Paper: Enhancing Human-AI Collaboration Through Logic-Guided Reasoning (ICLR-2024)

Reviewer comment (Novelty):
More discussions comparing the proposed approach to related studies need to be provided. Otherwise, it is not easy to understand how we can distinguish the proposed method from existing frameworks, and providing them would help readers understand the literature clearly.

Author response:
In our original submission, **because of the page limitation in the main paper, the description of the related work is provided in the supplementary material Section 2**. It includes a comprehensive overview of the literature (including behavior understanding, logical reasoning, and human-robot interaction) related to our work. We apologize for not including the related work directly in the main document and have added it into our revised manuscript.

Reviewer comment (Writing):
Some parts of the method explanation are somewhat hard to follow. In Sec 2.3, the query is introduced as $\mathbf{v} = (\mathbf{a}$, $\mathbf{s})$, but there is no specification for these variables. It is supposed that this is a tuple of a sequence of actions and a sequence of states, but that should be noted explicitly. An intuitive explanation of what $\mathbf{v}$ stands for would help readers.

Author response:
Thank you for your feedback. The query, denoted as v=(a, s), actually represents a tuple consisting of an action and a state. We have added specifications for these variables in a revision.

Reviewer comment (Writing):
In Eq. (5), it is written as $g \in f$, but this may not be a standard notation because here $f$ is a rule, not a set.

Author response:
Thank you for your suggestion. In our formulation, although f represents a rule, it is also conceptualized as a set comprising all the predicates that constitute the rule. This dual characterization allows us to employ set notation to encapsulate the composite elements of the rule f. Moreover, we also add [1,2] in the related work and analyze their novelty clearly.

[1] Artur d'Avila Garcez, Luís C. Lamb: Neurosymbolic AI: the 3rd wave. Artif. Intell. Rev. 56(11): 12387-12406 (2023)

[2] Henry Kautz. The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture. AI Magazine, 43(1), 105-125 (2022).

Reviewer comment (Novelty):
It is suggested to include the literature on neuro-symbolic research (e.g., [1,2]), since the proposed approach is significantly related to the field, and the community would benefit from this work. The readability of the method explanation could also be improved.

Author response:
Thank you for your suggestion. In our formulation, although f represents a rule, it is also conceptualized as a set comprising all the predicates that constitute the rule. This dual characterization allows us to employ set notation to encapsulate the composite elements of the rule f. Moreover, we also add [1,2] in the related work and analyze their novelty clearly.

[1] Artur d'Avila Garcez, Luís C. Lamb: Neurosymbolic AI: the 3rd wave. Artif. Intell. Rev. 56(11): 12387-12406 (2023)

[2] Henry Kautz. The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture. AI Magazine, 43(1), 105-125 (2022).

Reviewer comment (Theory):
Including the time and location in the predicate (in Eq. (4)) would generate a large number of ground atoms, and typically, reasoners (including probabilistic ones such as ProbLog and Markov Logic Networks) need to compute ground atoms to perform reasoning. Does the proposed framework suffer from the large number of logic representations to be generated in the inference? If not, how does it avoid this problem? Are there any restrictions over the considered language/environments?

Author response:
Yes, the proposed framework suffers from the large number of logic representations to be generated. The best set of logic rules is approximately obtained by sampling and preserving the top-K rules according to their posterior probabilities. **Note that in the supplementary material section 5**, we have demonstrated the optimization of our proposed algorithm, which clearly describes the calculation of the posterior probabilities of the latent rule and the selection of high-quality rules from a large number of logic representations.  This sampling strategy, which focuses on the most probable logic rules, allows our framework to operate efficiently without compromising the integrity of the inference process.

Reviewer comment (Writing):
Different examples of rules are inconsistent. In (3), an item of a logic rule is `Walk_to(person, bedroom)`, but the examples of rule 1 in Fig.3 contain an item `Walk_to(plate)`. It is inconsistent in whether `Walk_to` needs a person as the first entity argument. I wonder which setting is actually used in the method.

Author response:
**Q2:Different examples of rules are inconsistent.**

A2:Thank you for your valuable feedback. In our setting, the command “Walk_to” needs a person as the first entity argument. We have rectified all ambiguous instructions in the paper.

Reviewer comment (Presentation):
(Minor) In tables, the best results are not easy for audiences to discover. Please use bold text for the best results, and show ↑ or ↓ for each metric to indicate whether greater or less is better.

Author response:
**Q3:minor errors.**
 
A3: Following the reviewer’s suggestions, we have modified all tables and added arrows (↑ or ↓) next to each metric to indicate whether a higher or lower value is considered better. These modifications will help readers quickly identify the best results and understand the direction of improvement for each metric.

Reviewer comment (Writing):
(Minor) There are a few confusing wordings. What is the meaning of "hardness level" in the caption of Tab.1 and Tab.2? Its meaning is more like "non-softness" than "difficulty".

Author response:
**Q4:There are a few confusing wordings. What is the meaning of "hardness level" in the caption of Tab.1 and Tab.2? Its meaning is more like "non-softness" than "difficulty".**

A4: We follow [1] to split our datasets into four hardness levels, and the gaps between levels correspond to different challenges. We apologize for any misunderstanding caused by the wording and make the necessary adjustments in the captions of Table 1 and Table 2 to ensure clarity and avoid any further confusion.

[1] Wan Y, Mao J, Tenenbaum J. HandMeThat: Human-Robot Communication in Physical and Social Environments[J]. Advances in Neural Information Processing Systems, 2022, 35: 12014-12026.

Reviewer comment (Presentation):
The authors should provide the related work in the main text rather than in the appendix. The related work can help readers who are not familiar with this field quickly gain background information about this work and its positioning. Furthermore, I recommend adding a section in the related work that focuses on human-AI collaboration, as it is highly relevant to the topic of this paper.

Author response:
A2:Thanks for your recommendation. By highlighting the existing research and advancements in human-AI collaboration, we can demonstrate the significance of our work and the contributions we are making to this particular area. We have added related work that focuses on human-AI collaboration into our revised manuscript.

Reviewer comment (Presentation):
Presentation: (a) I suggest that the authors highlight the best results in Table 1 and clarify in the caption whether each metric is better when higher (success rate) or lower (average number of moves). (b) Watch-and-help is cited twice in references. (c) Citation format like WAH Puig et al. (2020) should be WAH (Puig et al., 2020). Use `\citep` command in latex.

Author response:
A3: Thank you for your valuable suggestions. I have certainly incorporated your recommendations to improve the clarity and formatting of our paper.

Reviewer comment (Experiments):
Although the author has demonstrated in the experiments that the method in the paper is indeed effective, one point still puzzles me: I understand how theory of mind can assist in improving the effectiveness of human-AI collaboration, but why does logical reasoning help enhance human-AI collaboration? Is it merely because logical reasoning is effective in all similar tasks (i.e. reasoning) rather than just theory of mind or human-AI collaboration? If it is effective in both, why not use this framework for accomplishing more reasoning tasks? I hope the author can provide some analysis or experimental data to elucidate the relationship between logical reasoning and human-AI collaboration.

Author response:
A4: Logical reasoning allows humans and AI systems to interpret and understand each other’s actions, intentions, and reasoning processes. By applying logical rules and principles, both parties can analyze and make sense of the information exchanged during collaboration. This shared understanding enables effective communication and coordination. It ensures coherence and consistency in the collaboration process. When humans and AI systems apply logical principles, they can identify and resolve contradictions or inconsistencies in their respective actions or decisions. This helps in aligning their efforts and maintaining a coherent and productive collaboration. By employing logical thinking, both humans and AI systems can break down complex problems into smaller components, identify patterns, and derive logical deductions to arrive at optimal solutions. We have added more analysis and reasoning experiments (e.g, overcooked games) in the supplementary material to elucidate the relationship between logical reasoning and human-AI collaboration.