DL-based prediction of optimal actions of human experts
Authors: Jung H. Lee
Ryan S Butner
Elise Saxon
Nathan Oken Hodas
Venue: ICLR 2022 Submitted (2022)
Keywords: deep learning
expert system
sequential learning

Abstract: Expert systems have been developed to emulate human experts’ decision-making. Once developed properly, expert systems can assist or substitute human experts, but they require overly expensive knowledge engineering/acquisition. Notably, deep learning (DL) can train highly efficient computer vision systems only from examples instead of relying on carefully selected feature sets by human experts. Thus, we hypothesize that DL can be used to build expert systems that can learn human experts’ decision-making from examples only without relying on overly expensive knowledge engineering. To address this hypothesis, we train DL agents to predict optimal strategies (actions or action sequences) for the popular game `Angry Birds’, which requires complex problem-solving skills. In our experiments, after being trained with screenshots of different levels and pertinent 3-star guides, DL agents can predict strategies for unseen levels. This raises the possibility of building DL-based expert systems that do not require overly expensive knowledge engineering.

Under review as a conference paper at ICLR 2022
DL- PREDICTION OF OPTIMAL ACTIONS OF HU- MAN EXPERTS
Anonymous authors Paper under double-bling review
ABSTRACT
Expert systems have been developed to emulate human experts' decision-making. Once developed properly, expert systems can assist or substitute human experts. but they require overly expensive knowledge enginecring/acquisitonn Notably. deep learning (DI. can train highly efficient computer vision systems only from examples instead of relying carefully selected feature sets by human experts. Thus, we hypothesize that DL can be used to build expert systems that can learn human experts' decision-male makinn from examples only without relying on overly expensive knowledge engineering. To address this hypothesis, we train DL agents predict optimal strategies (actions action sequences) for the popular game 'Angry Birds', which requires complex problem-soo solving skills. ments, after being trained with screenshots of different levels and pertinent 3-star guides, DL agents can predict strategies for unseen levels. This raises the possi- bility of building DL-based expert systems that do not require overly expensive knowledge engineering.
our experi-
INTRODUCTION
To assist our decision-making, knowledge engineering has been developed to code human experts' knowledge into artificial expert systems, which have been successfully applied multiple do- mains (Buchanan & Smith 1988 Russell & Norvig 1995 Kiritsis 1995). For instance, MYCYN (Buchaman Shortliffe I 1984) can provide medical advice on infectious diseases, and XCON (Mc- Dermott 1980) can help users to find right computer components. However, knowledge engineer- ing requires expensive and complex engineering, limiting the expert systems' utilities (Buchanan & Smith, 1988, Kiritsis 1995). Notably, DL. can enable artificial agents to automatically learn general rules essential for complex tasks from examples without human experts' instructions (LeCun et al.) 2015 Hertz et al. 1991). This leads to the hypothesis that we can use DL to build expert systems without expensive knowledge engineering (Tan 2017). Indeed, line of study already explores DL agents' utility in diagnosing diseases based on medical images, for which traditional expert systems were used; see (Litjens et al., 2017) for a review.
However, it should be noted that most humans' decisions involve a sequence of actions, each of which has a subgoal. Thus, sequence learning can be a key component in building artificial in- telligent agents that mimic human experts' decision-making (Sun & Giles 2001 Boddy 1996). Traditionally, symbolic planning is used to mimic humans' goal-directed high-level planning, which repeatedly creates sub-goals necessary to accomplish higher level goals until subgoal can be ac- complished in single action, and deploying symbolic planning requires 'substantial prior knowl- edge' and highly computational complexity (Sun & Giles 2001). That is, the cost of symbolic planning can be as high as that of other expert systems.
In this study, we explored DI. can be used to learn high-level planning essential for human ex- perts' decision-making. We note that deep Q learning (Mnih et al., 2013) has demonstrated impres- sive achievements in learning optimal sequences of actions for games (Silver et al., 2016) and that recurrent neural networks have been used to model time series (Hertz et al. 1991. Our goal differs from these traditional DL approaches, in that we aim to model high-level planning more directly. To this end, asked if deep neural networks (DNNs, agents trained by DL) can lear to predict strategies written by human experts for the popular game 'Angry Bird' (AB) (Contributors 202T] Renz et al. 2019) from examples. To clear each stage, players must destroy all pigs in each stage.

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As the pigs within the game are protected by piles of objects stay inside towers, single attack is often not enough to destroy all the pigs in each stage. Instead, players must sequentially orga- nize subgoals to destroy all pigs efficiently, making AB an ideal testbed for high-level planning. Thus, we trained DNNs to predict human experts' AB game strategies available at AB wiki pages (ontributorss 202T); the AB strategies will be referred as 3-star guides (3SG) hereafter.
In the experiments, we asked three questions. First, can DL learn the functional relationships be- tween screenshots (still images) of the stages and 3SG (human experts' strategies)? Second, can an attention-based image captioning (ABIC) system, previously proposed (Xu et al. 2013), learn to create verbal strategies, which would sound reasonable human players? Third, can ABIC predict a sequence of strategies (high-level planning) appropriate to clear a stage? Due to the lack of proper evaluation metric, we qualitatively evaluate the outcomes of the answers after training ABIC; the
evaluation metrics of the language model is the topic of active research (Sellam et al. 2020 Pap- ineni et al., 200T] Chen et al., 1998). Our empirical studies suggest that ABIC can learn to create verbal strategies after training and that it can predict sequence of actions, not just single action. They also support our hypothesis that DL can learn high-level planning from examples and that can be used to build expert systems to assist human decision making without relying on overly ex- pensive and complex knowledge engineering. Thus, our study indicates that DL can help us deploy expert systems into more domains.
2 DL AGENTS TO PREDICT OPTIMAL STRATEGIES OF ANGRY BIRDS (AB)
To clear each level of AB, players use birds to destroy the pigs that are scattered around and protected by towers or piles of wood, glass (or ice) and stone blocks. Each level provides fixed sequence of birds and a unique challenging environment, and the players need to find an effective sequence of actions that can destroy all pigs in each level. The scores are estimated using various factors such as incurred damages and the number of birds used. When players obtain sufficiently high scores, they receive 3 stars. Any experienced game player (i.e., expert) can devise good strategies (i.e., sequence of actions) to clear even the unseen levels, and conversely, the accuracy/efficiency of the plans can indicate the players' ability/efficiency Therefore, we ask if DL could predict optimal actions (or action sequence) from given screenshots levels. We use the 3SG available at the wiki page (Contributors 2021 as optimal strategies for DL predict them.
2.1 FUNCTIONAL CORRELATIONS BETWEEN SCREENSHOTS AND 3SG
In principle, we can consider an expert system as an unknown functional mapping function that can identify an optimal relationship between problems/situations (i.e., screenshots in this study) to decisions/solutions (i.e., 3SG in this study); see Fig. 1A. If the mapping does exist, it would be possible to build expert systems. Thus, we first investigate functional correlations between screen- shots and 3SG. Since multilayer perceptron (MLP) has been considered universal problem solver (Hertz et al. 1991). we assume that a trained MLP can predict the embeddings of 3SG (word embed- dings) from the embeddings of screenshots (image embeddings), if the functional mappings from the screenshots to 3SG exist.
Based this assumption, we train MLPs, which contain 100 hidden neurons in single hidden layers, with 80% levels (out of 327) randomly selected and test them with the remaining (test) levels. The image embeddings are created using the ResNet18 (Safkal 2021). and the word embeddings are generated using Hugging Face's BERT model (Wolf et al. 2020). We first use image embeddings as inputs and word embeddings as outputs (Fig. IB) and train MLP minimize the mean squared error (MSE). A open-source ML library Pytorch (Paszke et al. 2017) is used to construct MLPs, and its built-in function (MSE Loss) is used to estimate MSE The word embeddings are projected to the PCA space, which is spanned by various numbers of principal axes, using python package 'Scikit-leam' (Pedregosa et al., 201 In the experiments, we repeat the same experiment times with different random seeds and display the mean and standard deviations of them in Fig. IC. As the number of principal axes increases, MLPs' errors are reduced, and the magnitudes of reduction grow bigger; see the blue line in Fig. IC. Interestingly, when the word and image embeddings used as inputs and outputs, MLP cannot predict the outputs anymore (the orange line in Fig. 1C). These results suggest that functional mappings from screenshots 3SG exist, but not vice versa.
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A
C
acresmahot
Experts
guide
2creenshot
guide
1.4
word image
1.2
acresmahot
guide
Enor Ralio
1.0
acreenahots..
guide..
Output O
image to quides
Imaga to quilie
image to word
Image
0.4
0.2
Image embedding
Guides embedding guides
50
100
150
200
250
300
Image guides
# Principal axes
Figure 1: Functional mapping between image embeddings and word embeddings. (A), Schematics of expert system. (B), Diagram of MLP trained to detect functional mappings. In MLPs, single hidden layers contain 100 neurons. (C), MSE error ratio between before and after training. The MSE error estimated after training normalized to that estimated before training. Blue and Orange represent error ratios for experiments 'img-to-we wordd and 'word-to-img', respectively.
2.2 EXPERT SYSTEMS OF AB
Encouraged by the results above, we ask if DL can directly predict human instructions from screen- shots. Here, train the attention-based image-captioning system (ABIC) (Xu al. 2015) to predict 3SG from the screenshots. In the original form, ABIC uses convolutional networks as an encoder and recurrent networks as decoder. The encoder extracts 14-by-14 annotation vectors, and the decoder uses annotation vectors to predict word sequences after training. They further proposed that the attention network, which determines the region of interest, can help the encoder-decoder system learn to produce captions. We refer to the original study for more details (Xu et al.) 2015).
In this study, we adopted Pytorch implementation of ABIC from a public github repository (Vinod- ababu 2021) and train it to predict 3SG from the screenshots using the default setting of the imple- mentation. This adopted Pytorch implementation uses ResNet101 (He al. 2015) as encoder and the long short term memory (LSTM) (Hochreiter & Schmidhuber 1997) as decoder. Specifically, ResNet101 produces 2048 annotation vectors (14-by-14 pixels), the attentional network transforms annotation vectors using weighted sum, and LSTM selects the best word with previously selected word and weighted annotation vectors. In the experiments, we randomly select 70% of the levels training examples, 15% as validation examples, and 15% test examples. The number of total levels used here 359. ResNet101 was adopted from TorchVision (Marcel Rodriguez 2010) and frozen during training, whereas both LSTT and the attentional network were trained.
2.2.1 ABIC CAN BR AN EXPERT
We note that ABIC was trained to produce single sentence caption after being trained with multiple captions for single image (Xu al.] 2015). To test the potential AIBC as mimic this baseline training of AIBC by ignoring the fact that 3SG describe the sequence of actions. Instead, we treat them as independently generated guides for the same stage and use them to train AIBC expert system of Angry Birds (EAB) predict single action. With this setting, also effectively increase the number of training examples.
expert, we
In AB and its wiki page (Contributors, 2021), levels grouped into multiple stages, and the two consecutive numbers are used to refer to each level. For instance, level '12-03' refers to the third level in the 1 12th stage, and the same type of reference is used to clarify the levels below. In the
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experiments, 253 levels randomly chosen training and 50 levels test examples (i.e., levels). We edit the guides slightly for clarification and remove irrelevant descriptions. Many instructions explain the bird types, while most screenshots do not include the birds. Thus, we augment the screenshots by inserting the circles that represent the first birds in the levels via colors in the top-left corners (Fig. 2).
A
B
<start>
Fire
the
first
Big
Brother
Bird
into
the
top
of
the
left
tower
<end>
Figure 2:
Prediction of EAB on the single action. (A), Examples of screenshots augmented with bird symbols; see top-left corers. (B), Example of outputs of ABIC trained as EAB.
With the edited guides and augmented screenshots, we train EAB and evaluate its predicted actions for test (unseen) screenshots. Fig. 2B shows example of EAB outputs. As shown in the fig- ure, the model utilizes attention to create a strategy (sentence). Table shows some examples of EAB's predicted actions/strategies for the unseen test levels (i.e., screenshots). Here, we make few observations. First, although the guides describe the sequence of actions, most EAB outputs appear describe only the first action. Second, EAB selects the correct birds to create predic- tions on strategies for 10 test levels. Third, a few of the predicted strategies are consistent with the guides. Fourth, the 3SG have diverse writing styles, making the quantitative evaluation of predicted strategies extremely difficult.
These results raise the possibility that DL can learn predict an optimal action from examples only. Due to EAB's low precision, we tested if EAB's performance can be further improved..
2.2.2 EAB BECOMES MORE ACCURATE WITH BETTER OBJECT DETECTION UNITS
In the experiments mentioned above, the screenshots are fed to the image encoder (ResNet101) trained with ImageNet (Xu et al. 2015 VVnodabbbb 2021). However, it should be noted that the visual objects used in AB are abstract and substantially different from real world objects in the ImageNet, which could reduce the quality/precision of visual embeddings of the ResNet101 and consequently the precision of EAB. Thus, we ask if better image embeddings can improve EAB's s accuracy. In doing so, we build customized detectors of the objects the game, which use color templates of pigs. glass/ice, wood and stone blocks. Specifically, we use OpenCV's 'inRange' func- tion (Itsee7| 2015 Its 2014) find candidate areas for the objects and scipy. .ndimage's 'find_object' Virtanen et al. 2020) to test if the areas contain actual objects. For more accurate detection, manually adjust the templates of individual stages and use few simple error correction rules. After detecting the objects,
them to synthesize functional screenshots of the stages (Fig. 3B).
After training EAB with synthesized (reconstructed) screenshots and 3SG of 249 training levels, we qualitatively evaluate EAB's predictions for 58 test levels. We first select the predictions that artic- ulate the correct birds and categorize them into different categories, not-related strategy (NRS), probable strategy (PS), consistent strategy (CS). NRS indicates strategies not relevant the levels,
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A
Figure 3: Detection of game objects. (A), Examples of detected pigs (top-row) and wood blocks (bottom-row). Our detectors can distinguish the size pigs. Blue and red boundary boxes refer to mid and small pigs. (B) and (C), Examples of raw screenshots (top-row) and reconstructed images (bottom-row).
PS indicates strategies reasonable (not necessarily good) for the levels, and CS indicates strategies consistent with 3SG. We note that some CS do not specify targets and thus code them CS-ND (consistent strategies without detailed targets).
We found that 41 levels have the correct birds. 6 levels evaluated CS; 16 levels PS, 5 levels as CS-ND amd 14 levels as NRS. Table 2 shows the examples of all categories.
2.2.3 EAB CAN PROPOSE MULTIPLE ACTIONS
In AB, players can obtain optimal results by strategically organizing sequential actions. Thus, as the final test, we ask if EAB can learn create sequence of actions. Specifically, we remove the 'end' token from 3SG to build embeddings of action sequences. After training EAB with 253 training levels, we evaluate the predicted sequence for 50 test levels and make a few observations (Table 3). First, EAB predicts multiple actions for most levels-1 action in 10 levels, 36 actions in and 3 actions for levels. Second, EAB can choose a sequence of birds properly in some levels. Specifically, it selects two first birds correctly for 13 levels, 3 first birds correctly for 2 levels, and birds for 5 levels. Third, the accuracy of the predicted sequence of actions is low, but EAB still predicts action sequences consistent with 3SG for 3 levels. EAB also suggests probable action sequences for 6 levels. For 5 levels, one of the predicted actions includes some minor errors. For instance, EAB proposes aim 'wooden triangle' for level '4-04', but it does not exist in this level. We also found some predicted actions do not specify the targets for 3 levels. We categorize them into CS, PS, PS without targets (PS-ND) PS with minor errors (CS-me). Table 3 list the examples of CS, PS, PS-ND, PS-me and NRS.
3 DISCUSSION
In this study, we used the popular game Angry Bird to study the plausibility of DL-based expert sys- tems. Specifically, we trained an off-shelf model (ABIC) to predict human players' strategies (i.e., experts). Even though the strategies are written by many players, and thus they are heterogeneous in styles and vocabularies, ABIC was able to predict optimal strategies for given screenshots of stages. This result raises the possibility that DL be used build expert systems without overly expen- sive knowledge engineering and make the expert systems more affordable and deployable to more domains.
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Further, results suggest that DL can encode human experts' knowledge/skills from their verbal explanations, leading us to speculate that DL can be used to quantify or better understand human behaviors. Below we briefly discuss two possible scenarios.
3.1 IMPLICATION FOR INTENTION DETECTION
By predicting others' intentions, can make decisions and take actions. When detect dangerous behavioral cues from someone approaching us, we natively predict hostility and coordinate our actions accordingly. If an advanced surveillance camera detect hostile intentions in public, they may proactively alert authorities to prevent criminal activities or neutralize threats. However, the behavioral cues that we rely on remain poorly understood, making the explicit design of intention detectors extremely challenging. Based on our experiments suggesting that DL can learn to predict humans' eecision-making process from examples, we argue that DL can be used build intention detectors such as advanced surveillance systems.
3.2 IMPLICATION FOR TRAINING
To become experts, students need extensive training and experience. However, teaching requires perts to translate their experience/knowledee into standard and quantifiable languages. This remains challenging process in many domains. Our results (Fig. IC) suggest that we could build functional mappings between experts' behaviors and tasks. If we could build DL agents to capture functional relationships between expert's decisions and tasks' components, perts' knowledge into quantifiable forms, which can facilitate students' learning. For instance, we can build expert system that can faithfully predict veteran pilots' decisions and use it to analyze the influence of aircrafts' sensors on their decisions. The discovered correlations between aircrafts' sensors and pilots' decisions will prepare student pilots more effectively.
could use them translate ex-
3.3 LIMITATION AND FUTURE DIRECTION
Our EAB is trained to predict the optimal strategy (either single action or sequence of actions) for the level, but multiple strategies can be used to accomplish the same goal, and the best optimal choice varies depending on contexts. Therefore, real world expert systems must learn various types of strategies to choose the optimal strategy. To this end, we need to build multiple agents specializing in individual strategies and develop dynamic evaluation systems that can flexibly choose the best agent from the available pool. In the future, we plan to seek such multi-agent expert systems.
REFERENCES
Mark Boddy. Temporal reasoning for planning and scheduling in complex domains: Lessons learned. In ARPI 1996 Proceedings, pp. 269 271. AAAI, 1996.
Buchanan and R G Smith. Fundamentals of Expert Systems. Annual Review of Computer Sci- ence, 3(1):23-55) 1),223558, June 1988. ISSN 7016, 7016. doi: 10.1 10.1146/ammurerccss0...kk 00..00000.00000888. 000323. URL http:/t///// annua reviews. wtt /10 114 /aa//nnnueevveeeeeeee cs. 03.
0601 88, 000323
Bruce G. Buchanan and Edward Shortliffe. Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison Wesley, MA, 1984.
Stanley Chen, Douglas Beeferman, and Ronald Rosenfeld. EVALUATION METRICS FOR LAN- GUAGE MODELS, In DARPA Broadcast News Transcription and Understanding Workshop, pp. 275-280, 1998.
Contributors. Angry Birds Wiki, 2021. URL htt.tt////ngyyy..dd..aaooooo..o Angry Birds Wiki
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. arXiv:1512.0338: [cs], December 2015. URL http:://aaaa org/ abs/ 1512. 03385
arXiv: 1512.03385.

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Table 1: This table lists predicted actions with the correct bird and bad predictions. We also list the first action in 3SG for comparison.
Predictions with the correct bird
levels 8-07
Predicted action
First action in 3SG
Fire the first Black Bird into the left side of the main structure.
Fire the Black bird into the hol- low stone square of the first tower, pushing both stone towers to the right and popping all pigs on the right side.
12-05
Fire the first Big Brother Bird to the left side of the second struc- ture.
Fire Big Brother through the top half of the first wooden wheel.
12-09
Fire the first Red bird into the top of the left tower, causing the structure fail to the left.
Fire Big Brother through the horizontal wooden plank above the first pig.
15-02
Fire the first Black Bird into the horizontal stone plank on the right side.
Shoot the Black Bird in the mid- dle of the level to activate the TNT.
4-04 First, fire the Yellow Bird so he <unk>hits the second stone plank
First, send the Yellow Bird through the second angled wooden plank.
5-11 Fire the first Yellow Bird into the first horizontal stone plank.
First, fire the Yellow Bird so he breaks the horizontal wood plank supporting the stone plank Shoot the first Black Bird so that it lands directly to the right of the highest pig.
2-06 Lob the first Black bird into the hollow stone square of the right side of the level Fire the first Big Brother into the bottom-left of the main struc- ture.
10-13
Fire the first Big Brother into the middle of the all-stone tower (which has the Medium Pig and a round wooden ball under the tower).
11-11 Fire the first Big Brother through the horizontal wooden plank be- tween the first <unk>> Fire the first Big Brother into the top of the left tower
Fire the first Big Brother into the horizontal stone planks in front of the slingshot platform. First, fire the first Big Brother Bird head-on into the top-left pig.
9-08
Bad examples
Prediction
First action in 3SG
13-05
Fire the first Red bird into the lefimost structure, spinning it back through the the TNT in the right.
Lob one or both Brothers high in the air, crashing through the middle of the structure.
15-10 Fire the first Red bird into the leftmost structure, spinning it back through the the TNT in the right.
Fire the first Yellow bird through the wooden block (not the hol- low square) the left side.
1-03
The first Red Bird at the <f the <unk>>ig.
Fire the first Red Bird into the wooden sphere (ball) on top of the hill.

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Table 2: Below we list all CS and CS-ND and some examples of PS and NRS. EV and LV refers evaluation
and level.
EV
Predicted action
First action in guide
LV
CS
Fire the Boomerang bird into the main structure, spinning it back into the TNT.
Fling the Boomerang bird over the main structure, spinning it back into the TNT.
22-
02
Cs
Fire the first Red bird into the horizon-
Hire the Red bird into the top of the left structure, pushing its roof to the right.
12-
tal stone block on the first structure.
03
CS
Launch the first Blue bird into the middle of the ice tower.
Shoot the Blue Bird at the glass house the right.
15-
04
CS
Shoot the White Bird the top of the structure, bombing as late as possible. Launch the first Big Brother into the bottom of the ice tower.
Shoot the first White Bird's egg the stone blocks to the right the TNT.
2-
20
CS
Fire the Big Brother Birds straight ahead, aiming for the TNT beneath the boat.
10-
14
CS
Fire the first Big Brother into the bot- tom of the first tower, taking it to the right.
Fire the Red bird through the first hor- izontal wooden plank, causing the first column to collapse.
12-
08
CS-
Shoot the first Black Bird through the gap between the two towers.
Loft the Black bird to land dead-
12-
ND
center on the bridge.
15
CS-
Fire the first Yellow Bird into the left
Shoot the Yellow Bird at the lone wood block surrounded by stone.
2-
ND
side of the first structure.
13
CS-
Shoot the Red Bird at the middle of the structure.
Launch the Big Brother Bird through the first pig and into the first horizon- tal stone block and the entire structure will crumble.
9-
ND
04
CS-
Use the Boomerang bird over the main structure, spinning it back through the stone blocks on the right side.
Launch a Boomerang Bird. with per- feet timing. boomerang into stone brick on the upper "cannon" to ram the stone into the TNT, killing the Medium pig.
6-
ND
06
PS
Fire the first Big Brother through the horizontal wooden block anove the first structure
Launch Big Brother Bird through the small ice square in the top-left of the structure.
05
PS
Fire the Yellow bird horizontally into the gap in the middle of the level Fire the first Big Brother through the top of the small wooden squares on the bottom-of the structure.
Fire the first Yellow bird into the top- left TNT.
17-
14
PS
Use the Red bird to topple the stones
22-
atop the tower, clearing the pigs on the right side of the level.
08
PS
Loft the Yellow Bird to break the TNT In the <f the <unk>> Fire the first Big Brother into the bot- tom of the first tower.
Loft Yellow bird and accelerate
12-
through the middle structure. fire Big Brother through the horizontal wooden plank above the first pig.
04
PS
12-
09
NRS
Fire the first Big Brother through the top of the small wooden squares the bottom-of the structure.
Use the Red bird to topple the stones
22-
atop the tower, clearing the pigs on the right side the level.
08
NRS
Fire the first Black bird into the top of the pig, causing the TNT to the right
Fire the Black bird downward into the pile of square stones, launching them rightward and popping all pigs on the left side of the level.
13-
02
NRS
Shoot the Yellow Bird so that so that it hits the TNT in the middle of the level.
First, shoot the Yellow Bird through
4-
the second level of the triangle block, eliminating the first pig.
06
NRS
Use the Boomerang bird blast the wood blocks <unk>>
Launch the first Hal upwards. With precise timing, activate the boomerang mode hit the middle layer of wood.
6-
05
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Table 3: We list examples of prediction of sequence of actions. All CS are displayed.
EV
Predicted action
Action sequence in guide
LV
CS
Fire the first Yellow Bird into the mid- dle of the right tower. Use the next Yel- low bird through the wooden plank in the middle of the structure. Use the next Yel- low Bird to take the remaining pigs on the left side.
Fire the first Yellow bird through the wooden block (not the hollow square) the left side. Launch the next Yellow bird through the wood in the middle of the level. If pigs remain, you have a Yellow bird and Big Brother clean up.
15-
10
CS
Fire the first Black bird into the top of the structure, causing the main tower fall. Then use the next Black bird, landing it to clear the remaining pigs.
Fling the bomb bird directly into the left helmet pig, and trigger upon impact. Then drop the second bomb bird in the gap between the two lower structures.
23-
03
CS
First, use the White Bird bomb the middle of the structure, bombing as late as possible. Then use a Yellow Bird through the wooden blocks in the middle of the level.
Shoot the first White Bird's egg the stone blocks to the right of the TNT.
2-
20
PS
First, fire the Yellow Bird to the left of the left structure. Then, shoot the Yellow Bird through the middle of the structure, popping all three pigs.
Shoot the first Yellow Bird next to the Small Pig that stands next to the TNT at the top. Shoot the second Yellow Bird to the remaining pigs to clear the level.
14-
01
PS
Fire the first Big Brother through the highest wooden block on the right side of the tower to fall.
Fire Big Brother straight ahead. Use the Boomerang bird to attack the right side of the level from behind, then fire an- other Brother forward to pop all remain- ing pigs.
13-
15
PS
Lob the first Black bird into the gap be- tween the first two towers. Fire the next Black bird into the left side of the struc- ture, causing it to topple. Fire the next Black bird into the left side of the struc- ture, causing it to topple.
Fire the first Black bird into the bottom- left support. Arc the next Black bird into the small pig in the top-right and the plosion should topple everything that re- mains.
16-
05
PS- me
Fire the first Red Bird so he lands the middle of the structure, causing it to top- ple. Fire the next Black bird through the stone triangle of the structure to finish.
Fire Big Brother through the horizontal wooden plank above the first pig. Fire the first Black bird along a similar trajec- tory, blasting the left side of the second- tallest tower. Launch the last Black bird little lower, under the overhanging plank and into the base of the tall tower. Shoot the first blue bird into the air, then aim it so it hits the wood between the first couple of stone blocks and the boulder. Use the White Bird's ability to reach the stone blocks hanging by string. Shoot the next White Bird to the wood "house" closest to the slingshot. Before you get hit, use the egg bomb. Shoot the Yellow Bird at the TNT. Shoot the Red Bird then to the last pig in the stone house.
12- 09
PS-
First, fire the Yellow Bird the left hol- low wooden squares. Then shoot the Yel- low Bird the top hollow stone squares. Send the White bird towards the top structure. Then use the White bird to the right tower.
1-
me
20
PS-
15-
ND
05
NRS
Shoot the first Black Bird that it hits the first pig. Shoot the next Black bird through the gap, spinning back through the small wooden block above the next two pigs.
Shoot the first Black Bird that it lands directly to the right of the highest pig.
2-
06
NRS
Fire the Blue bird to break the two wooden blocks in the bottom-left of the structure. Fire the Blue bird through the left side of the structure, causing the TNT within.
Fling the Red bird into the ice leg of the rightmost tower. There can be an ex- treme amount of secondary destruction. In some cases, only one or two pigs re- mained.
13-
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Reviews:
[{"review_id": "t5DGY9ATe9_", "reviewer": "Reviewer_iZWN", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "The paper studies a very interesting problem and has detailed discussions about the approach it pursues, but I have several concerns listed below:\n\n[Statements not supported]\n- The contribution of the paper and the difference from the previous work is not really well described. In the third paragraph of the introduction for example, it is written: \"Our goal differs from these traditional DL approaches, in that we aim to model high-level planning more directly\". This sounds very vague to me. It was better if you had described in a more scientific way what you mean by \"more directly\".\n\n- In the end of page 2 you mention that from the word embeddings it is not possible to get to the image embeddings without investigating any further why that could be the case. Did you try any other architecture for this task and no other model was able to learn this mapping or you made this conclusion based on the result of the same model. In general, I find the last paragraph of page 2 a bit unorganized. \n\n- There is no discussion on the relationship between the number of principal components chosen in PCA and the Error. I see that you conclude that more principal components result in lower error (Figure 1) but I see no more discussion on why that is the case.\n\n[Lack of scientific writing/mathematical modeling]\n- In the second paragraph of 2.1 where you start to say how you train your MLP to learn the embeddings it is better if you are more precise than saying \"we train the MLP to minimize the mean squared loss\". I assume what you mean is the MSE between the predicted word embedding and the ground truth embedding but it was better if you had defined the embedding vectors previously and had written the exact objective for training the MLP and then the MSE loss. I understand that you try to explain the training procedure verbally but in some cases it is much more elegant if you use mathematical notation. For example, in each of the three questions that you try to answer, you could first mathematically model the objective and then say how you optimize it.\n\n[Not enough details regarding implementation]\n- What is the architecture of the MLP you use?  What are the size of the image and word embeddings?\n\n[Questions]\n- What do you mean by error ratio in Figure 1. I cannot find the definition.\n- You mention in the Discussion that ABIC is able to predict optimal strategies given the screenshots but you never defined what you mean by optimal and you never reported the accuracy that you achieve. How did you conclude that it is the optimal strategy?\n- In none of the three tasks the performance of the machine in terms of the metric that you consider (I assume MSE) is not reported for either of the train/val/test sets. It is not possible to comment on the performance of the model when this is the case.\n\n[Typos]\n- ln the end of line 4 in 2.2.3\n- I find the discussions in 3.1 and 3.2 a bit out of context and not related to the paper.\n\n[Eplainability]\n- I was expecting more examples of the outputs of the model. One of the goals of the paper is to study whether the instructions output by the model sounds reasonable to a human expert but this is not well discussed in the paper.\n", "summary_of_the_paper": "The paper aims to study the training of Deep Learning (DL) models from examples and to this end trains a DL agent to predict actions given screenshots of the game \"Angry Birds\" and observe that the trained model is able to predict actions for test samples and concludes that possibly DL expert systems can learn only by examples. The contributions are three-fold:\n\n1- First, they train an MLP model to learn the mapping from screenshots of the game to the word embeddings which correspond to the guide provided by a player on that stage of the game. \n \n2-Trains an Attention Based Image Captioning System (ABIC) to predict human strategies from the screenshots of the game.\n\n3- Trains ABIC to learn the sequence of strategies necessary to pass a stage.", "summary_of_the_review": "The paper studies a very interesting problem but in my opinion, it lacks scientific writing and reasoning in some instances. It can be further improved if it is written in a more organized way where the objectives are defined and the error and the structure of the model are clearly reported. Please refer to my main review where I have addressed these concerns one by one.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "XYY9b6aXaey", "reviewer": "Reviewer_QBNS", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "This paper is clearly not ready for publication. It has a number of extremely large issues. The only strength of the paper is that the task of learning human guides from visual inputs is potentially interesting. However, otherwise the execution of this paper is very flawed.\n- Why just Angry Birds? The evaluation should be much more thorough than a single game.\n- While they have a dataset of human guides for each level, it's extremely small, with only a few hundred levels total. \n- The evaluation is generally sparse and uncompelling. They have no quantitative metrics at all, their qualitative evaluation is not impressive, and their evaluation is based on an extremely small number of examples anyway.\n- The paper also doesn't even seem complete. There are only 3 sections, a bit over 5 pages, etc.\n\nI have a number of other concerns, but these are enough to make this paper seem like a clear reject.", "summary_of_the_paper": "This paper suggests using deep learning to learn expert decision-making from game strategy guides. They focus on the task of Angry Birds, for which they have natural language descriptions of what actions to take to pass a given level. Given a visual representation of an Angry Birds screenshot, they train an LSTM to model the natural language guides. They claim that their model generalizes well.", "summary_of_the_review": "The evaluation of this paper is extremely limited and uncompelling.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "VPPSvg-C7X", "reviewer": "Reviewer_7AXP", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "The paper is clearly written and the experimental setup is clearly provided. The topic is interesting, but I'm not sure if the contribution is novel enough or if the model/experiment achieve the given goal.\n\nThe model or the task/data is not novel. The combination can be novel. However, predicting the optimal strategy description from the screenshot does not seem to serve as a planning task (or a task that involves a sequence of actions). The difficulty of this task can come from an unknown outcome from a pre-defined action, e.g. the physical interaction is unknown and needs to be learned from experience. Thus, the next action cannot be pre-defined without the outcome of the previous action. The experiment in this paper completely remove this layer of complexity by only trying to produce the 3SG.\n\nThe paper lacks baseline model comparison. \n\nInstead of mapping from a problem to a solution (2.1), the model might just be doing object detection. There are only a small set of possible objects in the screenshot and in the strategy description. The training setup in 2.2.1 seems to encourage object detection than a problem to solution mapping. It ignores the temporal relationship of actions of different stages. It's not surprising that the results show that EAB can only describe the first action, especially given the authors argument the screenshot with the first bird.", "summary_of_the_paper": "The paper proposes to use DL to predict the optimal strategy (in words) for angry birds levels. It applies a image captioning model to predict the optimal strategy description using the screenshot of the setup of different levels. ", "summary_of_the_review": "The topic of the paper is interesting, but overall the paper lacks novelty and contribution, and the experiments are not carefully designed and done, and with no comparison to other models", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "CsL4r4dGYe", "reviewer": "Reviewer_6Hog", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "The paper tackles an interesting problem, which might be situated in or in between learning from demonstrations [0] or imitation learning variants and learning from activity descriptions [1]. \n\nThe paper, however, never clearly and formally defines the learning objective and/or situates the research into such established frameworks/learning protocols. The paper only shortly mentions the standard RL setting and symbolic planning, but a clear relationship to prior, relevant works is not available.\n\nThe empirical results are only shown in interpretable numbers for an initial experiment for testing the feasibility of prediction word embeddings from image embeddings. There are no clear results for the angry birds experiments for predicting instructions.\n\n[0] Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Osband, I. and Dulac-Arnold, G., 2018, April. Deep q-learning from demonstrations. In Thirty-second AAAI conference on artificial intelligence.\n[1] Nguyen, K., Misra, D., Schapire, R., Dudík, M. and Shafto, P., 2021. Interactive Learning from Activity Description. arXiv preprint arXiv:2102.07024.", "summary_of_the_paper": "The paper proposes an approach for building an expert system for sequential decision making problems from image-represented states and instruction-represented actions.  The authors reuse pretrained image- and word embeddings to train a MLP for predicting instructions from game frames. The paper specifically addresses the game angry birds and reports good results for mapping image embeddings to word embeddings.", "summary_of_the_review": "Paper does not clearly formalize the problem, misses to draw concise relationships to established, relevant prior works, and does not report the empirical results in an interpretable form.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper trains a neural network to predict expert strategies (described in natural language) in the game of Angry Birds. While the reviewers agreed this was potentially interesting, there was also a consensus that the scope of the paper was too narrow, that the writing was imprecise, and that the evaluations too few and too qualitative. I agree the paper does not seem thorough enough for ICLR, and recommend rejection.
Decision: Reject