pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Difficulty Adjustment on MOBA Games
Authors: Mirna Paula Silva, Victor do Nascimento Silva, Luiz Chaimowicz
Date: 2017-06-08 00:00:00

This paper addresses the dynamic difficulty adjustment on MOBA games as a way
to improve the player's entertainment. Although MOBA is currently one of the
most played genres around the world, it is known as a game that offer less
autonomy, more challenges and consequently more frustration. Due to these
characteristics, the use of a mechanism that performs the difficulty balance
dynamically seems to be an interesting alternative to minimize and/or avoid
that players experience such frustrations. In this sense, this paper presents a
dynamic difficulty adjustment mechanism for MOBA games. The main idea is to
create a computer controlled opponent that adapts dynamically to the player
performance, trying to offer to the player a better game experience. This is
done by evaluating the performance of the player using a metric based on some
game features and switching the difficulty of the opponent's artificial
intelligence behavior accordingly. Quantitative and qualitative experiments
were performed and the results showed that the system is capable of adapting
dynamically to the opponent's skills. In spite of that, the qualitative
experiments with users showed that the player's expertise has a greater
influence on the perception of the difficulty level and dynamic adaptation.
A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform
Authors: Thai T. Pham, Yuanyuan Shen
Date: 2017-06-08 00:00:00
Tasks: Causal Inference

Kiva is an online non-profit crowdsouring microfinance platform that raises
funds for the poor in the third world. The borrowers on Kiva are small business
owners and individuals in urgent need of money. To raise funds as fast as
possible, they have the option to form groups and post loan requests in the
name of their groups. While it is generally believed that group loans pose less
risk for investors than individual loans do, we study whether this is the case
in a philanthropic online marketplace. In particular, we measure the effect of
group loans on funding time while controlling for the loan sizes and other
factors. Because loan descriptions (in the form of texts) play an important
role in lenders' decision process on Kiva, we make use of this information
through deep learning in natural language processing. In this aspect, this is
the first paper that uses one of the most advanced deep learning techniques to
deal with unstructured data in a way that can take advantage of its superior
prediction power to answer causal questions. We find that on average, forming
group loans speeds up the funding time by about 3.3 days.
Rapid Randomized Restarts for Multi-Agent Path Finding Solvers
Authors: Liron Cohen, Glenn Wagner, T. K. Satish Kumar, Howie Choset, Sven Koenig
Date: 2017-06-08 00:00:00
Tasks: Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in
artificial intelligence and robotics. It has many real-world applications for
which existing MAPF solvers use various heuristics. However, these solvers are
deterministic and perform poorly on "hard" instances typically characterized by
many agents interfering with each other in a small region. In this paper, we
enhance MAPF solvers with randomization and observe that they exhibit
heavy-tailed distributions of runtimes on hard instances. This leads us to
develop simple rapid randomized restart (RRR) strategies with the intuition
that, given a hard instance, multiple short runs have a better chance of
solving it compared to one long run. We validate this intuition through
experiments and show that our RRR strategies indeed boost the performance of
state-of-the-art MAPF solvers such as iECBS and M*.
On the Development of Intelligent Agents for MOBA Games
Authors: Victor do Nascimento Silva, Luiz Chaimowicz
Date: 2017-06-08 00:00:00

Multiplayer Online Battle Arena (MOBA) is one of the most played game genres
nowadays. With the increasing growth of this genre, it becomes necessary to
develop effective intelligent agents to play alongside or against human
players. In this paper we address the problem of agent development for MOBA
games. We implement a two-layered architecture agent that handles both
navigation and game mechanics. This architecture relies on the use of Influence
Maps, a widely used approach for tactical analysis. Several experiments were
performed using {\em League of Legends} as a testbed, and show promising
results in this highly dynamic real-time context.
Setting Players' Behaviors in World of Warcraft through Semi-Supervised Learning
Authors: Marcelo Souza Nery, Roque Anderson Teixeira, Victor do Nascimento Silva, Adriano Alonso Veloso
Date: 2017-06-08 00:00:00

Digital games are one of the major and most important fields on the
entertainment domain, which also involves cinema and music. Numerous attempts
have been done to improve the quality of the games including more realistic
artistic production and computer science. Assessing the player's behavior, a
task known as player modeling, is currently the need of the hour which leads to
possible improvements in terms of: (i) better game interaction experience, (ii)
better exploitation of the relationship between players, and (iii)
increasing/maintaining the number of players interested in the game. In this
paper we model players using the basic four behaviors proposed in
\cite{BartleArtigo}, namely: achiever, explorer, socializer and killer. Our
analysis is carried out using data obtained from the game "World of Warcraft"
over 3 years (2006 $-$ 2009). We employ a semi-supervised learning technique in
order to find out characteristics that possibly impact player's behavior.
Optimizing expected word error rate via sampling for speech recognition
Authors: Matt Shannon
Date: 2017-06-08 00:00:00
Tasks: speech-recognition, Speech Recognition

State-level minimum Bayes risk (sMBR) training has become the de facto
standard for sequence-level training of speech recognition acoustic models. It
has an elegant formulation using the expectation semiring, and gives large
improvements in word error rate (WER) over models trained solely using
cross-entropy (CE) or connectionist temporal classification (CTC). sMBR
training optimizes the expected number of frames at which the reference and
hypothesized acoustic states differ. It may be preferable to optimize the
expected WER, but WER does not interact well with the expectation semiring, and
previous approaches based on computing expected WER exactly involve expanding
the lattices used during training. In this paper we show how to perform
optimization of the expected WER by sampling paths from the lattices used
during conventional sMBR training. The gradient of the expected WER is itself
an expectation, and so may be approximated using Monte Carlo sampling. We show
experimentally that optimizing WER during acoustic model training gives 5%
relative improvement in WER over a well-tuned sMBR baseline on a 2-channel
query recognition task (Google Home).
Evaluating vector-space models of analogy
Authors: Dawn Chen, Joshua C. Peterson, Thomas L. Griffiths
Date: 2017-05-12 00:00:00
Tasks: Word Embeddings

Vector-space representations provide geometric tools for reasoning about the
similarity of a set of objects and their relationships. Recent machine learning
methods for deriving vector-space embeddings of words (e.g., word2vec) have
achieved considerable success in natural language processing. These vector
spaces have also been shown to exhibit a surprising capacity to capture verbal
analogies, with similar results for natural images, giving new life to a
classic model of analogies as parallelograms that was first proposed by
cognitive scientists. We evaluate the parallelogram model of analogy as applied
to modern word embeddings, providing a detailed analysis of the extent to which
this approach captures human relational similarity judgments in a large
benchmark dataset. We find that that some semantic relationships are better
captured than others. We then provide evidence for deeper limitations of the
parallelogram model based on the intrinsic geometric constraints of vector
spaces, paralleling classic results for first-order similarity.
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
Authors: Yuan Yuan, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Bingshui Da, Qingfu Zhang, Kay Chen Tan, Yaochu Jin, Hisao Ishibuchi
Date: 2017-06-08 00:00:00
Tasks: Multiobjective Optimization

In this report, we suggest nine test problems for multi-task multi-objective
optimization (MTMOO), each of which consists of two multiobjective optimization
tasks that need to be solved simultaneously. The relationship between tasks
varies between different test problems, which would be helpful to have a
comprehensive evaluation of the MO-MFO algorithms. It is expected that the
proposed test problems will germinate progress the field of the MTMOO research.
Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction
Authors: Jekaterina Novikova, Christian Dondrup, Ioannis Papaioannou, Oliver Lemon
Date: 2017-06-08 00:00:00
Proceeding: WS 2017 8
Tasks: Reinforcement Learning

Recognition of social signals, from human facial expressions or prosody of
speech, is a popular research topic in human-robot interaction studies. There
is also a long line of research in the spoken dialogue community that
investigates user satisfaction in relation to dialogue characteristics.
However, very little research relates a combination of multimodal social
signals and language features detected during spoken face-to-face human-robot
interaction to the resulting user perception of a robot. In this paper we show
how different emotional facial expressions of human users, in combination with
prosodic characteristics of human speech and features of human-robot dialogue,
correlate with users' impressions of the robot after a conversation. We find
that happiness in the user's recognised facial expression strongly correlates
with likeability of a robot, while dialogue-related features (such as number of
human turns or number of sentences per robot utterance) correlate with
perceiving a robot as intelligent. In addition, we show that facial expression,
emotional features, and prosody are better predictors of human ratings related
to perceived robot likeability and anthropomorphism, while linguistic and
non-linguistic features more often predict perceived robot intelligence and
interpretability. As such, these characteristics may in future be used as an
online reward signal for in-situ Reinforcement Learning based adaptive
human-robot dialogue systems.
Pruning Convolutional Neural Networks for Resource Efficient Inference
Authors: Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, Jan Kautz
Date: 2016-11-19 00:00:00
Tasks: Transfer Learning

We propose a new formulation for pruning convolutional kernels in neural
networks to enable efficient inference. We interleave greedy criteria-based
pruning with fine-tuning by backpropagation - a computationally efficient
procedure that maintains good generalization in the pruned network. We propose
a new criterion based on Taylor expansion that approximates the change in the
cost function induced by pruning network parameters. We focus on transfer
learning, where large pretrained networks are adapted to specialized tasks. The
proposed criterion demonstrates superior performance compared to other
criteria, e.g. the norm of kernel weights or feature map activation, for
pruning large CNNs after adaptation to fine-grained classification tasks
(Birds-200 and Flowers-102) relaying only on the first order gradient
information. We also show that pruning can lead to more than 10x theoretical
(5x practical) reduction in adapted 3D-convolutional filters with a small drop
in accuracy in a recurrent gesture classifier. Finally, we show results for the
large-scale ImageNet dataset to emphasize the flexibility of our approach.
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
Authors: Takaaki Hori, Shinji Watanabe, Yu Zhang, William Chan
Date: 2017-06-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, General Classification, Language Modeling, Language Modelling, Speech Recognition

We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR)
model. We learn to listen and write characters with a joint Connectionist
Temporal Classification (CTC) and attention-based encoder-decoder network. The
encoder is a deep Convolutional Neural Network (CNN) based on the VGG network.
The CTC network sits on top of the encoder and is jointly trained with the
attention-based decoder. During the beam search process, we combine the CTC
predictions, the attention-based decoder predictions and a separately trained
LSTM language model. We achieve a 5-10\% error reduction compared to prior
systems on spontaneous Japanese and Chinese speech, and our end-to-end model
beats out traditional hybrid ASR systems.
Climbing a shaky ladder: Better adaptive risk estimation
Authors: Moritz Hardt
Date: 2017-06-08 00:00:00

We revisit the \emph{leaderboard problem} introduced by Blum and Hardt (2015)
in an effort to reduce overfitting in machine learning benchmarks. We show that
a randomized version of their Ladder algorithm achieves leaderboard error
O(1/n^{0.4}) compared with the previous best rate of O(1/n^{1/3}).
  Short of proving that our algorithm is optimal, we point out a major obstacle
toward further progress. Specifically, any improvement to our upper bound would
lead to asymptotic improvements in the general adaptive estimation setting as
have remained elusive in recent years. This connection also directly leads to
lower bounds for specific classes of algorithms. In particular, we exhibit a
new attack on the leaderboard algorithm that both theoretically and empirically
distinguishes between our algorithm and previous leaderboard algorithms.
A budget-constrained inverse classification framework for smooth classifiers
Authors: Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson
Date: 2016-05-29 00:00:00
Tasks: Classification, General Classification

Inverse classification is the process of manipulating an instance such that
it is more likely to conform to a specific class. Past methods that address
such a problem have shortcomings. Greedy methods make changes that are overly
radical, often relying on data that is strictly discrete. Other methods rely on
certain data points, the presence of which cannot be guaranteed. In this paper
we propose a general framework and method that overcomes these and other
limitations. The formulation of our method can use any differentiable
classification function. We demonstrate the method by using logistic regression
and Gaussian kernel SVMs. We constrain the inverse classification to occur on
features that can actually be changed, each of which incurs an individual cost.
We further subject such changes to fall within a certain level of cumulative
change (budget). Our framework can also accommodate the estimation of
(indirectly changeable) features whose values change as a consequence of
actions taken. Furthermore, we propose two methods for specifying feature-value
ranges that result in different algorithmic behavior. We apply our method, and
a proposed sensitivity analysis-based benchmark method, to two freely available
datasets: Student Performance from the UCI Machine Learning Repository and a
real world cardiovascular disease dataset. The results obtained demonstrate the
validity and benefits of our framework and method.
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
Authors: Dat Quoc Nguyen, Mark Dras, Mark Johnson
Date: 2017-05-16 00:00:00
Proceeding: CONLL 2017 8
Tasks: Dependency Parsing, Feature Engineering, Part-Of-Speech Tagging, POS, POS Tagging, Transition-Based Dependency Parsing

We present a novel neural network model that learns POS tagging and
graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to
learn feature representations shared for both POS tagging and dependency
parsing tasks, thus handling the feature-engineering problem. Our extensive
experiments, on 19 languages from the Universal Dependencies project, show that
our model outperforms the state-of-the-art neural network-based
Stack-propagation model for joint POS tagging and transition-based dependency
parsing, resulting in a new state of the art. Our code is open-source and
available together with pre-trained models at:
https://github.com/datquocnguyen/jPTDP
Causes and Corrections for Bimodal Multipath Scanning with Structured Light
Authors: Yu Zhang, Daniel L. Lau, Ying Yu
Date: 2017-06-08 00:00:00

Structured light illumination is an active 3-D scanning technique based on
projecting/capturing a set of striped patterns and measuring the warping of the
patterns as they reflect off a target object's surface. As designed, each pixel
in the camera sees exactly one pixel from the projector; however, there are
exceptions to this when the scanned surface has a complicated geometry with
step edges and other discontinuities in depth or where the target surface has
specularities that reflect light away from the camera. These situations are
generally referred to multipath where a given camera pixel receives light from
multiple positions from the projector. In the case of bimodal multipath, the
camera pixel receives light from exactly two positions from the projector which
occurs when light bounce back from a reflective surface or along a step edge
where the edge slices through a pixel so that the pixel sees both a foreground
and background surface. In this paper, we present a general mathematical model
and address the bimodal multipath issue in a phase measuring profilometry
scanner to measure the constructive and destructive interference between the
two light paths, and by taking advantage of this interesting cue, separate the
paths and make two separated depth measurements. We also validate our algorithm
with both simulation and a number of challenging real cases.
Structured Light Phase Measuring Profilometry Pattern Design for Binary Spatial Light Modulators
Authors: Daniel L. Lau, Yu Zhang, Kai Liu
Date: 2017-06-08 00:00:00

Structured light illumination is an active 3-D scanning technique based on
projecting/capturing a set of striped patterns and measuring the warping of the
patterns as they reflect off a target object's surface. In the case of phase
measuring profilometry (PMP), the projected patterns are composed of a rolling
sinusoidal wave, but as a set of time-multiplexed patterns, PMP requires the
target surface to remain motionless or for scanning to be performed at such
high rates that any movement is small. But high speed scanning places a
significant burden on the projector electronics to produce contone patterns
inside of short exposure intervals. Binary patterns are, therefore, of great
value, but converting contone patterns into binary comes with significant risk.
As such, this paper introduces a contone-to-binary conversion algorithm for
deriving binary patterns that best mimic their contone counterparts.
Experimental results will show a greater than 3 times reduction in pattern
noise over traditional halftoning procedures.
The Morphospace of Consciousness
Authors: Xerxes D. Arsiwalla, Ricard Sole, Clement Moulin-Frier, Ivan Herreros, Marti Sanchez-Fibla, Paul Verschure
Date: 2017-05-31 00:00:00

We construct a complexity-based morphospace to study systems-level properties
of conscious & intelligent systems. The axes of this space label 3 complexity
types: autonomous, cognitive & social. Given recent proposals to synthesize
consciousness, a generic complexity-based conceptualization provides a useful
framework for identifying defining features of conscious & synthetic systems.
Based on current clinical scales of consciousness that measure cognitive
awareness and wakefulness, we take a perspective on how contemporary
artificially intelligent machines & synthetically engineered life forms measure
on these scales. It turns out that awareness & wakefulness can be associated to
computational & autonomous complexity respectively. Subsequently, building on
insights from cognitive robotics, we examine the function that consciousness
serves, & argue the role of consciousness as an evolutionary game-theoretic
strategy. This makes the case for a third type of complexity for describing
consciousness: social complexity. Having identified these complexity types,
allows for a representation of both, biological & synthetic systems in a common
morphospace. A consequence of this classification is a taxonomy of possible
conscious machines. We identify four types of consciousness, based on
embodiment: (i) biological consciousness, (ii) synthetic consciousness, (iii)
group consciousness (resulting from group interactions), & (iv) simulated
consciousness (embodied by virtual agents within a simulated reality). This
taxonomy helps in the investigation of comparative signatures of consciousness
across domains, in order to highlight design principles necessary to engineer
conscious machines. This is particularly relevant in the light of recent
developments at the crossroads of cognitive neuroscience, biomedical
engineering, artificial intelligence & biomimetics.
Evidence Against Evidence Theory (?!)
Authors: Mieczysław A. Kłopotek, Andrzej Matuszewski
Date: 2017-06-08 00:00:00

This paper is concerned with the apparent greatest weakness of the
Mathematical Theory of Evidence (MTE) of Shafer \cite{Shafer:76}, which has
been strongly criticized by Wasserman \cite{Wasserman:92ijar} - the
relationship to frequencies.
  Weaknesses of various proposals of probabilistic interpretation of MTE belief
functions are demonstrated.
  A new frequency-based interpretation is presented overcoming various
drawbacks of earlier interpretations.
What Does a Belief Function Believe In ?
Authors: Andrzej Matuszewski, Mieczysław A. Kłopotek
Date: 2017-06-08 00:00:00

The conditioning in the Dempster-Shafer Theory of Evidence has been defined
(by Shafer \cite{Shafer:90} as combination of a belief function and of an
"event" via Dempster rule.
  On the other hand Shafer \cite{Shafer:90} gives a "probabilistic"
interpretation of a belief function (hence indirectly its derivation from a
sample). Given the fact that conditional probability distribution of a
sample-derived probability distribution is a probability distribution derived
from a subsample (selected on the grounds of a conditioning event), the paper
investigates the empirical nature of the Dempster- rule of combination.
  It is demonstrated that the so-called "conditional" belief function is not a
belief function given an event but rather a belief function given manipulation
of original empirical data.\\ Given this, an interpretation of belief function
different from that of Shafer is proposed. Algorithms for construction of
belief networks from data are derived for this interpretation.