Enhancing Human-AI Collaboration Through Logic-Guided Reasoning
Authors: Chengzhi Cao
Yinghao Fu
Sheng Xu
Ruimao Zhang
Shuang Li
Venue: ICLR 2024 poster (2024)
Keywords: Logic rule
human-robot interaction and collaboration
Primary area: applications to robotics, autonomy, planning

Abstract: We present a systematic framework designed to enhance human-robot perception and collaboration through the integration of logical rules and Theory of Mind (ToM). Logical rules provide interpretable predictions and generalize well across diverse tasks, making them valuable for learning and decision-making. Leveraging the ToM for understanding others' mental states, our approach facilitates effective collaboration. In this paper, we employ logic rules derived from observational data to infer human goals and guide human-like agents. These rules are treated as latent variables, and a rule encoder is trained alongside a multi-agent system in the robot's mind. We assess the posterior distribution of latent rules using learned embeddings, representing entities and relations. Confidence scores for each rule indicate their consistency with observed data. Then, we employ a hierarchical reinforcement learning model with ToM to plan robot actions for assisting humans. Extensive experiments validate each component of our framework, and results on multiple benchmarks demonstrate that our model outperforms the majority of existing approaches.

Published as a conference paper at ICLR 2024
ENHANCING HUMAN-AL COLLABORATION THROUGH LOGIC-GUIDED REASONING
Chengzhi Cao Yinghao F Xu', Ruimao Zhang', Shunng Li 'Te Chinese University of Hong Kong (Shenzhen) University of Science and Technology of China
caanngzhica@maalle wust.......... edu. en, yinghaofullink cuhk. edu. cn, shengxule & Link. cuhk. edu. en, zhangruimao@ cuhk. edu. on 1 lishuange cuhk. edu.en
ABSTRACT
We present a systematic framework designed to enhance human-robot perception and collaboration through the integration of logical rules and Theory of Mind (ToM). Logical rules provide interpretable predictions and generalize well across diverse tasks, making them valuable for learning and decision-making. Lever- aging the ToM for understanding others' mental states, our approach facilitates effective collaboration. In this paper, we employ logic rules derived from obser- vational data to infer human goals and guide human-like agents. These rules are treated latent variables, and rule encoder is trained alongside multi-agent system in the robot's ' mind. We assess the posterior distribution of latent rules us- ing learned embeddings, representing entities and relations. Confidence scores for each rule indicate their consistency with observed data. Then, we employ a hierar- chical reinforcement learning model with ToM to plan robot actions for assisting humans. Extensive experiments validate each component of our framework, and results on multiple benchmarks demonstrate that our model outperforms the ma- jority of existing approaches.
INTRODUCTION
Humans are natural mind-readers. The capacity deduce what others thinking or wanting from brief nonverbal interactions, called Theory of Mind (ToM), is crucial to our social lives (Mao et al., 2023 Fuchs et al., 2023). We know not only how take actions to interact with the world but, moreover, how to purposefully influence the thoughts of other humans (collaborators or opponents) to achieve our goal (Chandra et al. 22200). This intellectual ability enables humans to work smoothly and strategically with other humans, which distinguishes humans from other lower-level animals. An intriguing question arises:
Could we endow Al agents with this ability so that the Al agents can be more intellectually human- like?
Particularly in a human-Al collaborative task setting, if Al agents can adeptly reason about the unobservable mental states humans, they could engage more effectively in cooperative tasks, facilitating scamless Auman-AI interactions (Rabinowitz et al. 2018 Grassiotto & Costal 2021). For example, self-driving car equipped with the ToM can better communicate with other human drivers nonverbally. Faced with sophisticated or ambiguous traffic scenarios, ToM enables self-driving cars to reason about the mental states, specifically the beliefs, of human drivers sharing the road (Patel) Chernoval 2022). For another instance, suppose one day aim develop an AI agent aid and take care of elder people, it requires understanding human nonverbal communication such as hand gestures, or eye contact, understand the intentions or desires of humans. An agent equipped with ToM is endowed with the ability to interpret these cues more effectively. To achieve this goal, the robots must develop two key skills: 1) comprehend human behavior (called social perception);
"quual Contribution Corresponding author

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Published as a conference paper at ICLR 2024
X-Beity Yroup Zusen
MinsoyKKAAOOYYaalAAAllimiio Is BipnikZ)--DriiiZ222)
X L-Lyyy
Y-plate Z-milkk
Scane 2
Figure Using logical rules (shown in the black box) for human action perception.
2) reason critically about the environment and organize its actions to work with humans (Puig et al. 2020). Researchers typically refer the two agents engaged in behavior modeling as "actor" and "observer". The actor behaves based on his own perception of the world, then the observer watches the actor and forms an understanding of the mental state held by the actor and/or the resulting actions that the actor intends to take. In the simplest case, the actor behaves deterministically. and the observer has full knowledge of the world external to the actor (Wei et al. 2018). In more complex
cases, observers can have partial knowledge, and agents can be both actors and observers at the same time, simultancously modeling each other.
Complex social movements often have underlying logic that is clear and generalizable. Logic rules serve as high-level representations for reasoning, and guiding actions in specific scenarios (Duan) et al. 2022 Yang et al. 2017). These interpretable rules not only aid in understanding inference outcomes but also enhance robustness diverse situations. The burgeoning field of neurosymbolic AT exemplifies this trend, merging neural networks with Symbolic Al to foster robust and adaptable models. It maintains the validity of logic rules across various contexts without retraining, contrasting with methods reliant on specific entity embeddings (Kautz) [022] d' Avila Garcez Lamb (020). Figure [ illustrates an example of rules. If new facts about more scenes or locations are added the knowledge base, the rule about "Drink(person, water)" will still be usefully accurate without retraining. The same might not be true for methods that learn embeddings for specific knowledge base entities.
In this paper, we leverage probabilistic logical reasoning to generate generalized knowledge about an agent's goal new environments, enhancing social perception and human-AI collahoration. This approach, grounded in probabilistic logic, is adept handling uncertainties and modeling complex data. Our methodology intertwines perception and collaboration learning, where perception informs logic rule learning, and logical reasoning guides collaborative efforts. This reciprocal enhancement offers two key advantages: (i) Rule-based Probabilistic Social Perception: Our model treats each hidden rule latent variables based probabilistic assessments. These rules capture both object relations and event sequence constraints. (ii) Dynamic Collaboration: Our human-like agent adapts its subgoals and beliefs in real-time, guided by the latest observations and logical rules. It formulates and executes concurrent plans alongside other agents, ensuring robust task performance partially observable environments and generating human-like behaviors.
2 RELATED WORK
Behavior understanding. The task of recognizing and interpreting human activities faces sig- nificant challenges due to the variability in execution styles and the complexity of visual stimuli. Previous research primarily centered on visual behavior modeling (Chen al., 2021 and trajec- tory prediction (Cao et al., [0033] Gupta et al. 2018) Alahi et al.] | Chandra et al. (020). with
some studies focusing on group activities and plans (lbrahim et al. 2016 Shu et al. 2015 Choi & Savarese 2013). Our approach diverges from these works by focusing on activity understanding that infers mental states (e.g.. intentions, desires) from behaviors, aligning closely with computational Theory of Mind (Jara-Ettinger 2019; Rabinowitz et al.] 2018, Nguyen et al., 2022). We aim to synergize behavior understanding with logical reasoning, enhancing the machine's 's comprehension of human actions through a unified, interactive framework.
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Logical reasoning. The integration of logical reasoning with deep learning architectures has gar- nered considerable interest (Kautz] 2022 d Avila Garcez & Lamb] 2020). Most existing algorithms develop differentiable modules based known relationships for deep networks learn (Zhou, 2019 Mao et al. 2023 Evans & Grefenstette 2018). Innovations in this field include learning nu- merical rules from knowledge graphs (Wang al., 2019h), differentiable maximum satisfiability solvers (Wang et al.) (019a). and neural-sy -suallsymmolli reasoning models (Tang et al. (022). However, these models often simplify relations and rely on basic triplet-based embeddings. Our approach ad- vances this field by embedding more intricate first-order logical rules and jointly modeling entities and relations.
Human-robot interaction. Historically, human-robot interaction studies have been confined to controlled lab settings (Goodrich & Schultz, 2007; Dautenhahn 2007). Notable developments in- clude frameworks for learning from joint-action demonstrations (Nikolaidis al. 2013) and modu- lating robot behavior based on user actions (Rozo et al. 2016). Other approaches, like natural lan- guage interaction (Gervasi et al. 2020 Ogenyi et al.) 2021), focus on creating user models within a Partially Observable Markov Decision Process (POMDP) framework. Our research departs from these methods by utilizing logic rules to infer human goals and efficiently associate new users in diverse environments.
3 MODEL
3.1 PROBLEM STATEMENT
In this system, each individual represents an integrated agent who formulates their actions based on their individual goals, their beliefs about the possible actions of another agent, and a complete observation of the environment. The robot, on the other hand, has access to a single demonstration of human performing the same task in the past, as well as a complete observation of the environ- ment. Consequently, at each step, the robot receives observations from the system and issues action commands control the avatar within the environment. Formally, each task is defined by the per- son's goal g (a set of goal predicates), demonstration of Alice taking actions to achieve that goal D = {s a² } (a sequence of states s² and actions a²) and a new environment where the robot collaborates with the person and help achieve the same goal quickly as possible. During training, the ground-true goal of the person is shown to the robot as supervision; during testing, the robot no longer has access to the ground-truth goal and thus has to infer it from the given demonstration.
3.2 LOGIC-BASED MODEL
We regard the trajectories and actions of each agent sequence of events. In general, the logic rule our paper is defined logical connection of predicates, including property predicates and spatial-temporal relation predicates. The property predicates illustrate some intrinsic properties of objects, such as "move slowly", and the spatial-temporal relation predicates (e.g. "book" is inside the "bookshelf") are introduced to define the spatial and temporal relations of two entities. For example, a sensible rule will look like:
W watchhperson, TV) -W alk tok bedroom) A SwanmnnnSStshootoo Swritch.on(persom.TT TV)
(I)
A Sil_on(person, sofa) A Move.slouly(person
which means that a person walked slowly into the bedroom, switched on the television, and on the sofa. From this, we can infer that the person intends to watch TV. This logical rule may seem simple, but it applies to various different scenarios because most people, when wanting to watch TV, go through these stages.
We aim to develop a model that is informed by logical reasoning, with the capability to predict and rationalize the agent's actions. This model utilizes a query vector (a,s), s). comprising the action a and the state s, along with the historical trajectory тi observed up to the current timestep t. The integration of these elements enables our model to provide insightful explanations for the agent's behavior within the given context. Assume that for each possible symbolic state g E G. where serves as the head predicate and represents the set of head predicates, propose to infer the
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Syntholi
lafer goal:
kinten
Couppee 18:4)
Actiam fromt
rermat policy and
Uystann Duiley
legit ruln:
Spea] <Be doct
Freeding
[al] saple
Rule Encoder
Scenc
Fladlng
Syntholic Coupph Solcoaaii
Footback
suplc
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The overview of proposed framework. In the first stage, given the historical motion trajectory of a person on a scene, we first utilize the symbolic graph to represent objects as nodes and the spatial relationship between them edges. The rule encoder generates logic rules with their the person was trying to achieve. In the second stage, the Al agent is asked to work together with the person to achieve the same goal in environment. The scene is also encoded as graphs, which will pass through the propagation module to generate the Al agent's actions based on its latest observation and logic rulcs.
Figure 2:
to infer the goal iteratively that
predicate that is conditional on history and query as E(g v. ve).. Suppose there is rule set Fg and all rules will play together to reason about the occurrence of For each f E Fg.- one can compute the features as above. Given the rule set FØ, we model the probability the event g as follows:
Palg (v, Te) x exp (Eer,, as a (.lolv,n vi))
We optimize the logic-based model by maximizing the likelihood of data as B(a.)))) [log Ep, [Pa (g v, r.)]],, where f and a are learning weights. The detailed learning process and graph propagation module can be found in the Appendix. Then, we can use these interpretable logic rules to reason the actions of agents in the ToM collaboration framework.
3.3 HIERACHYCAL TOM COLLABORATION FRAMEWORK
Iterative Goal Inference By leveraging the trajectory information up to the current moment, we derive the probability distribution for the next potential predicate state, expressed
exp EIEF af as E 8" s',a'H,,")
g" arg max.aaaallll d'y, ) arg max 9
(3)
Eg exp (E af Eg 8 st,0' oHH,
Here, g" denotes the most probable goal inferred by the robot, which both the robot and the human strive to achieve within the next time horizon. Additionally, a' signifies the human action time
t. F, represents all possible rules in the rule set, and Eg exp (ErEF,) a Ex(-)) is the partition function used to aggregate the probabilities of paths leading to the same goal.
The primary objective of this collaborative framework between the human and the robot is identify the policy function я for the robot and assist people in accomplishing tasks within their capabilities. Additionally, the robot is required to consider the human's 's cognitive processes and construct men- tal model of the human. This model enables the robot to anticipate the human's 's policy and enhance collaboration through deeper understanding and consideration.
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Policy Estimation To formulate the policies for both the robot agent and the human agent model in the robot's mind, we employ the Boltzmann rationality model, where 3 serves as the rationality coefficient for the agent. This coefficient quantifies the extent to which the agent's choices are focused around the optimal decisions. As / approaches infinity, the agent behaves in a manner akin perfectly rational agent, meticulously optimizing its choices. Conversely, agent becomes increasingly indifferent to the quality of Q. The collaboration is purely decentralized, with all agents acting based solely on their local observations and without any direct communication, the agent i can be
approaches 0, the
except for ToM inferences from trajectory information. So the policy function for expressed as follows:
exp(3 Eumo b' bl(w (')Qg(a,s)) Tgasa st w w", = En'E exp(3 Ewra b( (')Qg(a', s'))
Here, 6 (w- 7") represents the agent's 's belief, a probability distribution used to estimate the private state wis; of the other agent based on this agent's trajectory. Q, st) is the Q-function, dependent on the state and action, and can be represented as:
Qo(a, s²) = =EES,, a Eg(g
a. (T))
(5)
is worth noting that our policy function and Q function are all based on the current goal g in the rule fgg E F. In fact, one policy and Q-value for each subtask in the hierarchy: = (Togs.... Ts.. and Q = [Q yo: ..... Qg... ). In each subtask (one rule in the rule set), the robot and the human need to take a set of primitive actions to reach the terminal states specified by this rule. Then, the next goal can be inferred and is conditioned on the current history and the next primitive action that either the human or the robot expected to take. Our approach employs a hierarchical policy execution strategy, akin to standard programming languages that use stack discipline. Each subtask policy receives a state as input and returns the name of subtask to invoke. When a subtask is invoked, its name is pushed onto the Task-Stack, and its policy is executed until it reaches one of its terminal states. Once a subtask terminates, its name is popped off the Task-Stack. Importantly, if any subtask currently on the Task-Stack terminates, all subtasks below it are immediately aborted. Control then returns to the subtask that initially invoked the terminated subtask. This hierarchical approach enables the dynamic execution of subtasks and completes the final goal in manner like online inference.
Belief Update However, in a ToM collaboration framework, the Q-function of each agent model should include two additional arguments to account for both agents' intentions and the estimated belief of the counterpart. The intentions of these two agents are considered as prior knowledge and are drawn from Ω = {Ωz, 22r} respectively, where U2 represents the robot's 's intention, and f2 represents the human's 's intention in the robot's mind. Besides, we employ the obverter technique Choi et al., 2018) to enable both agent models to hold a belief b_, as an estimation of the other agent's 's belief about their own intention w. To model this nested belief, we do not need to use distribution over the original intention distribution. This is because the belief update process follows deterministic path for rational agents when employing Bayesian inference. At each time step, the update process can be expressed as:
('(w>= 7') = ((w-|/>-3 .a') x के (aa, s", -
(6)
In this equation, A represents the agent's estimation of the other agent's policy, which is learned during centralized training. Our belief update function incorporates counterfactual reasoning. Es- sentially, the agent explores all possible private states w-; and estimates the likelihood of the actions it observed under the assumption that a specific set of private agent states is correct. This approach involves fixing one agent model while training the other, thus avoiding non- con-sttiionartt issues that could arise from simultancous agent updates. Consequently, the other agent model can be considered as part of the environment, ensuring the convergence of the Q-function of the learning agent. The whole algorithm is presented in Algorithm [ where ()(. (1..)) (1,)) represent the parameters of Q-function, policy function, and belief-updater function E, respectively.
Training In Algorithm I Deep Q-learning (Mnih al. 2013) is employed to train the Q-function Qi- Simultaneously, supervised learning is used to train both the policy function a_, and the belief update function E.,. The variable b_;, a result of discretizing b-;. is provided as supervisory input
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Algorithm 1 Adaptive Theory of Mind Collaboration Hierarchical Learning Algorithm
Randomly initialized network parameters (i,())) 4(i,))) ((i,)) (1(1.0) P(aa) &0,,)) learn-
ing rate (nQ. T ; me}
1: for each iteration do
2: for in } do
3:
m for episode = m (where is the number of sub-goals) do
4:
5: repeat
Sample sub sub-goal: g +- p (g st a' Sample an action a +- t (a st we x9) Sample an action a'_ -- R_ st we Update two agent models' beliefs as Eq. until Satisfy the sub-goal end for Vr (.(
6:
7:
8:
9:
10:
r/, (1((8) max.ed. Pibn (a, st,, (,,)),,,.)) B,:(K) (k is the index of trajectory)
11:
I E. 1l (a) :86 ww(l)) ((())) Vt 112 Compute Q-loss
(k) (10
12:
IF Σx,()) (#( (o (12,1)0000) with)) ) ) Compute policy loss
13:
I, (I.(b_-())) (1 (8-="1(0) 1(k) st((1)) Compute belief loss
15:
( (1,,) (1.))} - (1)(0.0) V(,)) ((,))) } (a. Hno L MnL" , Le)
16:
Update (K)i Vra (..)) +- (.)) (6,)) ()EE for Q-learning
17:
end for
18:
end for
to agent i streamlining the training of the belief update function E.,. Three loss terms are optimized sequentially. 77 quantifies the disparity between Q-values, while the second term, L*, corresponds to the softmax cross-entropy loss. The third term, L measures the Kullback-Leibler (KL) di- vergence used to assess the dissimilarity between the predicted partner's belief and the supervised partners' belief b_,.
4 EXPERIMENT
In this section, we provide some implementation details and show ablation studies to evaluate the performance of our framework the Watch-and-help dataset (Puig al., 2020) and Hand- Me'That dataset (Wan et al., 2022). We compare our model with several state-of-the approaches in human-AI interaction, including WAH (Puig et al. 2020). GEM (Netanyahu al.) 2022). DRRN (Hausknecht et al. 2020), Seq2Seq (Sutskever et al. 2014) and ReQueST (Reddy et al. 2020).
For the Watch-and-help (Puig al.) 2020) dataset, we first provide Bob video that shows Alice successfully performing the activity (Watch stage), and then place both agents in a new environment where Bob has to help Alice achieve the same goal with the minimum number of time steps (Help stage). We utilize success rate, average number of movements in successful episodes, and Speedup as our metrics. Speedup means we compare the episode length when the human and AI agent are working together with the episode length when the human is working alone. For the HandMeThaa dataset, we utilize the average score, the success rate(%) that the model achieves the goal within 40 steps, and the average number of moves to evaluate the performance. We split it into four levels depending on the episode length, and run each testing task for five times using different random seeds. For HandMeThat dataset (Wan et al.) 2022), It contains 14 locations and typically more than 200 movable objects, which induces a large set of possible actions. We also split it into four hardness levels, and the gaps between levels correspond different challenges.

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Comparative performance of various rule encoder backbones on the Watch-and-HH -eelp Dataset. An evaluation across four difficulty tiers using three metrics -veerae Number of Moves (AN, lower is better) for successful episodes, Success Rate (SR, higher better), and Speedup (SU, higher is better).
Table 1:
Partially Observable
Methods
Level
Level
Level 3
Level 4
AN 4
15.28:10.06
22.24:0.115
28.88:10.15
51.20:10.12
WAH
SR
78.61
71.02+0.08
60.13+0.11
51.48+0.04
SU
0.17=0.012
0.1410.019
0.1310.016
0.08100016
AN 4
14.08
20.40:10.10
28.71+10.05
42.12:10.04
GEM
SR 1
79.27
71.32++0.04
60.99-10.07
53.97=+0006
SUT
0.24+00017
0.22+
0.19+0.012
0.15+
AN 4
13.47
20.10::0.14
28.68
38.25
DRRN
SR 1
80.8610.09
71.74#0.06
66.27
57.5
SU
0.251
0.1910.017
0.17:00004
0.16900009
AN 4
12.76+0.006
19.92+0.06
28.32
34.16+0..04
Seq2Seq
SR
01.12
72.45
66.69
57.86
SU f
0.000
0.28+
0.26+0.007
0.214
AN 1
12.68+0.03
18.7240.10
18.12
32.92+0.18
ReQueST
SR
81.1210.05
77.44
68.52
55.34
SU f
0.3810.019
0.30+0.015
0,25
0.22+0.011
AN +
11.40=0.00
16.21
26.16
31.10#0.05
Ours
SR
82.43
78.14+0.03
+9.84
58.91
SU
0.485
0.44
0.36
0.33
4.1 QUANTITATIVE ANALYSIS
We compare method with several state-of of-the-art approaches, and Table presents the qualitative results in the Help stage of the Watch-and-help dataset. Note that WAH (Puig et al. 2020) represents hybrid model of RL and planning, where regression planning is deployed to generate plans for each sub-goal sampled from the RL-based high-level policy; DRRN (Hausknecht et al. (020) represents Deep Reinforcement Relevance Network. We observe that our method significantly outperforms all baselines measured by success rate with the fewest number of moves by using the true goals and reasoning about human's future plan, avoiding redundant actions and collisions with him. exceeds the second-best performance of Seq2Seq by 1.05% and 3.15% in Level 4 and Level 3 respectively. Compared with WAH, we also obtained 7.63% and 9.71% improvements. This because the rule encoder and evaluator can collaborate with each other to reduce search space and learn better rules. Figure illustrates an example of collaboration. Our framework can actually predict users' inten- tions and represent some help strategies to achieve the same goal. For the HandMeThas dataset, the performance of learning models in the partially-observal Random model presents an agent that randomly selects valid action at each time step. Obviously, our model performs decently well on all levels in partially-observae settings.
setting is shown in Table Note that the
4.2 VISUALIZATION
We show some visual representations of human-robo collaboration on the Watch-and-help dataset in Figure 4 In this picture, Bob's goal is to put the plate into the microwave, and then watch TV in the bedroom. When he walks towards the plate and grabs it, Alice (human-like agent) predicts his goal as "Inside(plate, microwave)". then walks towards the microwave and opens it to help achieve the same goal quickly as possible, that Bob can directly put the plate into the microwave. Obviously, this event can be properly represented by the first rule, which use the spatial and temporal relation between Bob and surrounding objects infer the human goal and help them to finish it.

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Table Experiment results on HandMeTTht dataset. Each model is evaluated on 4 hardness levels with 3 metrics: the Average Score (AS, higher is better), the Success Rate (SR, higher is better), and the Average Number of Moves (AN, lower better) in successful episodes.
Partially Observable
Methods
Level
Level
Level
Level 4
AS
-40.0
-40.0
-40.0
-40.0
Random
SR
0.0
0.0
0.0
0.0
AN
N/A
N/A
N/A
N/A
AS
-5.1=0.07
-25.3+00.9
13.5
-32.0=+0.20
Seq2Seq
SR
25.50+0.20
10.40+0.11
3.95=+0.23
5.30:t0.26
AN
4.21
4.17
4.19=0.02
4.12:00.06
AS 1
-6.3=0.11
-22.5+0.12
130.9:1.144
-29.6:10.18
GEM
SR f
24.64+10.0
12.47+0.22
5.74++0.15
7.21+0..13
AN
4.34
4.36
4.24
4.22:40.00
AS 1
-40.0
-00.0
-40.0
-0..0
DRRN
SR
0.0
0.0
0.0
0.0
AN
N/A
N/A
N/A
N/A
AS T
-104=00033
-5.8:10.06
-9.1:1.117
-11,990..2
Ours
SR
27.73-10.29
24.81
20.97:1.12
21.661:.118
AN
4.05+
4.09+-0.01
4.14
4.2140.02
Bob alone
Alice helps Bob
Bob's plan: Ingmilk,fiidge) ontsola)
Dob's plan: Infplate, microwave) (indgi))fndgge) on(TV) sit ontsol))
Alice's plan: Intplats, microwave) (njailk,Gidde))
Openttraige) switch on(TV)
Figure 3: The arrows indicate moving directions and the circles with black borders indicate moments when agents interacted with objects. When working alone (left), Alice had to search different rooms; but with Bob's help (right), Alice could finish the task much faster.
Next, Alice sees that Bob moving into the bedroom and getting close the sofa, so Alice infers his goal as "Watch(TV)" based on generated logical rules. Then, Alice walks towards the TV and switches on it, so that Bob can sit down immediately and finish his goal without any redundant actions. As we can strategies to achieve the same goal with the minimum number of time steps.
rule can actually predict users' intentions and represent some help
LIMITATION
The present work has a few limitations. First, for real-world applications, we need to manually set up different spatial-temporal property and relation predicates so that it can infer the graph structure for a large number objects. Also, the quality generated rules mainly depends on the setting of pre-defined predicates. We potentially achieve this by using language instructions to guide the inference. For instance, a human user can provide a language description of the task in addition to the physical demonstration to help achieve a better understanding of the task. Another limitation is that currently we only consider chain rules provided as prior knowledge. In the future, we plan to explore more efficient and effective nule reasoning algorithms and consider more complex rules.
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Rule: Walk to(Bob, plate) Grab(Bob,, plate) A Walk to(Bob, microwave) Open(Bob, microwave) -inside(plattemiiroowwa)) Explanation:
Bob the plate.
the microwave.
Bob walks towards the plate and grabs it
Bob walks towards the microwave
Alice infers his goal as "put the plate into the microwave"
Alies gool walks to the microware.
Alice opens the
Alice opens the microwave
Rule: Walk to(Bob, bedroom) A /SiichSwwss((ecc on(Alie,, TV) A Walk to (Bob, sofa) -Watch(Bob, TV)
Explanation:
walk towards the be bedroom.
walks towards the sofa.
Bob walks towards the bedroom
Bob walks towards the sofa
Alice infers his goal as "watch TV"
Alice switches on TV
Allice finds that Bob walks the bedroom
Alice switches on
Rule: Walk to(Bob, sofa) BlockkAAliccceB Make way (Alice,Bob) B
Explanation:
Rob meets Alice.
Rob walks by
Bob walks towards the sofa
Alice blocks Bob's 's way by chance
Alice makes way for Bob so he can walk quickly
Bob walks by Alice's side
moots Bob.
makes way Roh.
Figure 4: Visualization and explanation of logic rules in Watch-and-help dataset.
CONCLUSION
In this paper, we introduced a novel logic-guided reasoning framework aimed advancing human- Al collaboration. By infusing logical reasoning into Al agents, our model demonstrates unique capability to infer and adapt to the goals of others based on observed behaviors. This is achieved by treating logic rules as latent variables and iteratively training the Al agent. In the experiments, method can analyze the movement sequence of agents common household activities and obtain novel insights from generated logic rules. Looking ahead, aim integrate additional physical laws into our framework, like the principles of energy and momentum conservation. Furthermore, plan introduce a predicate generator into our system. This addition will endow Al agents with advanced capabilities, enabling them to generate and reason with new predicates, thereby signifi- cantly enriching their understanding and interaction with complex environments.
7
ACKNOWLEDGEMENT
Shuang Li's research was in part supported by the NSFC under grant No. 62206236, Shenzhen Science and Technology Program JCYI20210324120011 1032, National Key R&D Program of China under grant No. 20222D01 16004, Shenzhen Key Lab Cross-Modal Cognitive Computing under grant No. ZDSYS20230626091100000 and Guangdong Key Lab of Mathematical Foundations for Artificial Intelligence.
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Reviews:
[{"review_id": "f1H917h1sk", "reviewer": "Reviewer_wN2Q", "summary": "The paper presents a novel framework aimed at improving human-AI collaboration by integrating logic-guided reasoning and theory of mind. The key contributions include a method for generating and evaluating logic rules from observational data to infer human intentions and actions, a hierarchical reinforcement learning model incorporating theory of mind for planning robot actions to assist humans, and comprehensive experiments on two datasets demonstrating the effectiveness and generalizability of the proposed model.", "questions": "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.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. This paper proposes a novel method that utilizes logical reasoning to generate a broad understanding of the agent’s objectives in a new environment and thus strengthen social perception and collaboration between humans and AI.\n2. The detailed experiment analysis shows the effectiveness of the proposed method.", "weaknesses": "1. The experimental tasks seem somewhat weak, as even Seq2Seq (Sutskever et al., 2014) can achieve reasonably good results (Table 1). Could the authors provide some more robust human-AI collaboration tasks for reference? (I apologize for not being familiar with the relevant field, so I can only provide a general reference [1]). Additionally, as a human-AI collaboration method, it would be more convincing if a human study could be included if possible.\n2. 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.\n3. 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.\n\n[1] Building Cooperative Embodied Agents Modularly with Large Language Models", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3W2dYlT4Jw", "reviewer": "Reviewer_iG1y", "summary": "This paper focuses on exploiting logic rules to guide human-like agents. They design a rule generator and rule evaluator to obtain useful rules given entities and relations, and apply hierarchical reinforcement learning with ToM to plan actions. The results show that the model achieves SOTA performance.", "questions": "In \"Weaknesses\".", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The method to construct and utilize knowledge graphs to plan action is novel and interesting.\n- The experiments show that the proposed method significantly outperforms baselines in various metrics. With the provided standard deviation, the table is more convincible.\n\nI am not familiar with reinforcement learning and am unable to assess this part.", "weaknesses": "- The definitions of entity, relation, and logic rule are inconsistent with the widely agreed definition in the knowledge graph academia. It seems that the users redefine them in the context of their task, while re-using these terminologies of knowledge graph. This makes the paper a little confusing, especially for audiences with a knowledge graph background.\n- 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.\n- (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.\n- (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\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uFLYiPgIBN", "reviewer": "Reviewer_WpfX", "summary": "This paper introduces a framework aimed at improving human-robot perception and collaboration by integrating logical rules and Theory of Mind (ToM). Logical rules provide interpretable predictions and generalize well across diverse tasks, making them valuable for learning and decision-making. Leveraging ToM to understand the mental states of others enhances effective collaboration. \n\nIn this approach, the authors use logical rules derived from observational data to infer human goals and guide human-like agents. These rules are treated as latent variables, and a rule generator is trained alongside a multi-agent system within the robot's cognitive framework. The process involves two stages: first, assessing the posterior distribution of latent rules using learned embeddings to represent entities and relations, with confidence scores indicating consistency with observed data. Second, a joint optimization of the rule generator and model parameters is performed, maximizing the expected log-likelihood.", "questions": "Refer to the above comments.", "limitations": "", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "To assist humans, a hierarchical reinforcement learning model with ToM is employed to plan robot actions.   \nMultiple experiments validate each component of the framework, and the results on multiple benchmarks demonstrate that this model outperforms the majority of existing approaches.   \nThe combination of logical rules, Theory of Mind, and hierarchical reinforcement learning creates a comprehensive framework for enhancing human-robot collaboration and perception.", "weaknesses": "The rule generator and the reasoning evaluator are important but don't show the particular design.  \nThe Iterative Goal Inference seems that a process of conditional learning, so how to implement this part is not clear enough.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FYeqVbqGD6", "reviewer": "Reviewer_dixh", "summary": "The paper proposes a framework to engage human-AI collaboration by incorporating logical reasoning aiming to develop a machine to help humans work together. In the framework, trajectories of human activities are given, and logic rules that map spatial-temporal states to goals. Then, the agents are deployed to new environments where they need to work together with humans and are trained to help humans predict their goals from the sequence of actions and states. In the experiments, the proposed framework outperformed other SOTA approaches, e.g., in the success rate. Different datasets/tasks and evaluation metrics are used in the evaluation.", "questions": "What are the most related studies? How is the proposed approach compared and superior to them?\n\nIncluding the time and location in the predicate (in Eq. (4)) would generate a large number of ground atoms (atoms without variables), and typically, reasoners (including probabilistic ones e.g. ProbLog and Markov Logic Networks) need to compute ground atoms to perform reasoning.\nDoes 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?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper addresses an important problem of integrating robots to work with humans. The idea of integrating logical reasoning for generalization in this problem setting seems novel. \nThe paper provides good empirical evaluations showing the proposed framework’s advantages against baselines.\nThe experiments are conducted on practical datasets and environments, they give a great insight into the applications of neuro-symbolic methods, where symbolic logic and neural networks are integrated. Limitations are properly discussed.", "weaknesses": "Although I appreciate the paper’s ideas and evaluations, I found some concerns about the paper.\n\nFirst, 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.\n\nMoreover, 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. I suppose 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.\n\nI list some minor comments:\n- In Eq. (5). it is written as $g \\in f$, but I’m not sure this is a standard notation because here $f$ is a rule, not a set.\n- Typo: double periods in the caption of Fig. 3. \n-  I would kindly suggest including the literature on neuro-symbolic research (e.g. [1,2], but not necessary; it is the author’s choice), 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 be improved.\n\n[1] Artur d'Avila Garcez, Luís C. Lamb: Neurosymbolic AI: the 3rd wave. Artif. Intell. Rev. 56(11): 12387-12406 (2023)\n\n[2] Henry Kautz. The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture. AI Magazine, 43(1), 105-125 (2022).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)