Baby Intuitions Benchmark (BIB):  Discerning the goals, preferences, and actions of others

1 Introduction Humans have a rich capacity to infer the underlying intentions of others by observing their actions . For example , when we watch the animations from Heider and Simmel ( 1944 ) ( see video2 and Figure 1 ) , we attribute goals and preferences to simple 2D shapes moving in a flat world . Using behavioral experiments , developmental cognitive scientists have found that even young infants infer intentionality in the actions of other agents . Infants expect agents : to have object-based goals ( Gergely et al. , 1995 ; Luo , 2011 ; Song et al. , 2005 ; Woodward , 1998 , 1999 ; Woodward and Sommerville , 2000 ) ; to have goals that reflect preferences ( Repacholi and Gopnik , 1997 ; Kuhlmeier et al. , 2003 ; Buresh and Woodward , 2007 ) ; to engage in instrumental actions that bring about goals ( Carpenter et al. , 2005 ; Elsner et al. , 2007 ; Hernik and Csibra , 2015 ; Gerson et al. , 2015 ; Saxe et al. , 2007 ; Woodward and Sommerville , 2000 ) ; and to act efficiently towards goals ( Gergely et al. , 1995 ; Gergely and Csibra , 1997 , 2003 ; Liu et al. , 2019 , 2017 ; Colomer et al. , 2020 ) . Machine-learning and AI systems , in contrast , are much more limited in their understanding of other agents . They typically aim only to predict outcomes of interest ( e.g. , churn , clicks , likes , etc . ) rather than to learn about the goals and preferences that underlie such outcomes . This impoverished “ machine theory of mind ” 3may be a critical difference between human and machine intelligence more generally , and addressing it is crucial if machine learning aims to achieve the flexibility of human commonsense reasoning ( Lake et al. , 2017 ) . 1The dataset and code are available here : https : //kanishkgandhi.com/bib 2https : //www.youtube.com/watch ? v=VTNmLt7QX8E 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . Recent computational work has aimed to focus on such reasoning by adopting several approaches . Inverse reinforcement learning ( Ng et al. , 2000 ; Abbeel and Ng , 2004 ; Ziebart et al. , 2008 ; Ho and Ermon , 2016 ; Xu et al. , 2019 ) and Bayesian approaches ( Ullman et al. , 2009 ; Baker et al. , 2009 , 2011 , 2017 ; Jara-Ettinger , 2019 ) have modeled other agents as rational , yet noisy , planners . In these models , rationality serves as the tool by which to infer the underlying intentions that best explain an agent ’ s observed behavior . Game theoretic models have aimed to capture an opponent ’ s thought processes in multi-agent interactive scenarios ( see survey : Albrecht and Stone , 2018 ) , and learningbased neural network approaches have focused on learning predictive models of other agents ’ latent mental states , either through structured architectures that encourage mental-state representations ( Rabinowitz et al. , 2018 ) or through the explicit modeling of other agents ’ mental states using a different agent ’ s forward model ( Raileanu et al. , 2018 ) . Despite the increasing sophistication of models that focus on reasoning about agents , they have not been evaluated or compared using a comprehensive benchmark that captures the generalizability of human reasoning about agents . For example , some evaluations of machines ’ reasoning about agents have provided fewer than 100 sample episodes ( Baker et al. , 2009 , 2011 , 2017 ) , making it infeasible to evaluate learning-based approaches that require substantial training . Other evaluations have used the same distribution of episodes for both training and test ( Rabinowitz et al. , 2018 ) , making it difficult to measure how abstract or flexible a model ’ s performance is . Moreover , existing evaluations have not been translatable to the behavioral paradigms that test infant cognition , and so their results can not be analyzed in terms of the representations and processes that support successful human reasoning . Our benchmark , the Baby Intuitions Benchmark ( BIB ) , presents a comprehensive set of evaluations of commonsense reasoning about agents suitable for machines and infants alike . BIB adapts experimental stimuli from studies with infants that have captured the content and abstract nature of their knowledge ( Baillargeon et al. , 2016 ; Banaji and Gelman , 2013 ) . It provides a substantial amount of training episodes in addition to out-of-distribution test episodes . Moreover , BIB adopts a “ violation of expectation '' ( VOE ) paradigm ( similar to Riochet et al . ( 2018 ) ; Smith et al . ( 2019 ) ) , commonly used in infant research , which makes its direct validation with infants possible and its results interpretable in terms of human performance . The VOE paradigm , moreover , offers an additional advantage relative to other measures of machine performance like predictive accuracy , in that it reveals how an observer might fail : VOE directly contrasts one outcome , which requires a high-level , humanlike understanding of an event , to another one , which instantiates some lower-level , heuristically , or perceptually based alternative . BIB thus presents both a general framework for designing any benchmark aiming to examine commonsense reasoning across domains as varied as agents , objects , and places , as well as a key step in bridging machines ’ impoverished understanding of intentionality with humans ’ rich one . AGENT ( Shu et al. , 2021 ) , a benchmark developed contemporaneously with BIB , is inspired by infants ’ knowledge about agents and has been validated with behavioral data from adults . Similar to BIB , AGENT challenges machines to reason about the intentions of agents as the underlying cause of their actions . Both benchmarks test whether models can predict that agents have object-based goals and move efficiently to those goals . There are nevertheless key differences between BIB and AGENT . First , BIB evaluates whether models can reason about multiple agents , inaccessible goals , instrumental actions , and the differences between the intentions of rational and irrational agents ; AGENT does not test these competencies . The advantage of including them is that they introduce additional elements—beyond a single rational agent and one or two possible goal objects—that models must flexibly account for in their reasoning . These competencies , moreover , extend the infant cognition literature , potentially allowing BIB to further inform tests for infants . Second , BIB and AGENT evaluate new models differently . AGENT involves training on many different leave-out splits , where those splits include relatively minor differences between the training and test sets . BIB , in contrast , presents a single canonical split designed to maximally evaluate the abstractness of a model ’ s reasoning : Models tested on BIB must flexibly combine learning from different types of training scenarios to solve a novel test scenario . We thus ultimately see BIB and AGENT as complementary and hope that new models focused on commonsense reasoning about agents will be evaluated on both . 3Note that in the psychology literature , “ theory of mind '' typically refers to the attribution of mental states , such as phenomenological or epistemic states ( i.e. , perceptions or beliefs ) to other intentional agents ( Premack and Woodruff , 1978 ) . In this paper , we address on only one potential component of theory of mind , present from early infancy , which focuses on reasoning about the intentional states , not the phenomenological or epistemic states , of others ( Spelke , 2016 ) . 2 Baby Intuitions Benchmark ( BIB ) . BIB focuses on the following questions : 1 ) Can an AI system represent an agent as having a preferred goal object ? 2 ) Can it bind specific preferences for goal objects to specific agents ? 3 ) Can it understand that physical obstacles might restrict agents ’ actions , and does it predict that an agent might approach a nonpreferred object when their preferred one is inaccessible ? 4 ) Can it represent an agent ’ s sequence of actions as instrumental , directed towards a higher-order goal object ? 5 ) Can it infer that a rational agent will move efficiently towards a goal object ? Following the VOE paradigm , each of BIB ’ s tasks includes a familiarization phase and a test phase , together referred to as an “ episode . '' The familiarization phase presents eight successive trials introducing the main elements of the visual displays used in the test phase and allows the observer to form expectations about the future behavior of those elements based on their prior knowledge or learning . The test phase includes an unexpected and expected outcome based on what was observed during familiarization . Typically , the unexpected outcome is perceptually similar to the familiarization trials but is conceptually implausible , while the expected outcome is more perceptually different but involves no conceptual violation . The unexpected outcome is thus unexpected only if the observer possesses an abstract understanding of the events , and the expected outcome reflects a lower-level , heuristically , or perceptually based alternative . When VOE is used with infants , their looking time to each outcome is measured , and infants tend to look longer at the unexpected outcome ( Baillargeon et al. , 1985 ; Turk-Browne et al. , 2008 ; Oakes , 2010 ) . Inspired by Heider and Simmel ( 1944 ) , the primary set of visual stimuli present a fully observable “ grid world , '' shown from an overhead perspective , and populated with simple geometric shapes that take on different roles ( e.g . “ agents , '' “ objects , '' “ tools '' ) and provide few cues to those roles . We chose this type of environment as particularly suitable for testing AI systems ( e.g. , Baker et al. , 2017 ; Rabinowitz et al. , 2018 ) because it allows for procedural generation of a large number of episodes , and the simple visuals focus the problem on reasoning about agents . This design will also allow infancy researchers to test new questions about infant ’ s understanding of agents in future work . While BIB and the baseline models tested here focus on this 2D grid-world environment , we have also instantiated the stimuli in 3D as a means of varying perceptual difficulty in future studies evaluating other models ( appendix A ) . 2.1 Can an AI system represent an agent as having a preferred goal object ? . Developmental Background . Infants infer that agents have preferences for goal objects , not goal locations ( Gergely et al. , 1995 ; Luo , 2011 ; Song et al. , 2005 ; Woodward , 1998 , 1999 ; Woodward and Sommerville , 2000 ) . As illustrated in Figure 2 ( left ) , Woodward ( 1998 ) ’ s seminal study showed that when 5- and 9-month-old infants saw a hand repeatedly reaching to a ball on the left over a bear on the right , they then looked longer when the hand reached to the left for the bear , even though the direction of the reach was more similar in that event to the events in the previous trials . These results suggest that the infants expected that the hand would reach consistently to a preferred goal object as opposed to a preferred goal location . Other studies have shown that infants ’ interpretations are not restricted to reaching events . For example , infants attribute a preference for goal objects to a 3D box during a live puppet show when that box seemingly exhibits self-propelled motion . ( Luo , 2011 ; Luo and Baillargeon , 2005 ; Shimizu and Johnson , 2004 ) . When shown an agent repeatedly moving to the same object at approximately the same location , do machines infer that the agent ’ s goal is a preferred object , not location ? Familiarization Trials . The familiarization shows an agent repeatedly moving towards a specific object in a world with two objects ( Figure 2a center ) . The agent ’ s starting position is fixed , and the locations of the objects are correlated with their identities such that the preferred object and nonpreferred object appear in generally the same location across trials ( appendix Figure 9 and 10 ) . Test Trials . The test uses two object locations that had been used during one familiarization trial , but the identity of the objects at those locations has been switched . In the expected outcome ( Figure 2b ) , the agent moves to the object that had been their goal during the familiarization , i.e. , their preferred object , but the trajectory of their motion and the location of that object is different from familiarization . In the unexpected outcome ( Figure 2c ) , the agent moves to the nonpreferred object , but the trajectory of their motion and the location they move to is the same as familiarization . The model is successful if it expects the agent to go to the preferred object in a different location .

Summary: EDIT AFTER AUTHOR RESPONSE:  One concern I originally had was whether researchers would actually find this dataset useful, but the review by @Yh1E and @uke7 improved my impression of the utility of the dataset.  I am also pleased that the authors will comment more on the relation of their problem setup to classical MDP and POMDP frameworks.  Hence I am raising my score from 5 to 7.  This paper asks whether AI systems can demonstrate similar theory of mind (ToM) capabilities as human infants.  It identifies a number of key ToM capabilities that have been established in the cognitive science literature, such as learning preferences of observed agents, and predicting that agents will take the most efficient path to their goal.  The paper introduces a benchmark dataset with 2d and 3d videos with simulated agents for the purpose of testing ToM capabilities in AI systems.  Each task in the dataset consists of a familiarization phase with videos intended to allow learning of agent behavior, and then a test phase where the agent exhibits either expected or unexpected behavior.  Taking inspiration from the fact that human babies look longer at unexpected stumli, the paper introduces a "violation of expectations (VoE)" framework for evaluating AI systems.  Under this paradigm, an AI system is considered to demonstrate evidence of human-like learning if it has greater prediction error for video frames in the unexpected condition than in the expected condition for the testing episode.  It evaluates two recent approaches on this dataset, behavior cloning, and offline RL.