions are extensively explained. So, the paper is accepted after following minor changes:

1. Problem of paper and motivation is not clear in introduction

2. Contribution of paper is not clear and not given in bullets

3. Paper contains few grammar mistakes which will be cooperated in final version.

4. Only 67 references are added in paper, but more than 75 references so to attract readers add few latest references related to this paper, which is mentioned below

Laghari, Asif Ali, Hui He, Kamran Ali Memon, Rashid Ali Laghari, Imtiaz Ali Halepoto, and Asiya Khan. "Quality of experience (QoE) in cloud gaming models: A review." multiagent and grid systems 15, no. 3 (2019): 289-304.

Laghari, Asif Ali, Kamran Ali Memon, Muhammad Bux Soomro, Rashid Ali Laghari, and Vishal Kumar. "Quality of experience (QoE) assessment of games on workstations and mobile." Entertainment Computing 34 (2020): 100362.

Madiha, Hina, LiHui Lei, Asif Ali Laghari, and Sajida Karim. "Quality of experience and quality of service of gaming services in fog computing." In Proceedings of the 2020 4th international conference on management engineering, software engineering and service sciences, pp. 225-228. 2020.

Laghari, Asif Ali, Sana Shahid, Rahul Yadav, Shahid Karim, Awais Khan, Hang Li, and Yin Shoulin. "The state of art and review on video streaming." Journal of High Speed Networks Preprint (2023): 1-26.

Laghari, Asif Ali, Xiaobo Zhang, Zaffar Ahmed Shaikh, Asiya Khan, Vania V. Estrela, and Saadat Izadi. "A review on quality of experience (QoE) in cloud computing." Journal of Reliable Intelligent Environments (2023): 1-15.

Reviewer #2: - Introduction needs to be revised, including the problem identification and research gaps.

- Methodology is the not discussed systematically.

- Expand the critical results in the conclusion. Focus on the main developments in the finale. Also, write the main contributions in the conclusion.

- Results are not described properly.

- All figures have low quality, and please improve all of them.

- The article needs to be a review of grammatical errors.

Reviewer #3: This paper provides a study that is of great interest in the gaming field and increasingly in the VR field. The comparison of the three level of experiement give a siginificant understanding of future possibilities. However, there are still some parts that could be improve to make this paper more clear and also readable to a bigger audience.

1) For the few studies, are the three studies done on the same experiement? Or are they studies of three different experiments? The whole experiment process is not very concise and spreaded around the whole paper. I would suggest to put them at the end of chapter 3 before going into details.

2) This paper compares three different types of situation, no memory as basis, and then memory and appraisal. Since the key of this paper is about the appraisal and the memory, a bit more explanation and diagrams to show the difference between the two will be very necessary.

3) The language and words might be also an affecting factors, it might not be part of the key research of this paper, but I believe some explanation or observation should be given to explain how the results are putting this aside at this stage.

4) The design of the UI as well, it is good that the author provide a screenshot but no explanation was done with respect to the interface. There will definitely be some influence in what the participants see on the screen as well. It will be better if the author put that into account for the discussion.

Reviewer #4: The paper explores using large language models (LLMs) to develop believable and interactive artificial agents that simulate human emotions. Based on psychological appraisal research, the study presents a new chain-of-emotion architecture for emotion simulation in video games, which outperforms standard LLM architectures in user experience and content analysis metrics, Below are my comments.

Comment #1:

The paper should clarify the LLMs used in the study and expand on the testing scenarios. A description of the models' training datasets, limitations, and capabilities would greatly help in assessing the validity of the results. Testing the proposed Chain-of-Emotion architecture across varied gaming genres and contexts is recommended to strengthen the argument of its general applicability.

Comment #2:

The claim that the new architecture outperforms "standard LLM architectures" lacks a clear benchmark. The study should specify the other LLM architectures used for comparison, detailing their design and the metrics on which they were evaluated. This would provide the reader with a clearer understanding of the proposed architecture's relative performance.

Comment #3:

While user-rated metrics provide valuable insights into the user experience, the study should aim to balance these with objective measures where possible. The diversity and representativeness of the user group should be disclosed to ensure the reliability of these subjective metrics.

Comment #4:

The paper could further acknowledge the complexity of human emotions and how this complexity poses a challenge for AI simulation. There could be a discussion about how architecture accounts for or falls short in simulating the full spectrum of human emotional responses.

Comment #5:

It is suggested that the study references a broader range of psychological theories to ensure that the architecture isn't overly dependent on a narrow set of assumptions. The implications of basing LLM outcomes on these observations should be thoroughly discussed.

Comment #6:

The study should provide a clear, transparent methodology that allows other researchers to replicate the work. This includes detailed descriptions of the game scenarios used, the nature of the emotional responses evaluated, and the precise nature of the chain-of-emotion mechanism.

Comment #7:

Additionally, how to enhance the game agent architecture to better compete with other LLM frameworks such as langChan, Llama Index, Autogen, etc. that boast advanced modules like memory, chains, agents, callbacks, security, and integration capabilities:

• While the architecture's focus on emotional intelligence within conversational agents is commendable, it could benefit from incorporating more sophisticated memory modules, similar to those seen in competing frameworks. Enhanced memory capabilities would allow for a more nuanced understanding of context over longer interactions, which is crucial for maintaining coherent and emotionally appropriate responses. Consider adopting or developing memory structures that can handle complex conversational threads without losing the emotional thread of the interaction.

• The game agent architecture has potential, but to truly compete, it should look into creating or integrating more complex chain mechanisms. Chains that can manage sequences of interrelated tasks would provide a significant edge, enabling agents to handle multiturn dialogues with more awareness and anticipation of user needs, thus improving the emotional engagement in conversations.

• Your approach to developing emotional intelligence in game agents is innovative; however, it might be beneficial to include more robust agent management and callback functions that other frameworks offer. These would allow for better event-driven interactions, which can result in more dynamic and responsive emotional behaviors in real-time, leading to a more immersive user experience.

• Security is an increasingly vital concern in AI applications. To enhance the competitive edge of your architecture, it is crucial to integrate state-of-the-art security protocols to ensure user data, especially as it pertains to emotional data, is handled with the utmost care. This would not only increase trust in your system but also align with best practices in responsible AI development.

• Integration capabilities are a standout feature in existing LLM frameworks. To bolster your architecture's marketability, provide clear and streamlined processes for integrating with popular LLMs, databases, and external APIs. Ensuring your system can easily fit within different tech stacks will be key to its adoption.

• Lastly, the game agent architecture should refine its context management system. While handling emotional responses is your architecture's unique selling point, the ability to maintain and leverage context effectively over the course of long interactions is what will truly enhance its practicality and appeal. Better context management can lead to more personalized and accurate emotional interactions, which is paramount for user engagement.

By addressing these comments, the study can significantly improve its scientific rigor, relevance, and potential impact in the field of AI-driven emotional simulation within interactive media.

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Reviewer #1: No

Reviewer #2: Yes: krishna kumar mohbey

Reviewer #3: Yes: Sky Lo

Reviewer #4: No

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An Appraisal-Based Chain-Of-Emotion Architecture for Affective Language Model Game Agents

PONE-D-23-31194R1

Dear Dr. Croissant,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Michal Ptaszynski, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #4: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: N/A

Reviewer #4: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: authors done good work, quality of paper is improved and revised paper according to suggestions so I recommend this paper as accept

Reviewer #2: (No Response)

Reviewer #4: The author has meticulously addressed every one of my comments and concerns, demonstrating a thorough and thoughtful consideration of the feedback provided.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #4: No

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