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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1901.06620 | Seyedeh Zahra Razavi | S. Zahra Razavi, Lenhart K. Schubert, Benjamin Kane, Mohammad Rafayet
Ali, Kimberly Van Orden and Tianyi Ma | Dialogue Design and Management for Multi-Session Casual Conversation
with Older Adults | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the problem of designing a conversational avatar capable of a
sequence of casual conversations with older adults. Users at risk of
loneliness, social anxiety or a sense of ennui may benefit from practicing such
conversations in private, at their convenience. We describe an automatic spoken
dialogue manager for LISSA, an on-screen virtual agent that can keep older
users involved in conversations over several sessions, each lasting 10-20
minutes. The idea behind LISSA is to improve users' communication skills by
providing feedback on their non-verbal behavior at certain points in the course
of the conversations. In this paper, we analyze the dialogues collected from
the first session between LISSA and each of 8 participants. We examine the
quality of the conversations by comparing the transcripts with those collected
in a WOZ setting. LISSA's contributions to the conversations were judged by
research assistants who rated the extent to which the contributions were
"natural", "on track", "encouraging", "understanding", "relevant", and
"polite". The results show that the automatic dialogue manager was able to
handle conversation with the users smoothly and naturally.
| [
{
"version": "v1",
"created": "Sun, 20 Jan 2019 04:38:57 GMT"
},
{
"version": "v2",
"created": "Wed, 26 Jun 2019 14:53:15 GMT"
}
]
| 1,561,593,600,000 | [
[
"Razavi",
"S. Zahra",
""
],
[
"Schubert",
"Lenhart K.",
""
],
[
"Kane",
"Benjamin",
""
],
[
"Ali",
"Mohammad Rafayet",
""
],
[
"Van Orden",
"Kimberly",
""
],
[
"Ma",
"Tianyi",
""
]
]
|
1901.06622 | Sandra Carrico | Sandra Carrico | Mixed Formal Learning: A Path to Transparent Machine Learning | Accepted IEEE ICSC 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents Mixed Formal Learning, a new architecture that learns
models based on formal mathematical representations of the domain of interest
and exposes latent variables. The second element in the architecture learns a
particular skill, typically by using traditional prediction or classification
mechanisms. Our key findings include that this architecture: (1) Facilitates
transparency by exposing key latent variables based on a learned mathematical
model; (2) Enables Low Shot and Zero Shot training of machine learning without
sacrificing accuracy or recall.
| [
{
"version": "v1",
"created": "Sun, 20 Jan 2019 04:44:12 GMT"
}
]
| 1,548,201,600,000 | [
[
"Carrico",
"Sandra",
""
]
]
|
1901.06965 | Hongyang Gao | Hongyang Gao, Yongjun Chen, Shuiwang Ji | Learning Graph Pooling and Hybrid Convolutional Operations for Text
Representations | 7 pages, WWW19 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the development of graph convolutional networks (GCN), deep learning
methods have started to be used on graph data. In additional to convolutional
layers, pooling layers are another important components of deep learning.
However, no effective pooling methods have been developed for graphs currently.
In this work, we propose the graph pooling (gPool) layer, which employs a
trainable projection vector to measure the importance of nodes in graphs. By
selecting the k-most important nodes to form the new graph, gPool achieves the
same objective as regular max pooling layers operating on images. Another
limitation of GCN when used on graph-based text representation tasks is that,
GCNs do not consider the order information of nodes in graph. To address this
limitation, we propose the hybrid convolutional (hConv) layer that combines GCN
and regular convolutional operations. The hConv layer is capable of increasing
receptive fields quickly and computing features automatically. Based on the
proposed gPool and hConv layers, we develop new deep networks for text
categorization tasks. Our results show that the networks based on gPool and
hConv layers achieves new state-of-the-art performance as compared to baseline
methods.
| [
{
"version": "v1",
"created": "Mon, 21 Jan 2019 15:35:43 GMT"
},
{
"version": "v2",
"created": "Sun, 10 Mar 2019 04:48:56 GMT"
}
]
| 1,552,348,800,000 | [
[
"Gao",
"Hongyang",
""
],
[
"Chen",
"Yongjun",
""
],
[
"Ji",
"Shuiwang",
""
]
]
|
1901.07176 | Anupiya Nugaliyadde Mr | Anupiya Nugaliyadde, Kok Wai Wong, Ferdous Sohel, Hong Xie | Enhancing Semantic Word Representations by Embedding Deeper Word
Relationships | Accepted for the International Conference on Computer and Automation
Engineering (ICCAE) 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Word representations are created using analogy context-based statistics and
lexical relations on words. Word representations are inputs for the learning
models in Natural Language Understanding (NLU) tasks. However, to understand
language, knowing only the context is not sufficient. Reading between the lines
is a key component of NLU. Embedding deeper word relationships which are not
represented in the context enhances the word representation. This paper
presents a word embedding which combines an analogy, context-based statistics
using Word2Vec, and deeper word relationships using Conceptnet, to create an
expanded word representation. In order to fine-tune the word representation,
Self-Organizing Map is used to optimize it. The proposed word representation is
compared with semantic word representations using Simlex 999. Furthermore, the
use of 3D visual representations has shown to be capable of representing the
similarity and association between words. The proposed word representation
shows a Spearman correlation score of 0.886 and provided the best results when
compared to the current state-of-the-art methods, and exceed the human
performance of 0.78.
| [
{
"version": "v1",
"created": "Tue, 22 Jan 2019 05:31:54 GMT"
}
]
| 1,548,201,600,000 | [
[
"Nugaliyadde",
"Anupiya",
""
],
[
"Wong",
"Kok Wai",
""
],
[
"Sohel",
"Ferdous",
""
],
[
"Xie",
"Hong",
""
]
]
|
1901.07191 | Chang-Shing Lee | Chang-Shing Lee, Mei-Hui Wang, Li-Chuang Chen, Yusuke Nojima,
Tzong-Xiang Huang, Jinseok Woo, Naoyuki Kubota, Eri Sato-Shimokawara, Toru
Yamaguchi | A GFML-based Robot Agent for Human and Machine Cooperative Learning on
Game of Go | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper applies a genetic algorithm and fuzzy markup language to construct
a human and smart machine cooperative learning system on game of Go. The
genetic fuzzy markup language (GFML)-based Robot Agent can work on various
kinds of robots, including Palro, Pepper, and TMUs robots. We use the
parameters of FAIR open source Darkforest and OpenGo AI bots to construct the
knowledge base of Open Go Darkforest (OGD) cloud platform for student learning
on the Internet. In addition, we adopt the data from AlphaGo Master sixty
online games as the training data to construct the knowledge base and rule base
of the co-learning system. First, the Darkforest predicts the win rate based on
various simulation numbers and matching rates for each game on OGD platform,
then the win rate of OpenGo is as the final desired output. The experimental
results show that the proposed approach can improve knowledge base and rule
base of the prediction ability based on Darkforest and OpenGo AI bot with
various simulation numbers.
| [
{
"version": "v1",
"created": "Tue, 22 Jan 2019 07:35:08 GMT"
}
]
| 1,548,201,600,000 | [
[
"Lee",
"Chang-Shing",
""
],
[
"Wang",
"Mei-Hui",
""
],
[
"Chen",
"Li-Chuang",
""
],
[
"Nojima",
"Yusuke",
""
],
[
"Huang",
"Tzong-Xiang",
""
],
[
"Woo",
"Jinseok",
""
],
[
"Kubota",
"Naoyuki",
""
],
[
"Sato-Shimokawara",
"Eri",
""
],
[
"Yamaguchi",
"Toru",
""
]
]
|
1901.08129 | Diego Perez Liebana Dr. | Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty, Noburu
Kuno, Andre Kramer, Sam Devlin, Raluca D. Gaina, Daniel Ionita | The Multi-Agent Reinforcement Learning in Malm\"O (MARL\"O) Competition | 2 pages plus references | Challenges in Machine Learning (NIPS Workshop), 2018 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning in multi-agent scenarios is a fruitful research direction, but
current approaches still show scalability problems in multiple games with
general reward settings and different opponent types. The Multi-Agent
Reinforcement Learning in Malm\"O (MARL\"O) competition is a new challenge that
proposes research in this domain using multiple 3D games. The goal of this
contest is to foster research in general agents that can learn across different
games and opponent types, proposing a challenge as a milestone in the direction
of Artificial General Intelligence.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2019 21:01:27 GMT"
}
]
| 1,548,374,400,000 | [
[
"Perez-Liebana",
"Diego",
""
],
[
"Hofmann",
"Katja",
""
],
[
"Mohanty",
"Sharada Prasanna",
""
],
[
"Kuno",
"Noburu",
""
],
[
"Kramer",
"Andre",
""
],
[
"Devlin",
"Sam",
""
],
[
"Gaina",
"Raluca D.",
""
],
[
"Ionita",
"Daniel",
""
]
]
|
1901.08221 | Ajit Narayanan | Ajit Narayanan | When is it right and good for an intelligent autonomous vehicle to take
over control (and hand it back)? | 28 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is much debate in machine ethics about the most appropriate way to
introduce ethical reasoning capabilities into intelligent autonomous machines.
Recent incidents involving autonomous vehicles in which humans have been killed
or injured have raised questions about how we ensure that such vehicles have an
ethical dimension to their behaviour and are therefore trustworthy. The main
problem is that hardwiring such machines with rules not to cause harm or damage
is not consistent with the notion of autonomy and intelligence. Also, such
ethical hardwiring does not leave intelligent autonomous machines with any
course of action if they encounter situations or dilemmas for which they are
not programmed or where some harm is caused no matter what course of action is
taken. Teaching machines so that they learn ethics may also be problematic
given recent findings in machine learning that machines pick up the prejudices
and biases embedded in their learning algorithms or data. This paper describes
a fuzzy reasoning approach to machine ethics. The paper shows how it is
possible for an ethics architecture to reason when taking over from a human
driver is morally justified. The design behind such an ethical reasoner is also
applied to an ethical dilemma resolution case. One major advantage of the
approach is that the ethical reasoner can generate its own data for learning
moral rules (hence, autometric) and thereby reduce the possibility of picking
up human biases and prejudices. The results show that a new type of
metric-based ethics appropriate for autonomous intelligent machines is feasible
and that our current concept of ethical reasoning being largely qualitative in
nature may need revising if want to construct future autonomous machines that
have an ethical dimension to their reasoning so that they become moral
machines.
| [
{
"version": "v1",
"created": "Thu, 24 Jan 2019 03:51:10 GMT"
}
]
| 1,548,374,400,000 | [
[
"Narayanan",
"Ajit",
""
]
]
|
1901.08728 | Rishabh Agarwal | Rishabh Agarwal | Evaluation Function Approximation for Scrabble | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The current state-of-the-art Scrabble agents are not learning-based but
depend on truncated Monte Carlo simulations and the quality of such agents is
contingent upon the time available for running the simulations. This thesis
takes steps towards building a learning-based Scrabble agent using self-play.
Specifically, we try to find a better function approximation for the static
evaluation function used in Scrabble which determines the move goodness at a
given board configuration. In this work, we experimented with evolutionary
algorithms and Bayesian Optimization to learn the weights for an approximate
feature-based evaluation function. However, these optimization methods were not
quite effective, which lead us to explore the given problem from an Imitation
Learning point of view. We also tried to imitate the ranking of moves produced
by the Quackle simulation agent using supervised learning with a neural network
function approximator which takes the raw representation of the Scrabble board
as the input instead of using only a fixed number of handcrafted features.
| [
{
"version": "v1",
"created": "Fri, 25 Jan 2019 04:05:52 GMT"
}
]
| 1,548,633,600,000 | [
[
"Agarwal",
"Rishabh",
""
]
]
|
1901.08813 | Quanshi Zhang | Quanshi Zhang, Lixin Fan, Bolei Zhou | Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep
Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of AAAI 2019 Workshop on Network Interpretability for
Deep Learning
| [
{
"version": "v1",
"created": "Fri, 25 Jan 2019 10:12:23 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Feb 2019 04:29:29 GMT"
},
{
"version": "v3",
"created": "Wed, 29 Jul 2020 13:49:07 GMT"
}
]
| 1,596,067,200,000 | [
[
"Zhang",
"Quanshi",
""
],
[
"Fan",
"Lixin",
""
],
[
"Zhou",
"Bolei",
""
]
]
|
1901.09784 | Yunjuan Wang | Yunjuan Wang and Yong Deng | OWA aggregation of multi-criteria with mixed uncertain fuzzy
satisfactions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We apply the Ordered Weighted Averaging (OWA) operator in multi-criteria
decision-making. To satisfy different kinds of uncertainty, measure based
dominance has been presented to gain the order of different criterion. However,
this idea has not been applied in fuzzy system until now. In this paper, we
focus on the situation where the linguistic satisfactions are fuzzy measures
instead of the exact values. We review the concept of OWA operator and discuss
the order mechanism of fuzzy number. Then we combine with measure-based
dominance to give an overall score of each alternatives. An example is
illustrated to show the whole procedure.
| [
{
"version": "v1",
"created": "Thu, 24 Jan 2019 01:20:27 GMT"
}
]
| 1,548,720,000,000 | [
[
"Wang",
"Yunjuan",
""
],
[
"Deng",
"Yong",
""
]
]
|
1901.09786 | David Kupeev | Dr. David Kupeev | AlteregoNets: a way to human augmentation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A person dependent network, called an AlterEgo net, is proposed for
development. The networks are created per person. It receives at input an
object descriptions and outputs a simulation of the internal person's
representation of the objects. The network generates a textual stream
resembling the narrative stream of consciousness depicting multitudinous
thoughts and feelings related to a perceived object. In this way, the object is
described not by a 'static' set of its properties, like a dictionary, but by
the stream of words and word combinations referring to the object. The network
simulates a person's dialogue with a representation of the object. It is based
on an introduced algorithmic scheme, where perception is modeled by two
interacting iterative cycles, reminding one respectively the forward and
backward propagation executed at training convolution neural networks. The
'forward' iterations generate a stream representing the 'internal world' of a
human. The 'backward' iterations generate a stream representing an internal
representation of the object. People perceive the world differently. Tuning
AlterEgo nets to a specific person or group of persons, will allow simulation
of their thoughts and feelings. Thereby these nets is potentially a new human
augmentation technology for various applications.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2019 16:21:02 GMT"
}
]
| 1,548,720,000,000 | [
[
"Kupeev",
"Dr. David",
""
]
]
|
1901.09793 | Nicolas Beldiceanu | Ekaterina Arafailova and Nicolas Beldiceanu and Helmut Simonis | Synthesising a Database of Parameterised Linear and Non-Linear
Invariants for Time-Series Constraints | 42 pages, 14 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many constraints restricting the result of some computations over an integer
sequence can be compactly represented by register automata. We improve the
propagation of the conjunction of such constraints on the same sequence by
synthesising a database of linear and non-linear invariants using their
register-automaton representation. The obtained invariants are formulae
parameterised by a function of the sequence length and proven to be true for
any long enough sequence. To assess the quality of such linear invariants, we
developed a method to verify whether a generated linear invariant is a facet of
the convex hull of the feasible points. This method, as well as the proof of
non-linear invariants, are based on the systematic generation of constant-size
deterministic finite automata that accept all integer sequences whose result
verifies some simple condition. We apply such methodology to a set of 44
time-series constraints and obtain 1400 linear invariants from which 70% are
facet defining, and 600 non-linear invariants, which were tested on short-term
electricity production problems.
| [
{
"version": "v1",
"created": "Tue, 15 Jan 2019 13:43:42 GMT"
}
]
| 1,548,720,000,000 | [
[
"Arafailova",
"Ekaterina",
""
],
[
"Beldiceanu",
"Nicolas",
""
],
[
"Simonis",
"Helmut",
""
]
]
|
1901.09867 | Claudio Tomazzoli | Matteo Cristani, Francesco Domenichini, Claudio Tomazzoli, and Luca
Vigan\`o and Margherita Zorzi | It could be worse, it could be raining: reliable automatic
meteorological forecasting | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Meteorological forecasting provides reliable prediction about the future
weather within a given interval of time. Meteorological forecasting can be
viewed as a form of hybrid diagnostic reasoning and can be mapped onto an
integrated conceptual framework. The automation of the forecasting process
would be helpful in a number of contexts, in particular: when the amount of
data is too wide to be dealt with manually; to support forecasters education;
when forecasting about underpopulated geographic areas is not interesting for
everyday life (and then is out from human forecasters' tasks) but is central
for tourism sponsorship. We present logic MeteoLOG, a framework that models the
main steps of the reasoner the forecaster adopts to provide a bulletin.
MeteoLOG rests on several traditions, mainly on fuzzy, temporal and
probabilistic logics. On this basis, we also introduce the algorithm
Tournament, that transforms a set of MeteoLOG rules into a defeasible theory,
that can be implemented into an automatic reasoner. We finally propose an
example that models a real world forecasting scenario.
| [
{
"version": "v1",
"created": "Mon, 28 Jan 2019 18:18:04 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Feb 2019 08:43:05 GMT"
}
]
| 1,549,843,200,000 | [
[
"Cristani",
"Matteo",
""
],
[
"Domenichini",
"Francesco",
""
],
[
"Tomazzoli",
"Claudio",
""
],
[
"Viganò",
"Luca",
""
],
[
"Zorzi",
"Margherita",
""
]
]
|
1901.09894 | Soheila Sadeghiram | Soheila Sadeghiram, Hui Ma, Gang Chen | Composing Distributed Data-intensive Web Services Using a Flexible
Memetic Algorithm | arXiv admin note: text overlap with arXiv:1901.05564 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Web Service Composition (WSC) is a particularly promising application of Web
services, where multiple individual services with specific functionalities are
composed to accomplish a more complex task, which must fulfil functional
requirements and optimise Quality of Service (QoS) attributes, simultaneously.
Additionally, large quantities of data, produced by technological advances,
need to be exchanged between services. Data-intensive Web services, which
manipulate and deal with those data, are of great interest to implement
data-intensive processes, such as distributed Data-intensive Web Service
Composition (DWSC). Researchers have proposed Evolutionary Computing (EC)
fully-automated WSC techniques that meet all the above factors. Some of these
works employed Memetic Algorithms (MAs) to enhance the performance of EC
through increasing its exploitation ability of in searching neighbourhood area
of a solution. However, those works are not efficient or effective. This paper
proposes an MA-based approach to solving the problem of distributed DWSC in an
effective and efficient manner. In particular, we develop an MA that hybridises
EC with a flexible local search technique incorporating distance of services.
An evaluation using benchmark datasets is carried out, comparing existing
state-of-the-art methods. Results show that our proposed method has the highest
quality and an acceptable execution time overall.
| [
{
"version": "v1",
"created": "Sat, 26 Jan 2019 23:50:05 GMT"
}
]
| 1,548,806,400,000 | [
[
"Sadeghiram",
"Soheila",
""
],
[
"Ma",
"Hui",
""
],
[
"Chen",
"Gang",
""
]
]
|
1901.10051 | Kun Qian | Phokion G. Kolaitis, Lucian Popa, and Kun Qian | Knowledge Refinement via Rule Selection | null | null | 10.1609/aaai.v33i01.33012886 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In several different applications, including data transformation and entity
resolution, rules are used to capture aspects of knowledge about the
application at hand. Often, a large set of such rules is generated
automatically or semi-automatically, and the challenge is to refine the
encapsulated knowledge by selecting a subset of rules based on the expected
operational behavior of the rules on available data. In this paper, we carry
out a systematic complexity-theoretic investigation of the following rule
selection problem: given a set of rules specified by Horn formulas, and a pair
of an input database and an output database, find a subset of the rules that
minimizes the total error, that is, the number of false positive and false
negative errors arising from the selected rules. We first establish
computational hardness results for the decision problems underlying this
minimization problem, as well as upper and lower bounds for its
approximability. We then investigate a bi-objective optimization version of the
rule selection problem in which both the total error and the size of the
selected rules are taken into account. We show that testing for membership in
the Pareto front of this bi-objective optimization problem is DP-complete.
Finally, we show that a similar DP-completeness result holds for a bi-level
optimization version of the rule selection problem, where one minimizes first
the total error and then the size.
| [
{
"version": "v1",
"created": "Tue, 29 Jan 2019 00:37:24 GMT"
}
]
| 1,604,361,600,000 | [
[
"Kolaitis",
"Phokion G.",
""
],
[
"Popa",
"Lucian",
""
],
[
"Qian",
"Kun",
""
]
]
|
1901.10072 | Xinyang Deng | Xinyang Deng and Wen Jiang | On the negation of a Dempster-Shafer belief structure based on maximum
uncertainty allocation | 10 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probability theory and Dempster-Shafer theory are two germane theories to
represent and handle uncertain information. Recent study suggested a
transformation to obtain the negation of a probability distribution based on
the maximum entropy. Correspondingly, determining the negation of a belief
structure, however, is still an open issue in Dempster-Shafer theory, which is
very important in theoretical research and practical applications. In this
paper, a negation transformation for belief structures is proposed based on
maximum uncertainty allocation, and several important properties satisfied by
the transformation have been studied. The proposed negation transformation is
more general and could totally compatible with existing transformation for
probability distributions.
| [
{
"version": "v1",
"created": "Tue, 29 Jan 2019 02:35:19 GMT"
}
]
| 1,548,806,400,000 | [
[
"Deng",
"Xinyang",
""
],
[
"Jiang",
"Wen",
""
]
]
|
1901.10405 | Thomas Ringstrom | Thomas J. Ringstrom, Paul R. Schrater | Constraint Satisfaction Propagation: Non-stationary Policy Synthesis for
Temporal Logic Planning | Preprint. In progress. 10 Pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Problems arise when using reward functions to capture dependencies between
sequential time-constrained goal states because the state-space must be
prohibitively expanded to accommodate a history of successfully achieved
sub-goals. Also, policies and value functions derived with stationarity
assumptions are not readily decomposable, leading to a tension between reward
maximization and task generalization. We demonstrate a logic-compatible
approach using model-based knowledge of environment dynamics and deadline
information to directly infer non-stationary policies composed of reusable
stationary policies. The policies are constructed to maximize the probability
of satisfying time-sensitive goals while respecting time-varying obstacles. Our
approach explicitly maintains two different spaces, a high-level logical task
specification where the task-variables are grounded onto the low-level
state-space of a Markov decision process. Computing satisfiability at the
task-level is made possible by a Bellman-like equation which operates on a
tensor that links the temporal relationship between the two spaces; the
equation solves for a value function that can be explicitly interpreted as the
probability of sub-goal satisfaction under the synthesized non-stationary
policy, an approach we term Constraint Satisfaction Propagation (CSP).
| [
{
"version": "v1",
"created": "Tue, 29 Jan 2019 17:19:06 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Jan 2019 20:34:57 GMT"
},
{
"version": "v3",
"created": "Mon, 11 Feb 2019 21:55:44 GMT"
}
]
| 1,550,016,000,000 | [
[
"Ringstrom",
"Thomas J.",
""
],
[
"Schrater",
"Paul R.",
""
]
]
|
1901.11184 | Mark Riedl | Mark O. Riedl | Human-Centered Artificial Intelligence and Machine Learning | Human Behavior and Emerging Technologies, volume 1 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans are increasingly coming into contact with artificial intelligence and
machine learning systems. Human-centered artificial intelligence is a
perspective on AI and ML that algorithms must be designed with awareness that
they are part of a larger system consisting of humans. We lay forth an argument
that human-centered artificial intelligence can be broken down into two
aspects: (1) AI systems that understand humans from a sociocultural
perspective, and (2) AI systems that help humans understand them. We further
argue that issues of social responsibility such as fairness, accountability,
interpretability, and transparency.
| [
{
"version": "v1",
"created": "Thu, 31 Jan 2019 02:47:16 GMT"
}
]
| 1,548,979,200,000 | [
[
"Riedl",
"Mark O.",
""
]
]
|
1901.11529 | Himanshu Sahni | Himanshu Sahni, Toby Buckley, Pieter Abbeel, Ilya Kuzovkin | Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory
GANs | To appear in Neural Information Processing Systems (NeurIPS 2019),
Vancouver, Canada. Code available at
https://github.com/offworld-projects/research-halgan | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement Learning (RL) algorithms typically require millions of
environment interactions to learn successful policies in sparse reward
settings. Hindsight Experience Replay (HER) was introduced as a technique to
increase sample efficiency by reimagining unsuccessful trajectories as
successful ones by altering the originally intended goals. However, it cannot
be directly applied to visual environments where goal states are often
characterized by the presence of distinct visual features. In this work, we
show how visual trajectories can be hallucinated to appear successful by
altering agent observations using a generative model trained on relatively few
snapshots of the goal. We then use this model in combination with HER to train
RL agents in visual settings. We validate our approach on 3D navigation tasks
and a simulated robotics application and show marked improvement over baselines
derived from previous work.
| [
{
"version": "v1",
"created": "Thu, 31 Jan 2019 18:50:44 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Oct 2019 02:23:49 GMT"
}
]
| 1,572,480,000,000 | [
[
"Sahni",
"Himanshu",
""
],
[
"Buckley",
"Toby",
""
],
[
"Abbeel",
"Pieter",
""
],
[
"Kuzovkin",
"Ilya",
""
]
]
|
1902.00014 | Carsten Lutz | Elena Botoeva and Carsten Lutz and Vladislav Ryzhikov and Frank Wolter
and Michael Zakharyaschev | Query Inseparability for ALC Ontologies | arXiv admin note: text overlap with arXiv:1604.04164 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the problem whether two ALC ontologies are indistinguishable
(or inseparable) by means of queries in a given signature, which is fundamental
for ontology engineering tasks such as ontology versioning, modularisation,
update, and forgetting. We consider both knowledge base (KB) and TBox
inseparability. For KBs, we give model-theoretic criteria in terms of (finite
partial) homomorphisms and products and prove that this problem is undecidable
for conjunctive queries (CQs), but 2ExpTime-complete for unions of CQs (UCQs).
The same results hold if (U)CQs are replaced by rooted (U)CQs, where every
variable is connected to an answer variable. We also show that inseparability
by CQs is still undecidable if one KB is given in the lightweight DL EL and if
no restrictions are imposed on the signature of the CQs. We also consider the
problem whether two ALC TBoxes give the same answers to any query over any ABox
in a given signature and show that, for CQs, this problem is undecidable, too.
We then develop model-theoretic criteria for Horn-ALC TBoxes and show using
tree automata that, in contrast, inseparability becomes decidable and
2ExpTime-complete, even ExpTime-complete when restricted to (unions of) rooted
CQs.
| [
{
"version": "v1",
"created": "Thu, 31 Jan 2019 13:58:48 GMT"
}
]
| 1,549,238,400,000 | [
[
"Botoeva",
"Elena",
""
],
[
"Lutz",
"Carsten",
""
],
[
"Ryzhikov",
"Vladislav",
""
],
[
"Wolter",
"Frank",
""
],
[
"Zakharyaschev",
"Michael",
""
]
]
|
1902.00120 | Felix Hill Mr | Felix Hill, Adam Santoro, David G.T. Barrett, Ari S. Morcos and
Timothy Lillicrap | Learning to Make Analogies by Contrasting Abstract Relational Structure | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Analogical reasoning has been a principal focus of various waves of AI
research. Analogy is particularly challenging for machines because it requires
relational structures to be represented such that they can be flexibly applied
across diverse domains of experience. Here, we study how analogical reasoning
can be induced in neural networks that learn to perceive and reason about raw
visual data. We find that the critical factor for inducing such a capacity is
not an elaborate architecture, but rather, careful attention to the choice of
data and the manner in which it is presented to the model. The most robust
capacity for analogical reasoning is induced when networks learn analogies by
contrasting abstract relational structures in their input domains, a training
method that uses only the input data to force models to learn about important
abstract features. Using this technique we demonstrate capacities for complex,
visual and symbolic analogy making and generalisation in even the simplest
neural network architectures.
| [
{
"version": "v1",
"created": "Thu, 31 Jan 2019 23:10:31 GMT"
}
]
| 1,549,238,400,000 | [
[
"Hill",
"Felix",
""
],
[
"Santoro",
"Adam",
""
],
[
"Barrett",
"David G. T.",
""
],
[
"Morcos",
"Ari S.",
""
],
[
"Lillicrap",
"Timothy",
""
]
]
|
1902.00287 | Jeroen Berrevoets | Jeroen Berrevoets and Wouter Verbeke | Causal Simulations for Uplift Modeling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Uplift modeling requires experimental data, preferably collected in random
fashion. This places a logistical and financial burden upon any organisation
aspiring such models. Once deployed, uplift models are subject to effects from
concept drift. Hence, methods are being developed that are able to learn from
newly gained experience, as well as handle drifting environments. As these new
methods attempt to eliminate the need for experimental data, another approach
to test such methods must be formulated. Therefore, we propose a method to
simulate environments that offer causal relationships in their parameters.
| [
{
"version": "v1",
"created": "Fri, 1 Feb 2019 11:46:36 GMT"
}
]
| 1,549,238,400,000 | [
[
"Berrevoets",
"Jeroen",
""
],
[
"Verbeke",
"Wouter",
""
]
]
|
1902.00604 | Yu Zhang | Yu Zhang and Mehrdad Zakershahrak | Progressive Explanation Generation for Human-robot Teaming | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generating explanation to explain its behavior is an essential capability for
a robotic teammate. Explanations help human partners better understand the
situation and maintain trust of their teammates. Prior work on robot generating
explanations focuses on providing the reasoning behind its decision making.
These approaches, however, fail to heed the cognitive requirement of
understanding an explanation. In other words, while they provide the right
explanations from the explainer's perspective, the explainee part of the
equation is ignored. In this work, we address an important aspect along this
direction that contributes to a better understanding of a given explanation,
which we refer to as the progressiveness of explanations. A progressive
explanation improves understanding by limiting the cognitive effort required at
each step of making the explanation. As a result, such explanations are
expected to be smoother and hence easier to understand. A general formulation
of progressive explanation is presented. Algorithms are provided based on
several alternative quantifications of cognitive effort as an explanation is
being made, which are evaluated in a standard planning competition domain.
| [
{
"version": "v1",
"created": "Sat, 2 Feb 2019 01:02:59 GMT"
}
]
| 1,549,324,800,000 | [
[
"Zhang",
"Yu",
""
],
[
"Zakershahrak",
"Mehrdad",
""
]
]
|
1902.00659 | M. Hanefi Calp | Muhammed Hanefi Calp, Muhammet Ali Akcayol | Optimization of Project Scheduling Activities in Dynamic CPM and PERT
Networks Using Genetic Algorithms | 13 pages | null | 10.19113/sdufbed.35437 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Projects consist of interconnected dimensions such as objective, time,
resource and environment. Use of these dimensions in a controlled way and their
effective scheduling brings the project success. Project scheduling process
includes defining project activities, and estimation of time and resources to
be used for the activities. At this point, the project resource-scheduling
problems have begun to attract more attention after Program Evaluation and
Review Technique (PERT) and Critical Path Method (CPM) are developed one after
the other. However, complexity and difficulty of CPM and PERT processes led to
the use of these techniques through artificial intelligence methods such as
Genetic Algorithm (GA). In this study, an algorithm was proposed and developed,
which determines critical path, critical activities and project completion
duration by using GA, instead of CPM and PERT techniques used for network
analysis within the scope of project management. The purpose of using GA was
that these algorithms are an effective method for solution of complex
optimization problems. Therefore, correct decisions can be made for implemented
project activities by using obtained results. Thus, optimum results were
obtained in a shorter time than the CPM and PERT techniques by using the model
based on the dynamic algorithm. It is expected that this study will contribute
to the performance field (time, speed, low error etc.) of other studies.
| [
{
"version": "v1",
"created": "Sat, 2 Feb 2019 07:22:07 GMT"
}
]
| 1,549,324,800,000 | [
[
"Calp",
"Muhammed Hanefi",
""
],
[
"Akcayol",
"Muhammet Ali",
""
]
]
|
1902.00673 | Arun Kumar | Arun Kumar, Zhengwei Wu, Xaq Pitkow, Paul Schrater | Belief dynamics extraction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Animal behavior is not driven simply by its current observations, but is
strongly influenced by internal states. Estimating the structure of these
internal states is crucial for understanding the neural basis of behavior. In
principle, internal states can be estimated by inverting behavior models, as in
inverse model-based Reinforcement Learning. However, this requires careful
parameterization and risks model-mismatch to the animal. Here we take a
data-driven approach to infer latent states directly from observations of
behavior, using a partially observable switching semi-Markov process. This
process has two elements critical for capturing animal behavior: it captures
non-exponential distribution of times between observations, and transitions
between latent states depend on the animal's actions, features that require
more complex non-markovian models to represent. To demonstrate the utility of
our approach, we apply it to the observations of a simulated optimal agent
performing a foraging task, and find that latent dynamics extracted by the
model has correspondences with the belief dynamics of the agent. Finally, we
apply our model to identify latent states in the behaviors of monkey performing
a foraging task, and find clusters of latent states that identify periods of
time consistent with expectant waiting. This data-driven behavioral model will
be valuable for inferring latent cognitive states, and thereby for measuring
neural representations of those states.
| [
{
"version": "v1",
"created": "Sat, 2 Feb 2019 09:05:46 GMT"
}
]
| 1,549,324,800,000 | [
[
"Kumar",
"Arun",
""
],
[
"Wu",
"Zhengwei",
""
],
[
"Pitkow",
"Xaq",
""
],
[
"Schrater",
"Paul",
""
]
]
|
1902.00741 | Benjamin Goertzel | Ben Goertzel | Distinction Graphs and Graphtropy: A Formalized Phenomenological Layer
Underlying Classical and Quantum Entropy, Observational Semantics and
Cognitive Computation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new conceptual foundation for the notion of "information" is proposed,
based on the concept of a "distinction graph": a graph in which two nodes are
connected iff they cannot be distinguished by a particular observer. The
"graphtropy" of a distinction graph is defined as the average connection
probability of two nodes; in the case where the distinction graph is a composed
of disconnected components that are fully connected subgraphs, this is
equivalent to Ellerman's logical entropy, which has straightforward
relationships to Shannon entropy. Probabilistic distinction graphs and
probabilistic graphtropy are also considered, as well as connections between
graphtropy and thermodynamic and quantum entropy. The semantics of the Second
Law of Thermodynamics and the Maximum Entropy Production Principle are unfolded
in a novel way, via analysis of the cognitive processes underlying the making
of distinction graphs This evokes an interpretation in which complex
intelligence is seen to correspond to states of consciousness with intermediate
graphtropy, which are associated with memory imperfections that violate the
assumptions leading to derivation of the Second Law. In the case where nodes of
a distinction graph are labeled by computable entities, graphtropy is shown to
be monotonically related to the average algorithmic information of the nodes
(relative to to the algorithmic information of the observer). A
quantum-mechanical version of distinction graphs is considered, in which
distinctions can exist in a superposed state; this yields to graphtropy as a
measure of the impurity of a mixed state, and to a concept of "quangraphtropy."
Finally, a novel computational model called Dynamic Distinction Graphs (DDGs)
is formulated, via enhancing distinction graphs with additional links
expressing causal implications, enabling a distinction-based model of
"observers."
| [
{
"version": "v1",
"created": "Sat, 2 Feb 2019 15:59:29 GMT"
}
]
| 1,549,324,800,000 | [
[
"Goertzel",
"Ben",
""
]
]
|
1902.00771 | Adi Botea | Adi Botea, Christian Muise, Shubham Agarwal, Oznur Alkan, Ondrej
Bajgar, Elizabeth Daly, Akihiro Kishimoto, Luis Lastras, Radu Marinescu,
Josef Ondrej, Pablo Pedemonte, Miroslav Vodolan | Generating Dialogue Agents via Automated Planning | Accepted at the AAAI-2019 DEEP-DIAL workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dialogue systems have many applications such as customer support or question
answering. Typically they have been limited to shallow single turn
interactions. However more advanced applications such as career coaching or
planning a trip require a much more complex multi-turn dialogue. Current
limitations of conversational systems have made it difficult to support
applications that require personalization, customization and context dependent
interactions. We tackle this challenging problem by using domain-independent AI
planning to automatically create dialogue plans, customized to guide a dialogue
towards achieving a given goal. The input includes a library of atomic dialogue
actions, an initial state of the dialogue, and a goal. Dialogue plans are
plugged into a dialogue system capable to orchestrate their execution. Use
cases demonstrate the viability of the approach. Our work on dialogue planning
has been integrated into a product, and it is in the process of being deployed
into another.
| [
{
"version": "v1",
"created": "Sat, 2 Feb 2019 19:23:30 GMT"
}
]
| 1,549,324,800,000 | [
[
"Botea",
"Adi",
""
],
[
"Muise",
"Christian",
""
],
[
"Agarwal",
"Shubham",
""
],
[
"Alkan",
"Oznur",
""
],
[
"Bajgar",
"Ondrej",
""
],
[
"Daly",
"Elizabeth",
""
],
[
"Kishimoto",
"Akihiro",
""
],
[
"Lastras",
"Luis",
""
],
[
"Marinescu",
"Radu",
""
],
[
"Ondrej",
"Josef",
""
],
[
"Pedemonte",
"Pablo",
""
],
[
"Vodolan",
"Miroslav",
""
]
]
|
1902.00916 | Tom Hanika | Tom Hanika and Maximilian Marx and Gerd Stumme | Discovering Implicational Knowledge in Wikidata | null | null | 10.1007/978-3-030-21462-3_21 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graphs have recently become the state-of-the-art tool for
representing the diverse and complex knowledge of the world. Examples include
the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or
Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata.
A distinguishing feature of Wikidata is that the knowledge is collaboratively
edited and curated. While this greatly enhances the scope of Wikidata, it also
makes it impossible for a single individual to grasp complex connections
between properties or understand the global impact of edits in the graph. We
apply Formal Concept Analysis to efficiently identify comprehensible
implications that are implicitly present in the data. Although the complex
structure of data modelling in Wikidata is not amenable to a direct approach,
we overcome this limitation by extracting contextual representations of parts
of Wikidata in a systematic fashion. We demonstrate the practical feasibility
of our approach through several experiments and show that the results may lead
to the discovery of interesting implicational knowledge. Besides providing a
method for obtaining large real-world data sets for FCA, we sketch potential
applications in offering semantic assistance for editing and curating Wikidata.
| [
{
"version": "v1",
"created": "Sun, 3 Feb 2019 16:13:53 GMT"
}
]
| 1,582,848,000,000 | [
[
"Hanika",
"Tom",
""
],
[
"Marx",
"Maximilian",
""
],
[
"Stumme",
"Gerd",
""
]
]
|
1902.01193 | Akeem Amusat | O.M. Alade, A.O. Amusat | Solving Nurse Scheduling Problem Using Constraint Programming Technique | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Staff scheduling is a universal problem that can be encountered in many
organizations, such as call centers, educational institution, industry,
hospital, and any other public services. It is one of the most important
aspects of workforce management strategy and the one that is most prone to
errors or issues as there are many entities should be considered, such as the
staff turnover, employee availability, time between rotations, unusual periods
of activity, and even the last-minute shift changes. The nurse scheduling
problem is a variant of staff scheduling problems which appoints nurses to
shifts as well as rooms per day taking both hard constraints, i.e., hospital
requirements, and soft constraints, i.e., nurse preferences, into account. Most
algorithms used for scheduling problems fall short when it comes to the number
of inputs they can handle. In this paper, constraint programming was developed
to solve the nurse scheduling problem. The developed constraint programming
model was then implemented using python programming language.
| [
{
"version": "v1",
"created": "Mon, 4 Feb 2019 14:09:29 GMT"
}
]
| 1,549,324,800,000 | [
[
"Alade",
"O. M.",
""
],
[
"Amusat",
"A. O.",
""
]
]
|
1902.01360 | M. Hanefi Calp | Murat Dener, M. Hanefi Calp | Solving The Exam Scheduling Problems in Central Exams With Genetic
Algorithms | 14 pages | null | 10.22531/muglajsci.423185 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is the efficient use of resources expected from an exam scheduling
application. There are various criteria for efficient use of resources and for
all tests to be carried out at minimum cost in the shortest possible time. It
is aimed that educational institutions with such criteria successfully carry
out central examination organizations. In the study, a two-stage genetic
algorithm was developed. In the first stage, the assignment of courses to
sessions was carried out. In the second stage, the students who participated in
the test session were assigned to examination rooms. Purposes of the study are
increasing the number of joint students participating in sessions, using the
minimum number of buildings in the same session, and reducing the number of
supervisors using the minimum number of classrooms possible. In this study, a
general purpose exam scheduling solution for educational institutions was
presented. The developed system can be used in different central examinations
to create originality. Given the results of the sample application, it is seen
that the proposed genetic algorithm gives successful results.1
| [
{
"version": "v1",
"created": "Mon, 4 Feb 2019 18:21:37 GMT"
}
]
| 1,549,324,800,000 | [
[
"Dener",
"Murat",
""
],
[
"Calp",
"M. Hanefi",
""
]
]
|
1902.01362 | M. Hanefi Calp | M. H. Calp | Evaluation of Multidisciplinary Effects of Artificial Intelligence with
Optimization Perspective | 9 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence has an important place in the scientific community as
a result of its successful outputs in terms of different fields. In time, the
field of Artificial Intelligence has been divided into many sub-fields because
of increasing number of different solution approaches, methods, and techniques.
Machine Learning has the most remarkable role with its functions to learn from
samples from the environment. On the other hand, intelligent optimization done
by inspiring from nature and swarms had its own unique scientific literature,
with effective solutions provided for optimization problems from different
fields. Because intelligent optimization can be applied in different fields
effectively, this study aims to provide a general discussion on
multidisciplinary effects of Artificial Intelligence by considering its
optimization oriented solutions. The study briefly focuses on background of the
intelligent optimization briefly and then gives application examples of
intelligent optimization from a multidisciplinary perspective.
| [
{
"version": "v1",
"created": "Mon, 4 Feb 2019 18:26:12 GMT"
}
]
| 1,549,324,800,000 | [
[
"Calp",
"M. H.",
""
]
]
|
1902.01769 | Dustin Dannenhauer | Dustin Dannenhauer, Michael W. Floyd, Jonathan Decker, David W. Aha | Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial
Intelligence | AAAI-19 Workshop on Games and Simulations for Artificial Intelligence | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dungeon Crawl Stone Soup is a popular, single-player, free and open-source
rogue-like video game with a sufficiently complex decision space that makes it
an ideal testbed for research in cognitive systems and, more generally,
artificial intelligence. This paper describes the properties of Dungeon Crawl
Stone Soup that are conducive to evaluating new approaches of AI systems. We
also highlight an ongoing effort to build an API for AI researchers in the
spirit of recent game APIs such as MALMO, ELF, and the Starcraft II API.
Dungeon Crawl Stone Soup's complexity offers significant opportunities for
evaluating AI and cognitive systems, including human user studies. In this
paper we provide (1) a description of the state space of Dungeon Crawl Stone
Soup, (2) a description of the components for our API, and (3) the potential
benefits of evaluating AI agents in the Dungeon Crawl Stone Soup video game.
| [
{
"version": "v1",
"created": "Tue, 5 Feb 2019 16:26:56 GMT"
}
]
| 1,549,411,200,000 | [
[
"Dannenhauer",
"Dustin",
""
],
[
"Floyd",
"Michael W.",
""
],
[
"Decker",
"Jonathan",
""
],
[
"Aha",
"David W.",
""
]
]
|
1902.01876 | Shane Mueller | Shane T. Mueller, Robert R. Hoffman, William Clancey, Abigail Emrey,
Gary Klein | Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of
Key Ideas and Publications, and Bibliography for Explainable AI | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is an integrative review that address the question, "What makes for a
good explanation?" with reference to AI systems. Pertinent literatures are
vast. Thus, this review is necessarily selective. That said, most of the key
concepts and issues are expressed in this Report. The Report encapsulates the
history of computer science efforts to create systems that explain and instruct
(intelligent tutoring systems and expert systems). The Report expresses the
explainability issues and challenges in modern AI, and presents capsule views
of the leading psychological theories of explanation. Certain articles stand
out by virtue of their particular relevance to XAI, and their methods, results,
and key points are highlighted. It is recommended that AI/XAI researchers be
encouraged to include in their research reports fuller details on their
empirical or experimental methods, in the fashion of experimental psychology
research reports: details on Participants, Instructions, Procedures, Tasks,
Dependent Variables (operational definitions of the measures and metrics),
Independent Variables (conditions), and Control Conditions.
| [
{
"version": "v1",
"created": "Tue, 5 Feb 2019 19:16:17 GMT"
}
]
| 1,549,497,600,000 | [
[
"Mueller",
"Shane T.",
""
],
[
"Hoffman",
"Robert R.",
""
],
[
"Clancey",
"William",
""
],
[
"Emrey",
"Abigail",
""
],
[
"Klein",
"Gary",
""
]
]
|
1902.01886 | Nikhil Krishnaswamy | James Pustejovsky and Nikhil Krishnaswamy | Situational Grounding within Multimodal Simulations | AAAI-19 Workshop on Games and Simulations for Artificial Intelligence | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we argue that simulation platforms enable a novel type of
embodied spatial reasoning, one facilitated by a formal model of object and
event semantics that renders the continuous quantitative search space of an
open-world, real-time environment tractable. We provide examples for how a
semantically-informed AI system can exploit the precise, numerical information
provided by a game engine to perform qualitative reasoning about objects and
events, facilitate learning novel concepts from data, and communicate with a
human to improve its models and demonstrate its understanding. We argue that
simulation environments, and game engines in particular, bring together many
different notions of "simulation" and many different technologies to provide a
highly-effective platform for developing both AI systems and tools to
experiment in both machine and human intelligence.
| [
{
"version": "v1",
"created": "Tue, 5 Feb 2019 19:49:56 GMT"
}
]
| 1,549,497,600,000 | [
[
"Pustejovsky",
"James",
""
],
[
"Krishnaswamy",
"Nikhil",
""
]
]
|
1902.02132 | Felix Diaz Hermida | F\'elix D\'iaz-Hermida, Marcos Matabuena, Juan C. Vidal | The FA Quantifier Fuzzification Mechanism: analysis of convergence and
efficient implementations | 22 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The fuzzy quantification model FA has been identified as one of the best
behaved quantification models in several revisions of the field of fuzzy
quantification. This model is, to our knowledge, the unique one fulfilling the
strict Determiner Fuzzification Scheme axiomatic framework that does not induce
the standard min and max operators. The main contribution of this paper is the
proof of a convergence result that links this quantification model with the
Zadeh's model when the size of the input sets tends to infinite. The
convergence proof is, in any case, more general than the convergence to the
Zadeh's model, being applicable to any quantitative quantifier. In addition,
recent revisions papers have presented some doubts about the existence of
suitable computational implementations to evaluate the FA model in practical
applications. In order to prove that this model is not only a theoretical
approach, we show exact algorithmic solutions for the most common linguistic
quantifiers as well as an approximate implementation by means of Monte Carlo.
Additionally, we will also give a general overview of the main properties
fulfilled by the FA model, as a single compendium integrating the whole set of
properties fulfilled by it has not been previously published.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2019 12:17:08 GMT"
}
]
| 1,549,497,600,000 | [
[
"Díaz-Hermida",
"Félix",
""
],
[
"Matabuena",
"Marcos",
""
],
[
"Vidal",
"Juan C.",
""
]
]
|
1902.02194 | Romain Edelmann | Romain Edelmann, Viktor Kun\v{c}ak | Neural-Network Guided Expression Transformation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimizing compilers, as well as other translator systems, often work by
rewriting expressions according to equivalence preserving rules. Given an input
expression and its optimized form, finding the sequence of rules that were
applied is a non-trivial task. Most of the time, the tools provide no proof, of
any kind, of the equivalence between the original expression and its optimized
form. In this work, we propose to reconstruct proofs of equivalence of simple
mathematical expressions, after the fact, by finding paths of equivalence
preserving transformations between expressions. We propose to find those
sequences of transformations using a search algorithm, guided by a neural
network heuristic. Using a Tree-LSTM recursive neural network, we learn a
distributed representation of expressions where the Manhattan distance between
vectors approximately corresponds to the rewrite distance between expressions.
We then show how the neural network can be efficiently used to search for
transformation paths, leading to substantial gain in speed compared to an
uninformed exhaustive search. In one of our experiments, our neural-network
guided search algorithm is able to solve more instances with a 2 seconds
timeout per instance than breadth-first search does with a 5 minutes timeout
per instance.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2019 14:17:47 GMT"
}
]
| 1,549,497,600,000 | [
[
"Edelmann",
"Romain",
""
],
[
"Kunčak",
"Viktor",
""
]
]
|
1902.02279 | Mauricio Gonzalez-Soto | M. Gonzalez-Soto, L.E. Sucar, H.J. Escalante | A Guiding Principle for Causal Decision Problems | Submitted to AAAI Spring Symposium Beyond Curve Fitting | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We define a Causal Decision Problem as a Decision Problem where the available
actions, the family of uncertain events and the set of outcomes are related
through the variables of a Causal Graphical Model $\mathcal{G}$. A solution
criteria based on Pearl's Do-Calculus and the Expected Utility criteria for
rational preferences is proposed. The implementation of this criteria leads to
an on-line decision making procedure that has been shown to have similar
performance to classic Reinforcement Learning algorithms while allowing for a
causal model of an environment to be learned. Thus, we aim to provide the
theoretical guarantees of the usefulness and optimality of a decision making
procedure based on causal information.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2019 17:15:28 GMT"
}
]
| 1,549,497,600,000 | [
[
"Gonzalez-Soto",
"M.",
""
],
[
"Sucar",
"L. E.",
""
],
[
"Escalante",
"H. J.",
""
]
]
|
1902.02518 | Matthew Stephenson | Matthew Stephenson, Jochen Renz | Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment
in Angry Birds | AAAI-19 Workshop on Games and Simulations for Artificial Intelligence | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an adaptive level generation algorithm for the
physics-based puzzle game Angry Birds. The proposed algorithm is based on a
pre-existing level generator for this game, but where the difficulty of the
generated levels can be adjusted based on the player's performance. This allows
for the creation of personalised levels tailored specifically to the player's
own abilities. The effectiveness of our proposed method is evaluated using
several agents with differing strategies and AI techniques. By using these
agents as models / representations of real human player's characteristics, we
can optimise level properties efficiently over a large number of generations.
As a secondary investigation, we also demonstrate that by combining the
performance of several agents together it is possible to generate levels that
are especially challenging for certain players but not others.
| [
{
"version": "v1",
"created": "Thu, 7 Feb 2019 08:36:34 GMT"
}
]
| 1,549,584,000,000 | [
[
"Stephenson",
"Matthew",
""
],
[
"Renz",
"Jochen",
""
]
]
|
1902.02556 | H\'el\`ene Plisnier | H\'el\`ene Plisnier, Denis Steckelmacher, Diederik M. Roijers, Ann
Now\'e | The Actor-Advisor: Policy Gradient With Off-Policy Advice | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Actor-critic algorithms learn an explicit policy (actor), and an accompanying
value function (critic). The actor performs actions in the environment, while
the critic evaluates the actor's current policy. However, despite their
stability and promising convergence properties, current actor-critic algorithms
do not outperform critic-only ones in practice. We believe that the fact that
the critic learns Q^pi, instead of the optimal Q-function Q*, prevents
state-of-the-art robust and sample-efficient off-policy learning algorithms
from being used. In this paper, we propose an elegant solution, the
Actor-Advisor architecture, in which a Policy Gradient actor learns from
unbiased Monte-Carlo returns, while being shaped (or advised) by the Softmax
policy arising from an off-policy critic. The critic can be learned
independently from the actor, using any state-of-the-art algorithm. Being
advised by a high-quality critic, the actor quickly and robustly learns the
task, while its use of the Monte-Carlo return helps overcome any bias the
critic may have. In addition to a new Actor-Critic formulation, the
Actor-Advisor, a method that allows an external advisory policy to shape a
Policy Gradient actor, can be applied to many other domains. By varying the
source of advice, we demonstrate the wide applicability of the Actor-Advisor to
three other important subfields of RL: safe RL with backup policies, efficient
leverage of domain knowledge, and transfer learning in RL. Our experimental
results demonstrate the benefits of the Actor-Advisor compared to
state-of-the-art actor-critic methods, illustrate its applicability to the
three other application scenarios listed above, and show that many important
challenges of RL can now be solved using a single elegant solution.
| [
{
"version": "v1",
"created": "Thu, 7 Feb 2019 10:30:40 GMT"
}
]
| 1,549,584,000,000 | [
[
"Plisnier",
"Hélène",
""
],
[
"Steckelmacher",
"Denis",
""
],
[
"Roijers",
"Diederik M.",
""
],
[
"Nowé",
"Ann",
""
]
]
|
1902.03092 | Lin Xie | Lin Xie, Nils Thieme, Ruslan Krenzler, Hanyi Li | Efficient order picking methods in robotic mobile fulfillment systems | null | European Journal of Operational Research 2021 | 10.1016/j.ejor.2020.05.032 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robotic mobile fulfillment systems (RMFSs) are a new type of warehousing
system, which has received more attention recently, due to increasing growth in
the e-commerce sector. Instead of sending pickers to the inventory area to
search for and pick the ordered items, robots carry shelves (called "pods")
including ordered items from the inventory area to picking stations. In the
picking stations, human pickers put ordered items into totes; then these items
are transported by a conveyor to the packing stations. This type of warehousing
system relieves the human pickers and improves the picking process. In this
paper, we concentrate on decisions about the assignment of pods to stations and
orders to stations to fulfill picking for each incoming customer's order. In
previous research for an RMFS with multiple picking stations, these decisions
are made sequentially. Instead, we present a new integrated model. To improve
the system performance even more, we extend our model by splitting orders. This
means parts of an order are allowed to be picked at different stations. To the
best of the authors' knowledge, this is the first publication on split orders
in an RMFS. We analyze different performance metrics, such as pile-on,
pod-station visits, robot moving distance and order turn-over time. We compare
the results of our models in different instances with the sequential method in
our open-source simulation framework RAWSim-O.
| [
{
"version": "v1",
"created": "Thu, 31 Jan 2019 22:26:56 GMT"
}
]
| 1,611,792,000,000 | [
[
"Xie",
"Lin",
""
],
[
"Thieme",
"Nils",
""
],
[
"Krenzler",
"Ruslan",
""
],
[
"Li",
"Hanyi",
""
]
]
|
1902.03142 | Ethan C Jackson | Ethan C. Jackson and Mark Daley | Novelty Search for Deep Reinforcement Learning Policy Network Weights by
Action Sequence Edit Metric Distance | Submitted to GECCO 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning (RL) problems often feature deceptive local optima,
and learning methods that optimize purely for reward signal often fail to learn
strategies for overcoming them. Deep neuroevolution and novelty search have
been proposed as effective alternatives to gradient-based methods for learning
RL policies directly from pixels. In this paper, we introduce and evaluate the
use of novelty search over agent action sequences by string edit metric
distance as a means for promoting innovation. We also introduce a method for
stagnation detection and population resampling inspired by recent developments
in the RL community that uses the same mechanisms as novelty search to promote
and develop innovative policies. Our methods extend a state-of-the-art method
for deep neuroevolution using a simple-yet-effective genetic algorithm (GA)
designed to efficiently learn deep RL policy network weights. Experiments using
four games from the Atari 2600 benchmark were conducted. Results provide
further evidence that GAs are competitive with gradient-based algorithms for
deep RL. Results also demonstrate that novelty search over action sequences is
an effective source of selection pressure that can be integrated into existing
evolutionary algorithms for deep RL.
| [
{
"version": "v1",
"created": "Fri, 8 Feb 2019 15:14:09 GMT"
}
]
| 1,549,843,200,000 | [
[
"Jackson",
"Ethan C.",
""
],
[
"Daley",
"Mark",
""
]
]
|
1902.03155 | Timo Nolle | Timo Nolle and Stefan Luettgen and Alexander Seeliger and Max
M\"uhlh\"auser | BINet: Multi-perspective Business Process Anomaly Classification | null | null | 10.1016/j.is.2019.101458 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce BINet, a neural network architecture for
real-time multi-perspective anomaly detection in business process event logs.
BINet is designed to handle both the control flow and the data perspective of a
business process. Additionally, we propose a set of heuristics for setting the
threshold of an anomaly detection algorithm automatically. We demonstrate that
BINet can be used to detect anomalies in event logs not only on a case level
but also on event attribute level. Finally, we demonstrate that a simple set of
rules can be used to utilize the output of BINet for anomaly classification. We
compare BINet to eight other state-of-the-art anomaly detection algorithms and
evaluate their performance on an elaborate data corpus of 29 synthetic and 15
real-life event logs. BINet outperforms all other methods both on the synthetic
as well as on the real-life datasets.
| [
{
"version": "v1",
"created": "Fri, 8 Feb 2019 15:48:29 GMT"
}
]
| 1,572,912,000,000 | [
[
"Nolle",
"Timo",
""
],
[
"Luettgen",
"Stefan",
""
],
[
"Seeliger",
"Alexander",
""
],
[
"Mühlhäuser",
"Max",
""
]
]
|
1902.03930 | Michael Saint-Guillain | Michael Saint-Guillain, Christine Solnon, Yves Deville | Progressive Focus Search for the Static and Stochastic VRPTW with both
Random Customers and Reveal Times | arXiv admin note: substantial text overlap with arXiv:1708.03151 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Static stochastic VRPs aim at modeling real-life VRPs by considering
uncertainty on data. In particular, the SS-VRPTW-CR considers stochastic
customers with time windows and does not make any assumption on their reveal
times, which are stochastic as well. Based on customer request probabilities,
we look for an a priori solution composed preventive vehicle routes, minimizing
the expected number of unsatisfied customer requests at the end of the day. A
route describes a sequence of strategic vehicle relocations, from which nearby
requests can be rapidly reached. Instead of reoptimizing online, a so-called
recourse strategy defines the way the requests are handled, whenever they
appear. In this paper, we describe a new recourse strategy for the SS-VRPTW-CR,
improving vehicle routes by skipping useless parts. We show how to compute the
expected cost of a priori solutions, in pseudo-polynomial time, for this
recourse strategy. We introduce a new meta-heuristic, called Progressive Focus
Search (PFS), which may be combined with any local-search based algorithm for
solving static stochastic optimization problems. PFS accelerates the search by
using approximation factors: from an initial rough simplified problem, the
search progressively focuses to the actual problem description. We evaluate our
contributions on a new, real-world based, public benchmark.
| [
{
"version": "v1",
"created": "Fri, 8 Feb 2019 12:48:32 GMT"
}
]
| 1,549,929,600,000 | [
[
"Saint-Guillain",
"Michael",
""
],
[
"Solnon",
"Christine",
""
],
[
"Deville",
"Yves",
""
]
]
|
1902.04237 | Alexis Kirke | Alexis Kirke | Applying Quantum Hardware to non-Scientific Problems: Grover's Algorithm
and Rule-based Algorithmic Music Composition | Accepted by 'International Journal of Unconventional Computing' 18
July 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Of all novel computing methods, quantum computation (QC) is currently the
most likely to move from the realm of the unconventional into the conventional.
As a result some initial work has been done on applications of QC outside of
science: for example music. The small amount of arts research done in hardware
or with actual physical systems has not utilized any of the advantages of
quantum computation (QC): the main advantage being the potential speed increase
of quantum algorithms. This paper introduces a way of utilizing Grover's
algorithm - which has been shown to provide a quadratic speed-up over its
classical equivalent - in algorithmic rule-based music composition. The system
introduced - qgMuse - is simple but scalable. Example melodies are composed
using qgMuse using the ibmqx4 quantum hardware. The paper concludes with
discussion on how such an approach can grow with the improvement of quantum
computer hardware and software.
| [
{
"version": "v1",
"created": "Sat, 2 Feb 2019 13:19:05 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Jul 2019 14:23:34 GMT"
},
{
"version": "v3",
"created": "Mon, 29 Jul 2019 10:08:40 GMT"
}
]
| 1,564,444,800,000 | [
[
"Kirke",
"Alexis",
""
]
]
|
1902.04245 | Tommaso Dreossi | Tommaso Dreossi, Daniel J. Fremont, Shromona Ghosh, Edward Kim, Hadi
Ravanbakhsh, Marcell Vazquez-Chanlatte, and Sanjit A. Seshia | VERIFAI: A Toolkit for the Design and Analysis of Artificial
Intelligence-Based Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present VERIFAI, a software toolkit for the formal design and analysis of
systems that include artificial intelligence (AI) and machine learning (ML)
components. VERIFAI particularly seeks to address challenges with applying
formal methods to perception and ML components, including those based on neural
networks, and to model and analyze system behavior in the presence of
environment uncertainty. We describe the initial version of VERIFAI which
centers on simulation guided by formal models and specifications. Several use
cases are illustrated with examples, including temporal-logic falsification,
model-based systematic fuzz testing, parameter synthesis, counterexample
analysis, and data set augmentation.
| [
{
"version": "v1",
"created": "Tue, 12 Feb 2019 05:38:14 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Feb 2019 17:30:49 GMT"
}
]
| 1,550,188,800,000 | [
[
"Dreossi",
"Tommaso",
""
],
[
"Fremont",
"Daniel J.",
""
],
[
"Ghosh",
"Shromona",
""
],
[
"Kim",
"Edward",
""
],
[
"Ravanbakhsh",
"Hadi",
""
],
[
"Vazquez-Chanlatte",
"Marcell",
""
],
[
"Seshia",
"Sanjit A.",
""
]
]
|
1902.04259 | Matthew Hausknecht | Matthew Hausknecht, Ricky Loynd, Greg Yang, Adith Swaminathan, Jason
D. Williams | NAIL: A General Interactive Fiction Agent | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interactive Fiction (IF) games are complex textual decision making problems.
This paper introduces NAIL, an autonomous agent for general parser-based IF
games. NAIL won the 2018 Text Adventure AI Competition, where it was evaluated
on twenty unseen games. This paper describes the architecture, development, and
insights underpinning NAIL's performance.
| [
{
"version": "v1",
"created": "Tue, 12 Feb 2019 06:58:31 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Feb 2019 19:45:43 GMT"
}
]
| 1,550,448,000,000 | [
[
"Hausknecht",
"Matthew",
""
],
[
"Loynd",
"Ricky",
""
],
[
"Yang",
"Greg",
""
],
[
"Swaminathan",
"Adith",
""
],
[
"Williams",
"Jason D.",
""
]
]
|
1902.04832 | Tshilidzi Marwala | Tshilidzi Marwala | Relative rationality: Is machine rationality subjective? | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rational decision making in its linguistic description means making logical
decisions. In essence, a rational agent optimally processes all relevant
information to achieve its goal. Rationality has two elements and these are the
use of relevant information and the efficient processing of such information.
In reality, relevant information is incomplete, imperfect and the processing
engine, which is a brain for humans, is suboptimal. Humans are risk averse
rather than utility maximizers. In the real world, problems are predominantly
non-convex and this makes the idea of rational decision-making fundamentally
unachievable and Herbert Simon called this bounded rationality. There is a
trade-off between the amount of information used for decision-making and the
complexity of the decision model used. This explores whether machine
rationality is subjective and concludes that indeed it is.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2019 10:08:12 GMT"
}
]
| 1,550,102,400,000 | [
[
"Marwala",
"Tshilidzi",
""
]
]
|
1902.05284 | Xin Tong Mr. | Xin Tong, Weiming Liu and Bin Li | Learn a Prior for RHEA for Better Online Planning | 8 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rolling Horizon Evolutionary Algorithms (RHEA) are a class of online planning
methods for real-time game playing; their performance is closely related to the
planning horizon and the search time allowed. In this paper, we propose to
learn a prior for RHEA in an offline manner by training a value network and a
policy network. The value network is used to reduce the planning horizon by
providing an estimation of future rewards, and the policy network is used to
initialize the population, which helps to narrow down the search scope. The
proposed algorithm, named prior-based RHEA (p-RHEA), trains policy and value
networks by performing planning and learning iteratively. In the planning
stage, the horizon-limited search assisted with the policy network and value
network is performed to improve the policies and collect training samples. In
the learning stage, the policy network and value network are trained with the
collected samples to learn better prior knowledge. Experimental results on
OpenAI Gym MuJoCo tasks show that the performance of the proposed p-RHEA is
significantly improved compared to that of RHEA.
| [
{
"version": "v1",
"created": "Thu, 14 Feb 2019 09:56:00 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Feb 2019 12:25:06 GMT"
}
]
| 1,551,052,800,000 | [
[
"Tong",
"Xin",
""
],
[
"Liu",
"Weiming",
""
],
[
"Li",
"Bin",
""
]
]
|
1902.05632 | Nathan Fulton | Nathan Fulton and Andre Platzer | Verifiably Safe Off-Model Reinforcement Learning | TACAS 2019 | null | 10.1007/978-3-030-17462-0_28 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The desire to use reinforcement learning in safety-critical settings has
inspired a recent interest in formal methods for learning algorithms. Existing
formal methods for learning and optimization primarily consider the problem of
constrained learning or constrained optimization. Given a single correct model
and associated safety constraint, these approaches guarantee efficient learning
while provably avoiding behaviors outside the safety constraint. Acting well
given an accurate environmental model is an important pre-requisite for safe
learning, but is ultimately insufficient for systems that operate in complex
heterogeneous environments. This paper introduces verification-preserving model
updates, the first approach toward obtaining formal safety guarantees for
reinforcement learning in settings where multiple environmental models must be
taken into account. Through a combination of design-time model updates and
runtime model falsification, we provide a first approach toward obtaining
formal safety proofs for autonomous systems acting in heterogeneous
environments.
| [
{
"version": "v1",
"created": "Thu, 14 Feb 2019 22:36:54 GMT"
}
]
| 1,559,692,800,000 | [
[
"Fulton",
"Nathan",
""
],
[
"Platzer",
"Andre",
""
]
]
|
1902.05644 | Macheng Shen | Macheng Shen and Jonathan P How | Active Perception in Adversarial Scenarios using Maximum Entropy Deep
Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We pose an active perception problem where an autonomous agent actively
interacts with a second agent with potentially adversarial behaviors. Given the
uncertainty in the intent of the other agent, the objective is to collect
further evidence to help discriminate potential threats. The main technical
challenges are the partial observability of the agent intent, the adversary
modeling, and the corresponding uncertainty modeling. Note that an adversary
agent may act to mislead the autonomous agent by using a deceptive strategy
that is learned from past experiences. We propose an approach that combines
belief space planning, generative adversary modeling, and maximum entropy
reinforcement learning to obtain a stochastic belief space policy. By
accounting for various adversarial behaviors in the simulation framework and
minimizing the predictability of the autonomous agent's action, the resulting
policy is more robust to unmodeled adversarial strategies. This improved
robustness is empirically shown against an adversary that adapts to and
exploits the autonomous agent's policy when compared with a standard
Chance-Constraint Partially Observable Markov Decision Process robust approach.
| [
{
"version": "v1",
"created": "Thu, 14 Feb 2019 23:44:22 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Sep 2019 23:38:54 GMT"
}
]
| 1,568,937,600,000 | [
[
"Shen",
"Macheng",
""
],
[
"How",
"Jonathan P",
""
]
]
|
1902.05677 | Paul Cohen | Paul Cohen | Probabilistic Relational Agent-based Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | PRAM puts agent-based models on a sound probabilistic footing as a basis for
integrating agent-based and probabilistic models. It extends the themes of
probabilistic relational models and lifted inference to incorporate dynamical
models and simulation. It can also be much more efficient than agent-based
simulation.
| [
{
"version": "v1",
"created": "Fri, 15 Feb 2019 04:03:30 GMT"
}
]
| 1,550,448,000,000 | [
[
"Cohen",
"Paul",
""
]
]
|
1902.06370 | Chen Wang | Chen Wang, Hui Ma, Gang Chen and Sven Hartmann | Evolutionary Multitasking for Semantic Web Service Composition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Web services are basic functions of a software system to support the concept
of service-oriented architecture. They are often composed together to provide
added values, known as web service composition. Researchers often employ
Evolutionary Computation techniques to efficiently construct composite services
with near-optimized functional quality (i.e., Quality of Semantic Matchmaking)
or non-functional quality (i.e., Quality of Service) or both due to the
complexity of this problem. With a significant increase in service composition
requests, many composition requests have similar input and output requirements
but may vary due to different preferences from different user segments. This
problem is often treated as a multi-objective service composition so as to cope
with different preferences from different user segments simultaneously. Without
taking a multi-objective approach that gives rise to a solution selection
challenge, we perceive multiple similar service composition requests as jointly
forming an evolutionary multi-tasking problem in this work. We propose an
effective permutation-based evolutionary multi-tasking approach that can
simultaneously generate a set of solutions, with one for each service request.
We also introduce a neighborhood structure over multiple tasks to allow newly
evolved solutions to be evaluated on related tasks. Our proposed method can
perform better at the cost of only a fraction of time, compared to one
state-of-art single-tasking EC-based method. We also found that the use of the
proper neighborhood structure can enhance the effectiveness of our approach.
| [
{
"version": "v1",
"created": "Mon, 18 Feb 2019 01:22:02 GMT"
}
]
| 1,550,534,400,000 | [
[
"Wang",
"Chen",
""
],
[
"Ma",
"Hui",
""
],
[
"Chen",
"Gang",
""
],
[
"Hartmann",
"Sven",
""
]
]
|
1902.06824 | Syed Arbab Mohd Shihab | Syed Arbab Mohd Shihab, Caleb Logemann, Deepak-George Thomas and Peng
Wei | Autonomous Airline Revenue Management: A Deep Reinforcement Learning
Approach to Seat Inventory Control and Overbooking | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Revenue management can enable airline corporations to maximize the revenue
generated from each scheduled flight departing in their transportation network
by means of finding the optimal policies for differential pricing, seat
inventory control and overbooking. As different demand segments in the market
have different Willingness-To-Pay (WTP), airlines use differential pricing,
booking restrictions, and service amenities to determine different fare classes
or products targeted at each of these demand segments. Because seats are
limited for each flight, airlines also need to allocate seats for each of these
fare classes to prevent lower fare class passengers from displacing higher fare
class ones and set overbooking limits in anticipation of cancellations and
no-shows such that revenue is maximized. Previous work addresses these problems
using optimization techniques or classical Reinforcement Learning methods. This
paper focuses on the latter problem - the seat inventory control problem -
casting it as a Markov Decision Process to be able to find the optimal policy.
Multiple fare classes, concurrent continuous arrival of passengers of different
fare classes, overbooking and random cancellations that are independent of
class have been considered in the model. We have addressed this problem using
Deep Q-Learning with the goal of maximizing the reward for each flight
departure. The implementation of this technique allows us to employ large
continuous state space but also presents the potential opportunity to test on
real time airline data. To generate data and train the agent, a basic
air-travel market simulator was developed. The performance of the agent in
different simulated market scenarios was compared against theoretically optimal
solutions and was found to be nearly close to the expected optimal revenue.
| [
{
"version": "v1",
"created": "Mon, 18 Feb 2019 22:31:09 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Jun 2019 19:14:27 GMT"
}
]
| 1,560,729,600,000 | [
[
"Shihab",
"Syed Arbab Mohd",
""
],
[
"Logemann",
"Caleb",
""
],
[
"Thomas",
"Deepak-George",
""
],
[
"Wei",
"Peng",
""
]
]
|
1902.07151 | Siqi Liu | Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess,
Thore Graepel | Emergent Coordination Through Competition | null | ICLR (2019) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the emergence of cooperative behaviors in reinforcement learning
agents by introducing a challenging competitive multi-agent soccer environment
with continuous simulated physics. We demonstrate that decentralized,
population-based training with co-play can lead to a progression in agents'
behaviors: from random, to simple ball chasing, and finally showing evidence of
cooperation. Our study highlights several of the challenges encountered in
large scale multi-agent training in continuous control. In particular, we
demonstrate that the automatic optimization of simple shaping rewards, not
themselves conducive to co-operative behavior, can lead to long-horizon team
behavior. We further apply an evaluation scheme, grounded by game theoretic
principals, that can assess agent performance in the absence of pre-defined
evaluation tasks or human baselines.
| [
{
"version": "v1",
"created": "Tue, 19 Feb 2019 17:18:14 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Feb 2019 14:20:32 GMT"
}
]
| 1,621,555,200,000 | [
[
"Liu",
"Siqi",
""
],
[
"Lever",
"Guy",
""
],
[
"Merel",
"Josh",
""
],
[
"Tunyasuvunakool",
"Saran",
""
],
[
"Heess",
"Nicolas",
""
],
[
"Graepel",
"Thore",
""
]
]
|
1902.07526 | Kristijonas \v{C}yras | Kristijonas \v{C}yras, Tiago Oliveira | Resolving Conflicts in Clinical Guidelines using Argumentation | Paper accepted for publication at AAAMAS 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatically reasoning with conflicting generic clinical guidelines is a
burning issue in patient-centric medical reasoning where patient-specific
conditions and goals need to be taken into account. It is even more challenging
in the presence of preferences such as patient's wishes and clinician's
priorities over goals. We advance a structured argumentation formalism for
reasoning with conflicting clinical guidelines, patient-specific information
and preferences. Our formalism integrates assumption-based reasoning and
goal-driven selection among reasoning outcomes. Specifically, we assume
applicability of guideline recommendations concerning the generic goal of
patient well-being, resolve conflicts among recommendations using patient's
conditions and preferences, and then consider prioritised patient-centered
goals to yield non-conflicting, goal-maximising and preference-respecting
recommendations. We rely on the state-of-the-art Transition-based Medical
Recommendation model for representing guideline recommendations and augment it
with context given by the patient's conditions, goals, as well as preferences
over recommendations and goals. We establish desirable properties of our
approach in terms of sensitivity to recommendation conflicts and patient
context.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2019 11:55:02 GMT"
}
]
| 1,550,707,200,000 | [
[
"Čyras",
"Kristijonas",
""
],
[
"Oliveira",
"Tiago",
""
]
]
|
1902.09244 | Viktoria Hauder | Viktoria A. Hauder, Andreas Beham, Sebastian Raggl, Sophie N. Parragh,
Michael Affenzeller | Resource-constrained multi-project scheduling with activity and time
flexibility | null | Computers & Industrial Engineering, 106857 (2020) | 10.1016/j.cie.2020.106857 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Project scheduling in manufacturing environments often requires flexibility
in terms of the selection and the exact length of alternative production
activities. Moreover, the simultaneous scheduling of multiple lots is mandatory
in many production planning applications. To meet these requirements, a new
resource-constrained project scheduling problem (RCPSP) is introduced where
both decisions (activity flexibility and time flexibility) are integrated.
Besides the minimization of makespan, two new alternative objectives are
presented: maximization of balanced length of selected activities (time
balance) and maximization of balanced resource utilization (resource balance).
New mixed integer and constraint programming (CP) models are proposed for the
developed integrated flexible project scheduling problem. Benchmark instances
on an already existing flexible RCPSP and the newly developed problem are
solved to optimality. The real-world applicability of the suggested CP models
is shown by additionally solving a large industry case.
| [
{
"version": "v1",
"created": "Mon, 25 Feb 2019 13:08:53 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Oct 2020 07:54:09 GMT"
}
]
| 1,603,670,400,000 | [
[
"Hauder",
"Viktoria A.",
""
],
[
"Beham",
"Andreas",
""
],
[
"Raggl",
"Sebastian",
""
],
[
"Parragh",
"Sophie N.",
""
],
[
"Affenzeller",
"Michael",
""
]
]
|
1902.09291 | Guilherme Wachs-Lopes | Mariana B. Santos, Amanda M. Lima, Lucas A. Silva, Felipe S. Vargas,
Guilherme A. Wachs-Lopes, Paulo S. Rodrigues | MIRA: A Computational Neuro-Based Cognitive Architecture Applied to
Movie Recommender Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human mind is still an unknown process of neuroscience in many aspects.
Nevertheless, for decades the scientific community has proposed computational
models that try to simulate their parts, specific applications, or their
behavior in different situations. The most complete model in this line is
undoubtedly the LIDA model, proposed by Stan Franklin with the aim of serving
as a generic computational architecture for several applications. The present
project is inspired by the LIDA model to apply it to the process of movie
recommendation, the model called MIRA (Movie Intelligent Recommender Agent)
presented percentages of precision similar to a traditional model when
submitted to the same assay conditions. Moreover, the proposed model reinforced
the precision indexes when submitted to tests with volunteers, proving once
again its performance as a cognitive model, when executed with small data
volumes. Considering that the proposed model achieved a similar behavior to the
traditional models under conditions expected to be similar for natural systems,
it can be said that MIRA reinforces the applicability of LIDA as a path to be
followed for the study and generation of computational agents inspired by
neural behaviors.
| [
{
"version": "v1",
"created": "Mon, 25 Feb 2019 14:32:18 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Feb 2019 11:21:29 GMT"
}
]
| 1,551,312,000,000 | [
[
"Santos",
"Mariana B.",
""
],
[
"Lima",
"Amanda M.",
""
],
[
"Silva",
"Lucas A.",
""
],
[
"Vargas",
"Felipe S.",
""
],
[
"Wachs-Lopes",
"Guilherme A.",
""
],
[
"Rodrigues",
"Paulo S.",
""
]
]
|
1902.09335 | Sabrina Evans | Sabrina Evans, Paolo Turrini | Similarity Measures based on Local Game Trees | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study strategic similarity of game positions in two-player extensive games
of perfect information, by looking at the structure of their local game trees,
with the aim of improving the performance of game playing agents in detecting
forcing continuations. We present a range of measures over the induced game
trees and compare them against benchmark problems in chess, observing a
promising level of accuracy in matching up trap states.
| [
{
"version": "v1",
"created": "Mon, 25 Feb 2019 15:06:26 GMT"
}
]
| 1,551,139,200,000 | [
[
"Evans",
"Sabrina",
""
],
[
"Turrini",
"Paolo",
""
]
]
|
1902.09355 | Andrea Censi | Andrea Censi, Konstantin Slutsky, Tichakorn Wongpiromsarn, Dmitry
Yershov, Scott Pendleton, James Fu, Emilio Frazzoli | Liability, Ethics, and Culture-Aware Behavior Specification using
Rulebooks | To appear in ICRA 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The behavior of self-driving cars must be compatible with an enormous set of
conflicting and ambiguous objectives, from law, from ethics, from the local
culture, and so on. This paper describes a new way to conveniently define the
desired behavior for autonomous agents, which we use on the self-driving cars
developed at nuTonomy. We define a "rulebook" as a pre-ordered set of "rules",
each akin to a violation metric on the possible outcomes ("realizations"). The
rules are partially ordered by priority. The semantics of a rulebook imposes a
pre-order on the set of realizations. We study the compositional properties of
the rulebooks, and we derive which operations we can allow on the rulebooks to
preserve previously-introduced constraints. While we demonstrate the
application of these techniques in the self-driving domain, the methods are
domain-independent.
| [
{
"version": "v1",
"created": "Mon, 25 Feb 2019 15:17:15 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Mar 2019 07:09:30 GMT"
}
]
| 1,551,657,600,000 | [
[
"Censi",
"Andrea",
""
],
[
"Slutsky",
"Konstantin",
""
],
[
"Wongpiromsarn",
"Tichakorn",
""
],
[
"Yershov",
"Dmitry",
""
],
[
"Pendleton",
"Scott",
""
],
[
"Fu",
"James",
""
],
[
"Frazzoli",
"Emilio",
""
]
]
|
1902.09469 | Scott Garrabrant | Abram Demski and Scott Garrabrant | Embedded Agency | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional models of rational action treat the agent as though it is cleanly
separated from its environment, and can act on that environment from the
outside. Such agents have a known functional relationship with their
environment, can model their environment in every detail, and do not need to
reason about themselves or their internal parts.
We provide an informal survey of obstacles to formalizing good reasoning for
agents embedded in their environment. Such agents must optimize an environment
that is not of type "function"; they must rely on models that fit within the
modeled environment; and they must reason about themselves as just another
physical system, made of parts that can be modified and that can work at cross
purposes.
| [
{
"version": "v1",
"created": "Mon, 25 Feb 2019 17:38:48 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Aug 2020 18:36:39 GMT"
},
{
"version": "v3",
"created": "Tue, 6 Oct 2020 21:20:37 GMT"
}
]
| 1,602,115,200,000 | [
[
"Demski",
"Abram",
""
],
[
"Garrabrant",
"Scott",
""
]
]
|
1902.09706 | Wenjian Luo | Yamin Hu, Wenjian Luo, Junteng Wang | Community-based 3-SAT Formulas with a Predefined Solution | 23 pages; due to the limitation "The abstract field cannot be longer
than 1,920 characters", the abstract appearing here is slightly shorter than
that in the PDF file | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is crucial to generate crafted SAT formulas with predefined solutions for
the testing and development of SAT solvers since many SAT formulas from
real-world applications have solutions. Although some generating algorithms
have been proposed to generate SAT formulas with predefined solutions,
community structures of SAT formulas are not considered. We propose a 3-SAT
formula generating algorithm that not only guarantees the existence of a
predefined solution, but also simultaneously considers community structures and
clause distributions. The proposed 3-SAT formula generating algorithm controls
the quality of community structures through controlling (1) the number of
clauses whose variables have a common community, which we call intra-community
clauses, and (2) the number of variables that only belong to one community,
which we call intra-community variables. To study the combined effect of
community structures and clause distributions on the hardness of SAT formulas,
we measure solving runtimes of two solvers, gluHack (a leading CDCL solver) and
CPSparrow (a leading SLS solver), on the generated SAT formulas under different
groups of parameter settings. Through extensive experiments, we obtain some
noteworthy observations on the SAT formulas generated by the proposed
algorithm: (1) The community structure has little or no effects on the hardness
of SAT formulas with regard to CPSparrow but a strong effect with regard to
gluHack. (2) Only when the proportion of true literals in a SAT formula in
terms of the predefined solution is 0.5, SAT formulas are hard-to-solve with
regard to gluHack; when this proportion is below 0.5, SAT formulas are
hard-to-solve with regard to CPSparrow. (3) When the ratio of the number of
clauses to that of variables is around 4.25, the SAT formulas are hard-to-solve
with regard to both gluHack and CPSparrow.
| [
{
"version": "v1",
"created": "Tue, 26 Feb 2019 02:16:30 GMT"
}
]
| 1,551,225,600,000 | [
[
"Hu",
"Yamin",
""
],
[
"Luo",
"Wenjian",
""
],
[
"Wang",
"Junteng",
""
]
]
|
1902.09725 | Alexander Turner | Alexander Matt Turner, Dylan Hadfield-Menell, Prasad Tadepalli | Conservative Agency via Attainable Utility Preservation | Published in AI, Ethics, and Society 2020 | null | 10.1145/3375627.3375851 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reward functions are easy to misspecify; although designers can make
corrections after observing mistakes, an agent pursuing a misspecified reward
function can irreversibly change the state of its environment. If that change
precludes optimization of the correctly specified reward function, then
correction is futile. For example, a robotic factory assistant could break
expensive equipment due to a reward misspecification; even if the designers
immediately correct the reward function, the damage is done. To mitigate this
risk, we introduce an approach that balances optimization of the primary reward
function with preservation of the ability to optimize auxiliary reward
functions. Surprisingly, even when the auxiliary reward functions are randomly
generated and therefore uninformative about the correctly specified reward
function, this approach induces conservative, effective behavior.
| [
{
"version": "v1",
"created": "Tue, 26 Feb 2019 04:42:54 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Jul 2019 15:31:07 GMT"
},
{
"version": "v3",
"created": "Wed, 10 Jun 2020 15:10:04 GMT"
}
]
| 1,591,833,600,000 | [
[
"Turner",
"Alexander Matt",
""
],
[
"Hadfield-Menell",
"Dylan",
""
],
[
"Tadepalli",
"Prasad",
""
]
]
|
1902.10499 | Maxat Kulmanov | Maxat Kulmanov, Wang Liu-Wei, Yuan Yan and Robert Hoehndorf | EL Embeddings: Geometric construction of models for the Description
Logic EL ++ | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | An embedding is a function that maps entities from one algebraic structure
into another while preserving certain characteristics. Embeddings are being
used successfully for mapping relational data or text into vector spaces where
they can be used for machine learning, similarity search, or similar tasks. We
address the problem of finding vector space embeddings for theories in the
Description Logic $\mathcal{EL}^{++}$ that are also models of the TBox. To find
such embeddings, we define an optimization problem that characterizes the
model-theoretic semantics of the operators in $\mathcal{EL}^{++}$ within
$\Re^n$, thereby solving the problem of finding an interpretation function for
an $\mathcal{EL}^{++}$ theory given a particular domain $\Delta$. Our approach
is mainly relevant to large $\mathcal{EL}^{++}$ theories and knowledge bases
such as the ontologies and knowledge graphs used in the life sciences. We
demonstrate that our method can be used for improved prediction of
protein--protein interactions when compared to semantic similarity measures or
knowledge graph embedding
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2019 13:04:44 GMT"
}
]
| 1,551,312,000,000 | [
[
"Kulmanov",
"Maxat",
""
],
[
"Liu-Wei",
"Wang",
""
],
[
"Yan",
"Yuan",
""
],
[
"Hoehndorf",
"Robert",
""
]
]
|
1902.10552 | Marcos Cramer | Marcos Cramer, Mathieu Guillaume | Technical report of "Empirical Study on Human Evaluation of Complex
Argumentation Frameworks" | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In abstract argumentation, multiple argumentation semantics have been
proposed that allow to select sets of jointly acceptable arguments from a given
argumentation framework, i.e. based only on the attack relation between
arguments. The existence of multiple argumentation semantics raises the
question which of these semantics predicts best how humans evaluate arguments.
Previous empirical cognitive studies that have tested how humans evaluate sets
of arguments depending on the attack relation between them have been limited to
a small set of very simple argumentation frameworks, so that some semantics
studied in the literature could not be meaningfully distinguished by these
studies. In this paper we report on an empirical cognitive study that overcomes
these limitations by taking into consideration twelve argumentation frameworks
of three to eight arguments each. These argumentation frameworks were mostly
more complex than the argumentation frameworks considered in previous studies.
All twelve argumentation framework were systematically instantiated with
natural language arguments based on a certain fictional scenario, and
participants were shown both the natural language arguments and a graphical
depiction of the attack relation between them. Our data shows that grounded and
CF2 semantics were the best predictors of human argument evaluation. A detailed
analysis revealed that part of the participants chose a cognitively simpler
strategy that is predicted very well by grounded semantics, while another part
of the participants chose a cognitively more demanding strategy that is mostly
predicted well by CF2 semantics.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2019 14:29:34 GMT"
}
]
| 1,551,312,000,000 | [
[
"Cramer",
"Marcos",
""
],
[
"Guillaume",
"Mathieu",
""
]
]
|
1902.10619 | Craig Innes | Craig Innes, Alex Lascarides | Learning Factored Markov Decision Processes with Unawareness | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Methods for learning and planning in sequential decision problems often
assume the learner is aware of all possible states and actions in advance. This
assumption is sometimes untenable. In this paper, we give a method to learn
factored markov decision problems from both domain exploration and expert
assistance, which guarantees convergence to near-optimal behaviour, even when
the agent begins unaware of factors critical to success. Our experiments show
our agent learns optimal behaviour on small and large problems, and that
conserving information on discovering new possibilities results in faster
convergence.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2019 16:21:13 GMT"
}
]
| 1,551,312,000,000 | [
[
"Innes",
"Craig",
""
],
[
"Lascarides",
"Alex",
""
]
]
|
1902.10646 | Adish Singla | Rishav Chourasia, Adish Singla | Unifying Ensemble Methods for Q-learning via Social Choice Theory | Learning with Rich Experience (LIRE) Workshop, NeurIPS 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ensemble methods have been widely applied in Reinforcement Learning (RL) in
order to enhance stability, increase convergence speed, and improve
exploration. These methods typically work by employing an aggregation mechanism
over actions of different RL algorithms. We show that a variety of these
methods can be unified by drawing parallels from committee voting rules in
Social Choice Theory. We map the problem of designing an action aggregation
mechanism in an ensemble method to a voting problem which, under different
voting rules, yield popular ensemble-based RL algorithms like Majority Voting
Q-learning or Bootstrapped Q-learning. Our unification framework, in turn,
allows us to design new ensemble-RL algorithms with better performance. For
instance, we map two diversity-centered committee voting rules, namely Single
Non-Transferable Voting Rule and Chamberlin-Courant Rule, into new RL
algorithms that demonstrate excellent exploratory behavior in our experiments.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2019 17:27:30 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Oct 2019 09:14:26 GMT"
}
]
| 1,570,579,200,000 | [
[
"Chourasia",
"Rishav",
""
],
[
"Singla",
"Adish",
""
]
]
|
1902.10770 | Vahid Mokhtari | Vahid Mokhtari, Luis Seabra Lopes, Armando Pinho and Roman Manevich | Learning Task Knowledge and its Scope of Applicability in
Experience-Based Planning Domains | 25 pages, 6 figures, 6 tables, 1 algorithm, 6 listings | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experience-based planning domains (EBPDs) have been recently proposed to
improve problem solving by learning from experience. EBPDs provide important
concepts for long-term learning and planning in robotics. They rely on
acquiring and using task knowledge, i.e., activity schemata, for generating
concrete solutions to problem instances in a class of tasks. Using Three-Valued
Logic Analysis (TVLA), we extend previous work to generate a set of conditions
as the scope of applicability for an activity schema. The inferred scope is a
bounded representation of a set of problems of potentially unbounded size, in
the form of a 3-valued logical structure, which allows an EBPD system to
automatically find an applicable activity schema for solving task problems. We
demonstrate the utility of our approach in a set of classes of problems in a
simulated domain and a class of real world tasks in a fully physically
simulated PR2 robot in Gazebo.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2019 20:32:29 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Mar 2019 11:28:10 GMT"
}
]
| 1,551,830,400,000 | [
[
"Mokhtari",
"Vahid",
""
],
[
"Lopes",
"Luis Seabra",
""
],
[
"Pinho",
"Armando",
""
],
[
"Manevich",
"Roman",
""
]
]
|
1902.10870 | Takayuki Osogami Ph.D. | Takayuki Osogami, Toshihiro Takahashi | Real-time tree search with pessimistic scenarios | 14 pages, 3 figures, Published as IBM Research Report RT0982 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autonomous agents need to make decisions in a sequential manner, under
partially observable environment, and in consideration of how other agents
behave. In critical situations, such decisions need to be made in real time for
example to avoid collisions and recover to safe conditions. We propose a
technique of tree search where a deterministic and pessimistic scenario is used
after a specified depth. Because there is no branching with the deterministic
scenario, the proposed technique allows us to take into account the events that
can occur far ahead in the future. The effectiveness of the proposed technique
is demonstrated in Pommerman, a multi-agent environment used in a NeurIPS 2018
competition, where the agents that implement the proposed technique have won
the first and third places.
| [
{
"version": "v1",
"created": "Thu, 28 Feb 2019 02:47:05 GMT"
},
{
"version": "v2",
"created": "Sun, 14 Jul 2019 12:28:45 GMT"
}
]
| 1,563,235,200,000 | [
[
"Osogami",
"Takayuki",
""
],
[
"Takahashi",
"Toshihiro",
""
]
]
|
1903.00336 | Jasper De Bock | Jasper De Bock and Gert de Cooman | Interpreting, axiomatising and representing coherent choice functions in
terms of desirability | arXiv admin note: text overlap with arXiv:1806.01044 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Choice functions constitute a simple, direct and very general mathematical
framework for modelling choice under uncertainty. In particular, they are able
to represent the set-valued choices that appear in imprecise-probabilistic
decision making. We provide these choice functions with a clear interpretation
in terms of desirability, use this interpretation to derive a set of basic
coherence axioms, and show that this notion of coherence leads to a
representation in terms of sets of strict preference orders. By imposing
additional properties such as totality, the mixing property and Archimedeanity,
we obtain representation in terms of sets of strict total orders, lexicographic
probability systems, coherent lower previsions or linear previsions.
| [
{
"version": "v1",
"created": "Thu, 28 Feb 2019 13:27:07 GMT"
},
{
"version": "v2",
"created": "Mon, 20 May 2019 10:51:23 GMT"
}
]
| 1,558,483,200,000 | [
[
"De Bock",
"Jasper",
""
],
[
"de Cooman",
"Gert",
""
]
]
|
1903.00606 | Yuu Jinnai | Yuu Jinnai, Jee Won Park, David Abel, George Konidaris | Discovering Options for Exploration by Minimizing Cover Time | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the main challenges in reinforcement learning is solving tasks with
sparse reward. We show that the difficulty of discovering a distant rewarding
state in an MDP is bounded by the expected cover time of a random walk over the
graph induced by the MDP's transition dynamics. We therefore propose to
accelerate exploration by constructing options that minimize cover time. The
proposed algorithm finds an option which provably diminishes the expected
number of steps to visit every state in the state space by a uniform random
walk. We show empirically that the proposed algorithm improves the learning
time in several domains with sparse rewards.
| [
{
"version": "v1",
"created": "Sat, 2 Mar 2019 02:17:52 GMT"
},
{
"version": "v2",
"created": "Sat, 16 Mar 2019 18:26:07 GMT"
}
]
| 1,552,953,600,000 | [
[
"Jinnai",
"Yuu",
""
],
[
"Park",
"Jee Won",
""
],
[
"Abel",
"David",
""
],
[
"Konidaris",
"George",
""
]
]
|
1903.00900 | Jiang Rong | Jiang Rong, Tao Qin and Bo An | Competitive Bridge Bidding with Deep Neural Networks | This paper was submitted to AAMAS on Nov. 12, 2018, accepted on Jan.
23, 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The game of bridge consists of two stages: bidding and playing. While playing
is proved to be relatively easy for computer programs, bidding is very
challenging. During the bidding stage, each player knowing only his/her own
cards needs to exchange information with his/her partner and interfere with
opponents at the same time. Existing methods for solving perfect-information
games cannot be directly applied to bidding. Most bridge programs are based on
human-designed rules, which, however, cannot cover all situations and are
usually ambiguous and even conflicting with each other. In this paper, we, for
the first time, propose a competitive bidding system based on deep learning
techniques, which exhibits two novelties. First, we design a compact
representation to encode the private and public information available to a
player for bidding. Second, based on the analysis of the impact of other
players' unknown cards on one's final rewards, we design two neural networks to
deal with imperfect information, the first one inferring the cards of the
partner and the second one taking the outputs of the first one as part of its
input to select a bid. Experimental results show that our bidding system
outperforms the top rule-based program.
| [
{
"version": "v1",
"created": "Sun, 3 Mar 2019 13:17:21 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Mar 2019 17:55:08 GMT"
}
]
| 1,551,830,400,000 | [
[
"Rong",
"Jiang",
""
],
[
"Qin",
"Tao",
""
],
[
"An",
"Bo",
""
]
]
|
1903.01153 | Diego Aineto | Diego Aineto, Sergio Jim\'enez and Eva Onaindia | Learning STRIPS Action Models with Classical Planning | 8+1 pages, 4 figures, 6 tables | Twenty-Eighth International Conference on Automated Planning and
Scheduling (ICAPS 2018), pp. 399-407, Year 2018 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel approach for learning STRIPS action models from
examples that compiles this inductive learning task into a classical planning
task. Interestingly, the compilation approach is flexible to different amounts
of available input knowledge; the learning examples can range from a set of
plans (with their corresponding initial and final states) to just a pair of
initial and final states (no intermediate action or state is given). Moreover,
the compilation accepts partially specified action models and it can be used to
validate whether the observation of a plan execution follows a given STRIPS
action model, even if this model is not fully specified.
| [
{
"version": "v1",
"created": "Mon, 4 Mar 2019 09:55:33 GMT"
}
]
| 1,551,744,000,000 | [
[
"Aineto",
"Diego",
""
],
[
"Jiménez",
"Sergio",
""
],
[
"Onaindia",
"Eva",
""
]
]
|
1903.01710 | Elodie Chanthery | Elodie Chanthery (LAAS, LAAS-DISCO), Louise Trav\'e-Massuy\`es
(LAAS-DISCO), Yannick Pencol\'e (LAAS-DISCO), R\'egis De Ferluc, Brice
Dellandrea | Applying Active Diagnosis to Space Systems by On-Board Control
Procedures | IEEE Transactions on Aerospace and Electronic Systems, Institute of
Electrical and Electronics Engineers, In press | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The instrumentation of real systems is often designed for control purposes
and control inputs are designed to achieve nominal control objectives. Hence,
the available measurements may not be sufficient to isolate faults with
certainty and diagnoses are ambiguous. Active diagnosis formulates a planning
problem to generate a sequence of actions that, applied to the system, enforce
diagnosability and allow to iteratively refine ambiguous diagnoses. This paper
analyses the requirements for applying active diagnosis to space systems and
proposes ActHyDiag as an effective framework to solve this problem. It presents
the results of applying ActHyDiag to a real space case study and of
implementing the generated plans in the form of On-Board Control Procedures.
The case study is a redundant Spacewire Network where up to 6 instruments,
monitored and controlled by the on-board software hosted in the Satellite
Management Unit, are transferring science data to a mass memory unit through
Spacewire routers. Experiments have been conducted on a real physical benchmark
developed by Thales Alenia Space and demonstrate the effectiveness of the plans
proposed by ActHyDiag.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2019 07:44:35 GMT"
}
]
| 1,551,830,400,000 | [
[
"Chanthery",
"Elodie",
"",
"LAAS, LAAS-DISCO"
],
[
"Travé-Massuyès",
"Louise",
"",
"LAAS-DISCO"
],
[
"Pencolé",
"Yannick",
"",
"LAAS-DISCO"
],
[
"De Ferluc",
"Régis",
""
],
[
"Dellandrea",
"Brice",
""
]
]
|
1903.01865 | Maximiliano Celmo David Budan | Maximiliano C. D. Bud\'an, Gerardo I. Simari, Ignacio Viglizzo and
Guillermo R. Simari | An Approach to Characterize Graded Entailment of Arguments through a
Label-based Framework | null | Internation Journal of Approximate Reasoning - 2017 | 10.1016/j.ijar.2016.12.016 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Argumentation theory is a powerful paradigm that formalizes a type of
commonsense reasoning that aims to simulate the human ability to resolve a
specific problem in an intelligent manner. A classical argumentation process
takes into account only the properties related to the intrinsic logical
soundness of an argument in order to determine its acceptability status.
However, these properties are not always the only ones that matter to establish
the argument's acceptability---there exist other qualities, such as strength,
weight, social votes, trust degree, relevance level, and certainty degree,
among others.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2019 14:48:14 GMT"
}
]
| 1,551,830,400,000 | [
[
"Budán",
"Maximiliano C. D.",
""
],
[
"Simari",
"Gerardo I.",
""
],
[
"Viglizzo",
"Ignacio",
""
],
[
"Simari",
"Guillermo R.",
""
]
]
|
1903.01874 | Maximiliano Celmo David Budan | Maximiliano C. D. Bud\'an, Maria Laura Cobo, Diego C. Martinez and
Guillermo R. Simari | Bipolar in Temporal Argumentation Framework | null | Internation Journal of Approximate Reassoning - 2017 | 10.1016/j.ijar.2017.01.013 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A Timed Argumentation Framework (TAF) is a formalism where arguments are only
valid for consideration in a given period of time, called availability
intervals, which are defined for every individual argument. The original
proposal is based on a single, abstract notion of attack between arguments that
remains static and permanent in time. Thus, in general, when identifying the
set of acceptable arguments, the outcome associated with a TAF will vary over
time. In this work we introduce an extension of TAF adding the capability of
modeling a support relation between arguments. In this sense, the resulting
framework provides a suitable model for different time-dependent issues. Thus,
the main contribution here is to provide an enhanced framework for modeling a
positive (support) and negative (attack) interaction varying over time, which
are relevant in many real-world situations. This leads to a Timed Bipolar
Argumentation Framework (T-BAF), where classical argument extensions can be
defined. The proposal aims at advancing in the integration of temporal
argumentation in different application domain.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2019 14:57:23 GMT"
}
]
| 1,551,830,400,000 | [
[
"Budán",
"Maximiliano C. D.",
""
],
[
"Cobo",
"Maria Laura",
""
],
[
"Martinez",
"Diego C.",
""
],
[
"Simari",
"Guillermo R.",
""
]
]
|
1903.01920 | Guillermo Simari | Edgardo Ferretti, Luciano H. Tamargo, Alejandro J. Garcia, Marcelo L.
Errecalde, and Guillermo R. Simari | An approach to Decision Making based on Dynamic Argumentation Systems | null | Artif. Intell. 242: 107-131 (2017) | 10.1016/j.artint.2016.10.004 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce a formalism for single-agent decision making that
is based on Dynamic Argumentation Frameworks. The formalism can be used to
justify a choice, which is based on the current situation the agent is
involved. Taking advantage of the inference mechanism of the argumentation
formalism, it is possible to consider preference relations and conflicts among
the available alternatives for that reasoning. With this formalization, given a
particular set of evidence, the justified conclusions supported by warranted
arguments will be used by the agent's decision rules to determine which
alternatives will be selected. We also present an algorithm that implements a
choice function based on our formalization. Finally, we complete our
presentation by introducing formal results that relate the proposed framework
with approaches of classical decision theory.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2019 16:14:16 GMT"
}
]
| 1,551,830,400,000 | [
[
"Ferretti",
"Edgardo",
""
],
[
"Tamargo",
"Luciano H.",
""
],
[
"Garcia",
"Alejandro J.",
""
],
[
"Errecalde",
"Marcelo L.",
""
],
[
"Simari",
"Guillermo R.",
""
]
]
|
1903.01966 | Maximiliano Celmo David Budan | Maximiliano C. D. Bud\'an, Mar\'ia Laura Cobo, Diego I. Mart\'inez and
Antonino Rotolo | Dealing with Qualitative and Quantitative Features in Legal Domains | arXiv admin note: text overlap with arXiv:1903.01865 | International Conference on Legal Knowledge and Information
Systems - 2018 | 10.3233/978-1-61499-935-5-176 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we enrich a formalism for argumentation by including a formal
characterization of features related to the knowledge, in order to capture
proper reasoning in legal domains. We add meta-data information to the
arguments in the form of labels representing quantitative and qualitative data
about them. These labels are propagated through an argumentative graph
according to the relations of support, conflict, and aggregation between
arguments.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2019 18:18:41 GMT"
}
]
| 1,551,830,400,000 | [
[
"Budán",
"Maximiliano C. D.",
""
],
[
"Cobo",
"María Laura",
""
],
[
"Martínez",
"Diego I.",
""
],
[
"Rotolo",
"Antonino",
""
]
]
|
1903.02710 | Emilio Parisotto | Emilio Parisotto and Soham Ghosh and Sai Bhargav Yalamanchi and Varsha
Chinnaobireddy and Yuhuai Wu and Ruslan Salakhutdinov | Concurrent Meta Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | State-of-the-art meta reinforcement learning algorithms typically assume the
setting of a single agent interacting with its environment in a sequential
manner. A negative side-effect of this sequential execution paradigm is that,
as the environment becomes more and more challenging, and thus requiring more
interaction episodes for the meta-learner, it needs the agent to reason over
longer and longer time-scales. To combat the difficulty of long time-scale
credit assignment, we propose an alternative parallel framework, which we name
"Concurrent Meta-Reinforcement Learning" (CMRL), that transforms the temporal
credit assignment problem into a multi-agent reinforcement learning one. In
this multi-agent setting, a set of parallel agents are executed in the same
environment and each of these "rollout" agents are given the means to
communicate with each other. The goal of the communication is to coordinate, in
a collaborative manner, the most efficient exploration of the shared task the
agents are currently assigned. This coordination therefore represents the
meta-learning aspect of the framework, as each agent can be assigned or assign
itself a particular section of the current task's state space. This framework
is in contrast to standard RL methods that assume that each parallel rollout
occurs independently, which can potentially waste computation if many of the
rollouts end up sampling the same part of the state space. Furthermore, the
parallel setting enables us to define several reward sharing functions and
auxiliary losses that are non-trivial to apply in the sequential setting. We
demonstrate the effectiveness of our proposed CMRL at improving over sequential
methods in a variety of challenging tasks.
| [
{
"version": "v1",
"created": "Thu, 7 Mar 2019 03:28:41 GMT"
}
]
| 1,552,003,200,000 | [
[
"Parisotto",
"Emilio",
""
],
[
"Ghosh",
"Soham",
""
],
[
"Yalamanchi",
"Sai Bhargav",
""
],
[
"Chinnaobireddy",
"Varsha",
""
],
[
"Wu",
"Yuhuai",
""
],
[
"Salakhutdinov",
"Ruslan",
""
]
]
|
1903.02716 | Rongqi Li | Yujie Chen, Yu Qian, Yichen Yao, Zili Wu, Rongqi Li, Yinzhi Zhou,
Haoyuan Hu, Yinghui Xu | Can Sophisticated Dispatching Strategy Acquired by Reinforcement
Learning? - A Case Study in Dynamic Courier Dispatching System | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study a courier dispatching problem (CDP) raised from an
online pickup-service platform of Alibaba. The CDP aims to assign a set of
couriers to serve pickup requests with stochastic spatial and temporal arrival
rate among urban regions. The objective is to maximize the revenue of served
requests given a limited number of couriers over a period of time. Many online
algorithms such as dynamic matching and vehicle routing strategy from existing
literature could be applied to tackle this problem. However, these methods rely
on appropriately predefined optimization objectives at each decision point,
which is hard in dynamic situations. This paper formulates the CDP as a Markov
decision process (MDP) and proposes a data-driven approach to derive the
optimal dispatching rule-set under different scenarios. Our method stacks
multi-layer images of the spatial-and-temporal map and apply multi-agent
reinforcement learning (MARL) techniques to evolve dispatching models. This
method solves the learning inefficiency caused by traditional centralized MDP
modeling. Through comprehensive experiments on both artificial dataset and
real-world dataset, we show: 1) By utilizing historical data and considering
long-term revenue gains, MARL achieves better performance than myopic online
algorithms; 2) MARL is able to construct the mapping between complex scenarios
to sophisticated decisions such as the dispatching rule. 3) MARL has the
scalability to adopt in large-scale real-world scenarios.
| [
{
"version": "v1",
"created": "Thu, 7 Mar 2019 03:49:07 GMT"
}
]
| 1,552,003,200,000 | [
[
"Chen",
"Yujie",
""
],
[
"Qian",
"Yu",
""
],
[
"Yao",
"Yichen",
""
],
[
"Wu",
"Zili",
""
],
[
"Li",
"Rongqi",
""
],
[
"Zhou",
"Yinzhi",
""
],
[
"Hu",
"Haoyuan",
""
],
[
"Xu",
"Yinghui",
""
]
]
|
1903.03078 | Manolis Pitsikalis | Manolis Pitsikalis, Alexander Artikis, Richard Dreo, Cyril Ray, Elena
Camossi and Anne-Laure Jousselme | Composite Event Recognition for Maritime Monitoring | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Maritime monitoring systems support safe shipping as they allow for the
real-time detection of dangerous, suspicious and illegal vessel activities. We
present such a system using the Run-Time Event Calculus, a composite event
recognition system with formal, declarative semantics. For effective
recognition, we developed a library of maritime patterns in close collaboration
with domain experts. We present a thorough evaluation of the system and the
patterns both in terms of predictive accuracy and computational efficiency,
using real-world datasets of vessel position streams and contextual
geographical information.
| [
{
"version": "v1",
"created": "Thu, 7 Mar 2019 18:10:00 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Mar 2019 13:47:52 GMT"
},
{
"version": "v3",
"created": "Mon, 13 May 2019 12:17:36 GMT"
}
]
| 1,557,792,000,000 | [
[
"Pitsikalis",
"Manolis",
""
],
[
"Artikis",
"Alexander",
""
],
[
"Dreo",
"Richard",
""
],
[
"Ray",
"Cyril",
""
],
[
"Camossi",
"Elena",
""
],
[
"Jousselme",
"Anne-Laure",
""
]
]
|
1903.03099 | Ondrej Kuzelka | Ondrej Kuzelka and Vyacheslav Kungurtsev | Lifted Weight Learning of Markov Logic Networks Revisited | Appearing in the proceedings of the 22nd International Conference on
Artificial Intelligence and Statistics (AISTATS) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study lifted weight learning of Markov logic networks. We show that there
is an algorithm for maximum-likelihood learning of 2-variable Markov logic
networks which runs in time polynomial in the domain size. Our results are
based on existing lifted-inference algorithms and recent algorithmic results on
computing maximum entropy distributions.
| [
{
"version": "v1",
"created": "Thu, 7 Mar 2019 18:50:10 GMT"
}
]
| 1,552,003,200,000 | [
[
"Kuzelka",
"Ondrej",
""
],
[
"Kungurtsev",
"Vyacheslav",
""
]
]
|
1903.03205 | Guangming Lang | Guangming Lang | Three-Way Decisions-Based Conflict Analysis Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Three-way decision theory, which trisects the universe with less risks or
costs, is considered as a powerful mathematical tool for handling uncertainty
in incomplete and imprecise information tables, and provides an effective tool
for conflict analysis decision making in real-time situations. In this paper,
we propose the concepts of the agreement, disagreement and neutral subsets of a
strategy with two evaluation functions, which establish the three-way
decisions-based conflict analysis models(TWDCAMs) for trisecting the universe
of agents, and employ a pair of two-way decisions models to interpret the
mechanism of the three-way decision rules for an agent. Subsequently, we
develop the concepts of the agreement, disagreement and neutral strategies of
an agent group with two evaluation functions, which build the TWDCAMs for
trisecting the universe of issues, and take a couple of two-way decisions
models to explain the mechanism of the three-way decision rules for an issue.
Finally, we reconstruct Fan, Qi and Wei's conflict analysis models(FQWCAMs) and
Sun, Ma and Zhao's conflict analysis models(SMZCAMs) with two evaluation
functions, and interpret FQWCAMs and SMZCAMs with a pair of two-day decisions
models, which illustrates that FQWCAMs and SMZCAMs are special cases of
TWDCAMs.
| [
{
"version": "v1",
"created": "Thu, 7 Mar 2019 22:17:14 GMT"
}
]
| 1,552,262,400,000 | [
[
"Lang",
"Guangming",
""
]
]
|
1903.03294 | Sanjiang Li | Sanjiang Li and Xueqing Yan | Let's Play Mahjong! | 20 pages, 1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mahjong is a very popular tile-based game commonly played by four players.
Each player begins with a hand of 13 tiles and, in turn, players draw and
discard (i.e., change) tiles until they complete a legal hand using a 14th
tile. In this paper, we initiate a mathematical and AI study of the Mahjong
game and try to answer two fundamental questions: how bad is a hand of 14
tiles? and which tile should I discard? We define and characterise the notion
of deficiency and present an optimal policy to discard a tile in order to
increase the chance of completing a legal hand within $k$ tile changes for each
$k\geq 1$.
| [
{
"version": "v1",
"created": "Fri, 8 Mar 2019 05:43:21 GMT"
}
]
| 1,552,262,400,000 | [
[
"Li",
"Sanjiang",
""
],
[
"Yan",
"Xueqing",
""
]
]
|
1903.03408 | Marc Maliar | Marc Maliar | How Machine (Deep) Learning Helps Us Understand Human Learning: the
Value of Big Ideas | 17 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | I use simulation of two multilayer neural networks to gain intuition into the
determinants of human learning. The first network, the teacher, is trained to
achieve a high accuracy in handwritten digit recognition. The second network,
the student, learns to reproduce the output of the first network. I show that
learning from the teacher is more effective than learning from the data under
the appropriate degree of regularization. Regularization allows the teacher to
distinguish the trends and to deliver "big ideas" to the student. I also model
other learning situations such as expert and novice teachers, high- and
low-ability students and biased learning experience due to, e.g., poverty and
trauma. The results from computer simulation accord remarkably well with
finding of the modern psychological literature. The code is written in MATLAB
and will be publicly available from the author's web page.
| [
{
"version": "v1",
"created": "Sat, 16 Feb 2019 16:06:42 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Mar 2019 20:55:49 GMT"
}
]
| 1,553,212,800,000 | [
[
"Maliar",
"Marc",
""
]
]
|
1903.03424 | Michael Heller | Michael Heller | The Homunculus Brain and Categorical Logic | 21 pages, one diagram, no figures | Philosophical Problems in Science 69, 2020, 253-280 | 10.1007/978-3-030-40245-7_13 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The interaction between syntax (formal language) and its semantics (meanings
of language) is one which has been well studied in categorical logic. The
results of this particular study are employed to understand how the brain is
able to create meanings. To emphasize the toy character of the proposed model,
we prefer to speak of the homunculus brain rather than the brain per se. The
homunculus brain consists of neurons, each of which is modeled by a category,
and axons between neurons, which are modeled by functors between the
corresponding neuron-categories. Each neuron (category) has its own program
enabling its working, i.e. a theory of this neuron. In analogy to what is known
from categorical logic, we postulate the existence of a pair of adjoint
functors, called Lang and Syn, from a category, now called BRAIN, of
categories, to a category, now called MIND, of theories. Our homunculus is a
kind of ``mathematical robot'', the neuronal architecture of which is not
important. Its only aim is to provide us with the opportunity to study how such
a simple brain-like structure could ``create meanings'' and perform abstraction
operations out of its purely syntactic program. The pair of adjoint functors
Lang and Syn model the mutual dependencies between the syntactical structure of
a given theory of MIND and the internal logic of its semantics given by a
category of BRAIN. In this way, a formal language (syntax) and its meanings
(semantics) are interwoven with each other in a manner corresponding to the
adjointness of the functors Lang and Syn. Higher cognitive functions of
abstraction and realization of concepts are also modelled by a corresponding
pair of adjoint functors. The categories BRAIN and MIND interact with each
other with their entire structures and, at the same time, these very structures
are shaped by this interaction.
| [
{
"version": "v1",
"created": "Thu, 28 Feb 2019 18:42:00 GMT"
},
{
"version": "v2",
"created": "Fri, 24 Jan 2020 11:33:07 GMT"
}
]
| 1,620,172,800,000 | [
[
"Heller",
"Michael",
""
]
]
|
1903.03495 | Mohamed Akrout | Mohamed Akrout, Amir-massoud Farahmand, Tory Jarmain, Latif Abid | Improving Skin Condition Classification with a Visual Symptom Checker
Trained using Reinforcement Learning | Accepted for the Conference on Medical Image Computing and Computer
Assisted Intervention (MICCAI) 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a visual symptom checker that combines a pre-trained Convolutional
Neural Network (CNN) with a Reinforcement Learning (RL) agent as a Question
Answering (QA) model. This method increases the classification confidence and
accuracy of the visual symptom checker, and decreases the average number of
questions asked to narrow down the differential diagnosis. A Deep Q-Network
(DQN)-based RL agent learns how to ask the patient about the presence of
symptoms in order to maximize the probability of correctly identifying the
underlying condition. The RL agent uses the visual information provided by CNN
in addition to the answers to the asked questions to guide the QA system. We
demonstrate that the RL-based approach increases the accuracy more than 20%
compared to the CNN-only approach, which only uses the visual information to
predict the condition. Moreover, the increased accuracy is up to 10% compared
to the approach that uses the visual information provided by CNN along with a
conventional decision tree-based QA system. We finally show that the RL-based
approach not only outperforms the decision tree-based approach, but also
narrows down the diagnosis faster in terms of the average number of asked
questions.
| [
{
"version": "v1",
"created": "Fri, 8 Mar 2019 15:24:31 GMT"
},
{
"version": "v2",
"created": "Sat, 30 Mar 2019 16:09:27 GMT"
},
{
"version": "v3",
"created": "Fri, 26 Jul 2019 22:45:22 GMT"
},
{
"version": "v4",
"created": "Wed, 7 Aug 2019 23:32:01 GMT"
}
]
| 1,565,308,800,000 | [
[
"Akrout",
"Mohamed",
""
],
[
"Farahmand",
"Amir-massoud",
""
],
[
"Jarmain",
"Tory",
""
],
[
"Abid",
"Latif",
""
]
]
|
1903.03515 | Naveen Sundar Govindarajulu | Selmer Bringsjord and Naveen Sundar Govindarajulu | Learning $\textit{Ex Nihilo}$ | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces, philosophically and to a degree formally, the novel
concept of learning $\textit{ex nihilo}$, intended (obviously) to be analogous
to the concept of creation $\textit{ex nihilo}$. Learning $\textit{ex nihilo}$
is an agent's learning "from nothing," by the suitable employment of schemata
for deductive and inductive reasoning. This reasoning must be in
machine-verifiable accord with a formal proof/argument theory in a
$\textit{cognitive calculus}$ (i.e., roughly, an intensional higher-order
multi-operator quantified logic), and this reasoning is applied to percepts
received by the agent, in the context of both some prior knowledge, and some
prior and current interests. Learning $\textit{ex nihilo}$ is a challenge to
contemporary forms of ML, indeed a severe one, but the challenge is offered in
the spirt of seeking to stimulate attempts, on the part of non-logicist ML
researchers and engineers, to collaborate with those in possession of
learning-$\textit{ex nihilo}$ frameworks, and eventually attempts to integrate
directly with such frameworks at the implementation level. Such integration
will require, among other things, the symbiotic interoperation of
state-of-the-art automated reasoners and high-expressivity planners, with
statistical/connectionist ML technology.
| [
{
"version": "v1",
"created": "Mon, 4 Mar 2019 05:06:09 GMT"
},
{
"version": "v2",
"created": "Sun, 21 Apr 2019 06:30:47 GMT"
}
]
| 1,555,977,600,000 | [
[
"Bringsjord",
"Selmer",
""
],
[
"Govindarajulu",
"Naveen Sundar",
""
]
]
|
1903.03592 | Guillaume Escamocher | Guillaume Escamocher, Barry O'Sullivan, Steven David Prestwich | Generating Difficult SAT Instances by Preventing Triangles | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When creating benchmarks for SAT solvers, we need SAT instances that are easy
to build but hard to solve. A recent development in the search for such methods
has led to the Balanced SAT algorithm, which can create k-SAT instances with m
clauses of high difficulty, for arbitrary k and m. In this paper we introduce
the No-Triangle SAT algorithm, a SAT instance generator based on the cluster
coefficient graph statistic. We empirically compare the two algorithms by
fixing the arity and the number of variables, but varying the number of
clauses. The hardest instances that we find are produced by No-Triangle SAT.
Furthermore, difficult instances from No-Triangle SAT have a different number
of clauses than difficult instances from Balanced SAT, potentially allowing a
combination of the two methods to find hard SAT instances for a larger array of
parameters.
| [
{
"version": "v1",
"created": "Fri, 8 Mar 2019 18:21:46 GMT"
}
]
| 1,552,262,400,000 | [
[
"Escamocher",
"Guillaume",
""
],
[
"O'Sullivan",
"Barry",
""
],
[
"Prestwich",
"Steven David",
""
]
]
|
1903.03804 | Dingwu Tan | Mingming Lu, Dingwu Tan, Naixue Xiong, Zailiang Chen and Haifeng Li | Program Classification Using Gated Graph Attention Neural Network for
Online Programming Service | 12 pages, 27 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The online programing services, such as Github,TopCoder, and EduCoder, have
promoted a lot of social interactions among the service users. However, the
existing social interactions is rather limited and inefficient due to the rapid
increasing of source-code repositories, which is difficult to explore manually.
The emergence of source-code mining provides a promising way to analyze those
source codes, so that those source codes can be relatively easy to understand
and share among those service users. Among all the source-code mining
attempts,program classification lays a foundation for various tasks related to
source-code understanding, because it is impossible for a machine to understand
a computer program if it cannot classify the program correctly. Although
numerous machine learning models, such as the Natural Language Processing (NLP)
based models and the Abstract Syntax Tree (AST) based models, have been
proposed to classify computer programs based on their corresponding source
codes, the existing works cannot fully characterize the source codes from the
perspective of both the syntax and semantic information. To address this
problem, we proposed a Graph Neural Network (GNN) based model, which integrates
data flow and function call information to the AST,and applies an improved GNN
model to the integrated graph, so as to achieve the state-of-art program
classification accuracy. The experiment results have shown that the proposed
work can classify programs with accuracy over 97%.
| [
{
"version": "v1",
"created": "Sat, 9 Mar 2019 13:47:05 GMT"
}
]
| 1,552,348,800,000 | [
[
"Lu",
"Mingming",
""
],
[
"Tan",
"Dingwu",
""
],
[
"Xiong",
"Naixue",
""
],
[
"Chen",
"Zailiang",
""
],
[
"Li",
"Haifeng",
""
]
]
|
1903.03877 | Smitha Milli | Smitha Milli, Anca D. Dragan | Literal or Pedagogic Human? Analyzing Human Model Misspecification in
Objective Learning | Published at UAI 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is incredibly easy for a system designer to misspecify the objective for
an autonomous system ("robot''), thus motivating the desire to have the robot
learn the objective from human behavior instead. Recent work has suggested that
people have an interest in the robot performing well, and will thus behave
pedagogically, choosing actions that are informative to the robot. In turn,
robots benefit from interpreting the behavior by accounting for this pedagogy.
In this work, we focus on misspecification: we argue that robots might not know
whether people are being pedagogic or literal and that it is important to ask
which assumption is safer to make. We cast objective learning into the more
general form of a common-payoff game between the robot and human, and prove
that in any such game literal interpretation is more robust to
misspecification. Experiments with human data support our theoretical results
and point to the sensitivity of the pedagogic assumption.
| [
{
"version": "v1",
"created": "Sat, 9 Mar 2019 21:58:46 GMT"
},
{
"version": "v2",
"created": "Sat, 29 Jun 2019 03:26:48 GMT"
}
]
| 1,562,025,600,000 | [
[
"Milli",
"Smitha",
""
],
[
"Dragan",
"Anca D.",
""
]
]
|
1903.03993 | Massimiliano de Leoni | Massimiliano de Leoni, Safa Dundar | From Low-Level Events to Activities -- A Session-Based Approach
(Extended Version) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Process-Mining techniques aim to use event data about past executions to gain
insight into how processes are executed. While these techniques are proven to
be very valuable, they are less successful to reach their goal if the process
is flexible and, hence, events can potentially occur in any order. Furthermore,
information systems can record events at very low level, which do not match the
high-level concepts known at business level. Without abstracting sequences of
events to high-level concepts, the results of applying process mining (e.g.,
discovered models) easily become very complex and difficult to interpret, which
ultimately means that they are of little use. A large body of research exists
on event abstraction but typically a large amount of domain knowledge is
required to be fed in, which is often not readily available. Other abstraction
techniques are unsupervised, which give lower accuracy. This paper puts forward
a technique that requires limited domain knowledge that can be easily provided.
Traces are divided in sessions, and each session is abstracted as one single
high-level activity execution. The abstraction is based on a combination of
automatic clustering and visualization methods. The technique was assessed on
two case studies that evidently exhibits a large amount of behavior. The
results clearly illustrate the benefits of the abstraction to convey knowledge
to stakeholders.
| [
{
"version": "v1",
"created": "Sun, 10 Mar 2019 14:01:49 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Mar 2019 15:45:24 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Jun 2019 12:39:36 GMT"
}
]
| 1,559,606,400,000 | [
[
"de Leoni",
"Massimiliano",
""
],
[
"Dundar",
"Safa",
""
]
]
|
1903.04051 | Hongkai Wen | Man Luo, Hongkai Wen, Yi Luo, Bowen Du, Konstantin Klemmer, Hongming
Zhu | Demand Prediction for Electric Vehicle Sharing | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electric Vehicle (EV) sharing systems have recently experienced unprecedented
growth across the globe. Many car sharing service providers as well as
automobile manufacturers are entering this competition by expanding both their
EV fleets and renting/returning station networks, aiming to seize a share of
the market and bring car sharing to the zero emissions level. During their fast
expansion, one fundamental determinant for success is the capability of
dynamically predicting the demand of stations. In this paper we propose a novel
demand prediction approach, which is able to model the dynamics of the system
and predict demand accordingly. We use a local temporal encoding process to
handle the available historical data at individual stations, and a spatial
encoding process to take correlations between stations into account with graph
convolutional neural networks. The encoded features are fed to a prediction
network, which forecasts both the long-term expected demand of the stations. We
evaluate the proposed approach on real-world data collected from a major EV
sharing platform. Experimental results demonstrate that our approach
significantly outperforms the state of the art.
| [
{
"version": "v1",
"created": "Sun, 10 Mar 2019 20:03:43 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Apr 2019 17:05:50 GMT"
},
{
"version": "v3",
"created": "Fri, 10 May 2019 21:12:19 GMT"
}
]
| 1,557,792,000,000 | [
[
"Luo",
"Man",
""
],
[
"Wen",
"Hongkai",
""
],
[
"Luo",
"Yi",
""
],
[
"Du",
"Bowen",
""
],
[
"Klemmer",
"Konstantin",
""
],
[
"Zhu",
"Hongming",
""
]
]
|
1903.04672 | Steven Holtzen | Steven Holtzen and Todd Millstein and Guy Van den Broeck | Generating and Sampling Orbits for Lifted Probabilistic Inference | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A key goal in the design of probabilistic inference algorithms is identifying
and exploiting properties of the distribution that make inference tractable.
Lifted inference algorithms identify symmetry as a property that enables
efficient inference and seek to scale with the degree of symmetry of a
probability model. A limitation of existing exact lifted inference techniques
is that they do not apply to non-relational representations like factor graphs.
In this work we provide the first example of an exact lifted inference
algorithm for arbitrary discrete factor graphs. In addition we describe a
lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the
degree of symmetry of the distribution.
| [
{
"version": "v1",
"created": "Tue, 12 Mar 2019 00:15:46 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Mar 2019 15:46:52 GMT"
},
{
"version": "v3",
"created": "Sun, 30 Jun 2019 23:23:21 GMT"
}
]
| 1,562,025,600,000 | [
[
"Holtzen",
"Steven",
""
],
[
"Millstein",
"Todd",
""
],
[
"Broeck",
"Guy Van den",
""
]
]
|
1903.04966 | Jin-Kao Hao | Zequn Wei and Jin-Kao Hao | Iterated two-phase local search for the Set-Union Knapsack Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Set-union Knapsack Problem (SUKP) is a generalization of the popular 0-1
knapsack problem. Given a set of weighted elements and a set of items with
profits where each item is composed of a subset of elements, the SUKP involves
packing a subset of items in a capacity-constrained knapsack such that the
total profit of the selected items is maximized while their weights do not
exceed the knapsack capacity. In this work, we present an effective iterated
two-phase local search algorithm for this NP-hard combinatorial optimization
problem. The proposed algorithm iterates through two search phases: a local
optima exploration phase that alternates between a variable neighborhood
descent search and a tabu search to explore local optimal solutions, and a
local optima escaping phase to drive the search to unexplored regions. We show
the competitiveness of the algorithm compared to the state-of-the-art methods
in the literature. Specifically, the algorithm discovers 18 improved best
results (new lower bounds) for the 30 benchmark instances and matches the
best-known results for the 12 remaining instances. We also report the first
computational results with the general CPLEX solver, including 6 proven optimal
solutions. Finally, we investigate the effectiveness of the key ingredients of
the algorithm on its performance.
| [
{
"version": "v1",
"created": "Tue, 12 Mar 2019 14:48:24 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Mar 2019 09:46:30 GMT"
}
]
| 1,552,521,600,000 | [
[
"Wei",
"Zequn",
""
],
[
"Hao",
"Jin-Kao",
""
]
]
|
1903.05720 | Arjun Akula | Arjun R Akula, Sinisa Todorovic, Joyce Y Chai, Song-Chun Zhu | Natural Language Interaction with Explainable AI Models | null | CVPR 2019 Workshop on Explainable AI | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an explainable AI (XAI) system that provides explanations
for its predictions. The system consists of two key components -- namely, the
prediction And-Or graph (AOG) model for recognizing and localizing concepts of
interest in input data, and the XAI model for providing explanations to the
user about the AOG's predictions. In this work, we focus on the XAI model
specified to interact with the user in natural language, whereas the AOG's
predictions are considered given and represented by the corresponding parse
graphs (pg's) of the AOG. Our XAI model takes pg's as input and provides
answers to the user's questions using the following types of reasoning: direct
evidence (e.g., detection scores), part-based inference (e.g., detected parts
provide evidence for the concept asked), and other evidences from
spatio-temporal context (e.g., constraints from the spatio-temporal surround).
We identify several correlations between user's questions and the XAI answers
using Youtube Action dataset.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2019 21:29:13 GMT"
},
{
"version": "v2",
"created": "Sun, 7 Jul 2019 07:52:59 GMT"
}
]
| 1,562,630,400,000 | [
[
"Akula",
"Arjun R",
""
],
[
"Todorovic",
"Sinisa",
""
],
[
"Chai",
"Joyce Y",
""
],
[
"Zhu",
"Song-Chun",
""
]
]
|
1903.05937 | Luciano Serafini | Luciano Serafini, Paolo Traverso | Incremental Learning of Discrete Planning Domains from Continuous
Perceptions | Corrected lines 12 and 19 of algorithm 1: ALP | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a framework for learning discrete deterministic planning domains.
In this framework, an agent learns the domain by observing the action effects
through continuous features that describe the state of the environment after
the execution of each action. Besides, the agent learns its perception
function, i.e., a probabilistic mapping between state variables and sensor data
represented as a vector of continuous random variables called perception
variables. We define an algorithm that updates the planning domain and the
perception function by (i) introducing new states, either by extending the
possible values of state variables, or by weakening their constraints; (ii)
adapts the perception function to fit the observed data (iii) adapts the
transition function on the basis of the executed actions and the effects
observed via the perception function. The framework is able to deal with
exogenous events that happen in the environment.
| [
{
"version": "v1",
"created": "Thu, 14 Mar 2019 12:17:33 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Apr 2019 09:39:27 GMT"
}
]
| 1,555,891,200,000 | [
[
"Serafini",
"Luciano",
""
],
[
"Traverso",
"Paolo",
""
]
]
|
1903.06015 | Vahid Mokhtari | Vahid Mokhtari, Luis Seabra Lopes, Armando Pinho and Roman Manevich | Computing the Scope of Applicability for Acquired Task Knowledge in
Experience-Based Planning Domains | 8 pages, conference paper. arXiv admin note: text overlap with
arXiv:1902.10770 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experience-based planning domains have been proposed to improve problem
solving by learning from experience. They rely on acquiring and using task
knowledge, i.e., activity schemata, for generating solutions to problem
instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we
extend previous work to generate a set of conditions that determine the scope
of applicability of an activity schema. The inferred scope is a bounded
representation of a set of problems of potentially unbounded size, in the form
of a 3-valued logical structure, which is used to automatically find an
applicable activity schema for solving task problems. We validate this work in
two classical planning domains.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2019 09:05:47 GMT"
}
]
| 1,552,608,000,000 | [
[
"Mokhtari",
"Vahid",
""
],
[
"Lopes",
"Luis Seabra",
""
],
[
"Pinho",
"Armando",
""
],
[
"Manevich",
"Roman",
""
]
]
|
1903.06418 | Mehrdad Zakershahrak | Mehrdad Zakershahrak, Ze Gong, Nikhillesh Sadassivam and Yu Zhang | Online Explanation Generation for Human-Robot Teaming | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As AI becomes an integral part of our lives, the development of explainable
AI, embodied in the decision-making process of an AI or robotic agent, becomes
imperative. For a robotic teammate, the ability to generate explanations to
justify its behavior is one of the key requirements of explainable agency.
Prior work on explanation generation has been focused on supporting the
rationale behind the robot's decision or behavior. These approaches, however,
fail to consider the mental demand for understanding the received explanation.
In other words, the human teammate is expected to understand an explanation no
matter how much information is presented. In this work, we argue that
explanations, especially those of a complex nature, should be made in an online
fashion during the execution, which helps spread out the information to be
explained and thus reduce the mental workload of humans in highly cognitive
demanding tasks. However, a challenge here is that the different parts of an
explanation may be dependent on each other, which must be taken into account
when generating online explanations. To this end, a general formulation of
online explanation generation is presented with three variations satisfying
different "online" properties. The new explanation generation methods are based
on a model reconciliation setting introduced in our prior work. We evaluated
our methods both with human subjects in a simulated rover domain, using NASA
Task Load Index (TLX), and synthetically with ten different problems across two
standard IPC domains. Results strongly suggest that our methods generate
explanations that are perceived as less cognitively demanding and much
preferred over the baselines and are computationally efficient.
| [
{
"version": "v1",
"created": "Fri, 15 Mar 2019 09:09:53 GMT"
},
{
"version": "v2",
"created": "Tue, 2 Apr 2019 01:14:46 GMT"
},
{
"version": "v3",
"created": "Sun, 4 Aug 2019 00:42:18 GMT"
},
{
"version": "v4",
"created": "Tue, 6 Aug 2019 08:00:27 GMT"
},
{
"version": "v5",
"created": "Mon, 16 Sep 2019 05:31:34 GMT"
},
{
"version": "v6",
"created": "Mon, 31 Aug 2020 17:04:51 GMT"
}
]
| 1,598,918,400,000 | [
[
"Zakershahrak",
"Mehrdad",
""
],
[
"Gong",
"Ze",
""
],
[
"Sadassivam",
"Nikhillesh",
""
],
[
"Zhang",
"Yu",
""
]
]
|
1903.06445 | Desmond Ong | Desmond C. Ong, Harold Soh, Jamil Zaki, Noah D. Goodman | Applying Probabilistic Programming to Affective Computing | Accepted by IEEE Transactions on Affective Computing. 12 pages, 6
figures | null | 10.1109/TAFFC.2019.2905211 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Affective Computing is a rapidly growing field spurred by advancements in
artificial intelligence, but often, held back by the inability to translate
psychological theories of emotion into tractable computational models. To
address this, we propose a probabilistic programming approach to affective
computing, which models psychological-grounded theories as generative models of
emotion, and implements them as stochastic, executable computer programs. We
first review probabilistic approaches that integrate reasoning about emotions
with reasoning about other latent mental states (e.g., beliefs, desires) in
context. Recently-developed probabilistic programming languages offer several
key desidarata over previous approaches, such as: (i) flexibility in
representing emotions and emotional processes; (ii) modularity and
compositionality; (iii) integration with deep learning libraries that
facilitate efficient inference and learning from large, naturalistic data; and
(iv) ease of adoption. Furthermore, using a probabilistic programming framework
allows a standardized platform for theory-building and experimentation:
Competing theories (e.g., of appraisal or other emotional processes) can be
easily compared via modular substitution of code followed by model comparison.
To jumpstart adoption, we illustrate our points with executable code that
researchers can easily modify for their own models. We end with a discussion of
applications and future directions of the probabilistic programming approach.
| [
{
"version": "v1",
"created": "Fri, 15 Mar 2019 10:33:53 GMT"
}
]
| 1,596,412,800,000 | [
[
"Ong",
"Desmond C.",
""
],
[
"Soh",
"Harold",
""
],
[
"Zaki",
"Jamil",
""
],
[
"Goodman",
"Noah D.",
""
]
]
|
1903.07008 | Rodrigo Canaan | Rodrigo Canaan, Christoph Salge, Julian Togelius, Andy Nealen | Leveling the Playing Field -- Fairness in AI Versus Human Game
Benchmarks | 7 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | From the beginning if the history of AI, there has been interest in games as
a platform of research. As the field developed, human-level competence in
complex games became a target researchers worked to reach. Only relatively
recently has this target been finally met for traditional tabletop games such
as Backgammon, Chess and Go. Current research focus has shifted to electronic
games, which provide unique challenges. As is often the case with AI research,
these results are liable to be exaggerated or misrepresented by either authors
or third parties. The extent to which these games benchmark consist of fair
competition between human and AI is also a matter of debate. In this work, we
review the statements made by authors and third parties in the general media
and academic circle about these game benchmark results and discuss factors that
can impact the perception of fairness in the contest between humans and
machines
| [
{
"version": "v1",
"created": "Sun, 17 Mar 2019 00:42:26 GMT"
},
{
"version": "v2",
"created": "Sun, 24 Mar 2019 17:52:49 GMT"
},
{
"version": "v3",
"created": "Sun, 14 Apr 2019 01:20:16 GMT"
},
{
"version": "v4",
"created": "Thu, 29 Aug 2019 16:52:14 GMT"
}
]
| 1,567,123,200,000 | [
[
"Canaan",
"Rodrigo",
""
],
[
"Salge",
"Christoph",
""
],
[
"Togelius",
"Julian",
""
],
[
"Nealen",
"Andy",
""
]
]
|
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