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1610.08853 | Ahmed Alaa | Ahmed M. Alaa, Jinsung Yoon, Scott Hu, and Mihaela van der Schaar | Personalized Risk Scoring for Critical Care Prognosis using Mixtures of
Gaussian Processes | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: In this paper, we develop a personalized real-time risk scoring
algorithm that provides timely and granular assessments for the clinical acuity
of ward patients based on their (temporal) lab tests and vital signs; the
proposed risk scoring system ensures timely intensive care unit (ICU)
admissions for clinically deteriorating patients. Methods: The risk scoring
system learns a set of latent patient subtypes from the offline electronic
health record data, and trains a mixture of Gaussian Process (GP) experts,
where each expert models the physiological data streams associated with a
specific patient subtype. Transfer learning techniques are used to learn the
relationship between a patient's latent subtype and her static admission
information (e.g. age, gender, transfer status, ICD-9 codes, etc). Results:
Experiments conducted on data from a heterogeneous cohort of 6,321 patients
admitted to Ronald Reagan UCLA medical center show that our risk score
significantly and consistently outperforms the currently deployed risk scores,
such as the Rothman index, MEWS, APACHE and SOFA scores, in terms of
timeliness, true positive rate (TPR), and positive predictive value (PPV).
Conclusion: Our results reflect the importance of adopting the concepts of
personalized medicine in critical care settings; significant accuracy and
timeliness gains can be achieved by accounting for the patients' heterogeneity.
Significance: The proposed risk scoring methodology can confer huge clinical
and social benefits on more than 200,000 critically ill inpatient who exhibit
cardiac arrests in the US every year.
| [
{
"version": "v1",
"created": "Thu, 27 Oct 2016 15:54:04 GMT"
}
] | 1,477,612,800,000 | [
[
"Alaa",
"Ahmed M.",
""
],
[
"Yoon",
"Jinsung",
""
],
[
"Hu",
"Scott",
""
],
[
"van der Schaar",
"Mihaela",
""
]
] |
1610.09064 | Himabindu Lakkaraju | Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz | Identifying Unknown Unknowns in the Open World: Representations and
Policies for Guided Exploration | To appear in AAAI 2017; Presented at NIPS Workshop on Reliability in
ML, 2016 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predictive models deployed in the real world may assign incorrect labels to
instances with high confidence. Such errors or unknown unknowns are rooted in
model incompleteness, and typically arise because of the mismatch between
training data and the cases encountered at test time. As the models are blind
to such errors, input from an oracle is needed to identify these failures. In
this paper, we formulate and address the problem of informed discovery of
unknown unknowns of any given predictive model where unknown unknowns occur due
to systematic biases in the training data. We propose a model-agnostic
methodology which uses feedback from an oracle to both identify unknown
unknowns and to intelligently guide the discovery. We employ a two-phase
approach which first organizes the data into multiple partitions based on the
feature similarity of instances and the confidence scores assigned by the
predictive model, and then utilizes an explore-exploit strategy for discovering
unknown unknowns across these partitions. We demonstrate the efficacy of our
framework by varying the underlying causes of unknown unknowns across various
applications. To the best of our knowledge, this paper presents the first
algorithmic approach to the problem of discovering unknown unknowns of
predictive models.
| [
{
"version": "v1",
"created": "Fri, 28 Oct 2016 02:55:14 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2016 03:01:21 GMT"
},
{
"version": "v3",
"created": "Sat, 10 Dec 2016 06:02:38 GMT"
}
] | 1,481,587,200,000 | [
[
"Lakkaraju",
"Himabindu",
""
],
[
"Kamar",
"Ece",
""
],
[
"Caruana",
"Rich",
""
],
[
"Horvitz",
"Eric",
""
]
] |
1611.00183 | Bas Van Stein | Bas van Stein, Matthijs van Leeuwen and Thomas B\"ack | Local Subspace-Based Outlier Detection using Global Neighbourhoods | Short version accepted at IEEE BigData 2016 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Outlier detection in high-dimensional data is a challenging yet important
task, as it has applications in, e.g., fraud detection and quality control.
State-of-the-art density-based algorithms perform well because they 1) take the
local neighbourhoods of data points into account and 2) consider feature
subspaces. In highly complex and high-dimensional data, however, existing
methods are likely to overlook important outliers because they do not
explicitly take into account that the data is often a mixture distribution of
multiple components.
We therefore introduce GLOSS, an algorithm that performs local subspace
outlier detection using global neighbourhoods. Experiments on synthetic data
demonstrate that GLOSS more accurately detects local outliers in mixed data
than its competitors. Moreover, experiments on real-world data show that our
approach identifies relevant outliers overlooked by existing methods,
confirming that one should keep an eye on the global perspective even when
doing local outlier detection.
| [
{
"version": "v1",
"created": "Tue, 1 Nov 2016 11:22:26 GMT"
}
] | 1,478,044,800,000 | [
[
"van Stein",
"Bas",
""
],
[
"van Leeuwen",
"Matthijs",
""
],
[
"Bäck",
"Thomas",
""
]
] |
1611.00549 | Oliver Cliff | Oliver M. Cliff and Mikhail Prokopenko and Robert Fitch | Inferring Coupling of Distributed Dynamical Systems via Transfer Entropy | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we are interested in structure learning for a set of spatially
distributed dynamical systems, where individual subsystems are coupled via
latent variables and observed through a filter. We represent this model as a
directed acyclic graph (DAG) that characterises the unidirectional coupling
between subsystems. Standard approaches to structure learning are not
applicable in this framework due to the hidden variables, however we can
exploit the properties of certain dynamical systems to formulate exact methods
based on state space reconstruction. We approach the problem by using
reconstruction theorems to analytically derive a tractable expression for the
KL-divergence of a candidate DAG from the observed dataset. We show this
measure can be decomposed as a function of two information-theoretic measures,
transfer entropy and stochastic interaction. We then present two mathematically
robust scoring functions based on transfer entropy and statistical independence
tests. These results support the previously held conjecture that transfer
entropy can be used to infer effective connectivity in complex networks.
| [
{
"version": "v1",
"created": "Wed, 2 Nov 2016 11:23:54 GMT"
}
] | 1,478,131,200,000 | [
[
"Cliff",
"Oliver M.",
""
],
[
"Prokopenko",
"Mikhail",
""
],
[
"Fitch",
"Robert",
""
]
] |
1611.00576 | Florentin Smarandache | W. B. Vasantha Kandasamy, Ilanthenral K, Florentin Smarandache | Strong Neutrosophic Graphs and Subgraph Topological Subspaces | 226 pages, many graphs, Europa Belgique, 2016 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this book authors for the first time introduce the notion of strong
neutrosophic graphs. They are very different from the usual graphs and
neutrosophic graphs. Using these new structures special subgraph topological
spaces are defined. Further special lattice graph of subgraphs of these graphs
are defined and described. Several interesting properties using subgraphs of a
strong neutrosophic graph are obtained. Several open conjectures are proposed.
These new class of strong neutrosophic graphs will certainly find applications
in Neutrosophic Cognitive Maps (NCM), Neutrosophic Relational Maps (NRM) and
Neutrosophic Relational Equations (NRE) with appropriate modifications.
| [
{
"version": "v1",
"created": "Sun, 30 Oct 2016 15:10:55 GMT"
}
] | 1,478,131,200,000 | [
[
"Kandasamy",
"W. B. Vasantha",
""
],
[
"K",
"Ilanthenral",
""
],
[
"Smarandache",
"Florentin",
""
]
] |
1611.00685 | Jan Feyereisl | Marek Rosa, Jan Feyereisl and The GoodAI Collective | A Framework for Searching for General Artificial Intelligence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is a significant lack of unified approaches to building generally
intelligent machines. The majority of current artificial intelligence research
operates within a very narrow field of focus, frequently without considering
the importance of the 'big picture'. In this document, we seek to describe and
unify principles that guide the basis of our development of general artificial
intelligence. These principles revolve around the idea that intelligence is a
tool for searching for general solutions to problems. We define intelligence as
the ability to acquire skills that narrow this search, diversify it and help
steer it to more promising areas. We also provide suggestions for studying,
measuring, and testing the various skills and abilities that a human-level
intelligent machine needs to acquire. The document aims to be both
implementation agnostic, and to provide an analytic, systematic, and scalable
way to generate hypotheses that we believe are needed to meet the necessary
conditions in the search for general artificial intelligence. We believe that
such a framework is an important stepping stone for bringing together
definitions, highlighting open problems, connecting researchers willing to
collaborate, and for unifying the arguably most significant search of this
century.
| [
{
"version": "v1",
"created": "Wed, 2 Nov 2016 17:02:14 GMT"
}
] | 1,478,131,200,000 | [
[
"Rosa",
"Marek",
""
],
[
"Feyereisl",
"Jan",
""
],
[
"Collective",
"The GoodAI",
""
]
] |
1611.01080 | Giuliano Armano | Giuliano Armano | Probabilistic Modeling of Progressive Filtering | The article entitled Modeling Progressive Filtering, published on
Fundamenta Informaticae (Vol. 138, Issue 3, pp. 285-320, July 2015), has been
derived from this extended report | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Progressive filtering is a simple way to perform hierarchical classification,
inspired by the behavior that most humans put into practice while attempting to
categorize an item according to an underlying taxonomy. Each node of the
taxonomy being associated with a different category, one may visualize the
categorization process by looking at the item going downwards through all the
nodes that accept it as belonging to the corresponding category. This paper is
aimed at modeling the progressive filtering technique from a probabilistic
perspective, in a hierarchical text categorization setting. As a result, the
designer of a system based on progressive filtering should be facilitated in
the task of devising, training, and testing it.
| [
{
"version": "v1",
"created": "Thu, 3 Nov 2016 16:31:32 GMT"
}
] | 1,478,217,600,000 | [
[
"Armano",
"Giuliano",
""
]
] |
1611.02154 | Meisam Hejazi Nia | Meisam Hejazi Nia, Brian Ratchford | Bayesian Non-parametric model to Target Gamification Notifications Using
Big Data | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I suggest an approach that helps the online marketers to target their
Gamification elements to users by modifying the order of the list of tasks that
they send to users. It is more realistic and flexible as it allows the model to
learn more parameters when the online marketers collect more data. The
targeting approach is scalable and quick, and it can be used over streaming
data.
| [
{
"version": "v1",
"created": "Fri, 4 Nov 2016 04:40:23 GMT"
}
] | 1,478,563,200,000 | [
[
"Nia",
"Meisam Hejazi",
""
],
[
"Ratchford",
"Brian",
""
]
] |
1611.02439 | Sarah Alice Gaggl | Sarah Alice Gaggl, Juan Carlos Nieves, Hannes Strass | Proceedings of the First International Workshop on Argumentation in
Logic Programming and Non-Monotonic Reasoning (Arg-LPNMR 2016) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This volume contains the papers presented at Arg-LPNMR 2016: First
International Workshop on Argumentation in Logic Programming and Nonmonotonic
Reasoning held on July 8-10, 2016 in New York City, NY.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2016 09:17:08 GMT"
}
] | 1,478,649,600,000 | [
[
"Gaggl",
"Sarah Alice",
""
],
[
"Nieves",
"Juan Carlos",
""
],
[
"Strass",
"Hannes",
""
]
] |
1611.02453 | Thorsten Wissmann | Carsten Lutz and Frank Wolter | The Data Complexity of Description Logic Ontologies | null | Logical Methods in Computer Science, Volume 13, Issue 4 (November
13, 2017) lmcs:2203 | 10.23638/LMCS-13(4:7)2017 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyze the data complexity of ontology-mediated querying where the
ontologies are formulated in a description logic (DL) of the ALC family and
queries are conjunctive queries, positive existential queries, or acyclic
conjunctive queries. Our approach is non-uniform in the sense that we aim to
understand the complexity of each single ontology instead of for all ontologies
formulated in a certain language. While doing so, we quantify over the queries
and are interested, for example, in the question whether all queries can be
evaluated in polynomial time w.r.t. a given ontology. Our results include a
PTime/coNP-dichotomy for ontologies of depth one in the description logic
ALCFI, the same dichotomy for ALC- and ALCI-ontologies of unrestricted depth,
and the non-existence of such a dichotomy for ALCF-ontologies. For the latter
DL, we additionally show that it is undecidable whether a given ontology admits
PTime query evaluation. We also consider the connection between PTime query
evaluation and rewritability into (monadic) Datalog.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2016 09:52:54 GMT"
},
{
"version": "v2",
"created": "Tue, 24 Oct 2017 09:19:25 GMT"
},
{
"version": "v3",
"created": "Fri, 10 Nov 2017 09:38:00 GMT"
}
] | 1,687,392,000,000 | [
[
"Lutz",
"Carsten",
""
],
[
"Wolter",
"Frank",
""
]
] |
1611.02646 | Tatiana Makhalova | Sergei O. Kuznetsov, Tatiana Makhalova | On interestingness measures of formal concepts | 20 pages, 5 figures, 3 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Formal concepts and closed itemsets proved to be of big importance for
knowledge discovery, both as a tool for concise representation of association
rules and a tool for clustering and constructing domain taxonomies and
ontologies. Exponential explosion makes it difficult to consider the whole
concept lattice arising from data, one needs to select most useful and
interesting concepts. In this paper interestingness measures of concepts are
considered and compared with respect to various aspects, such as efficiency of
computation and applicability to noisy data and performing ranking correlation.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2016 18:26:24 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Apr 2017 18:19:22 GMT"
}
] | 1,492,732,800,000 | [
[
"Kuznetsov",
"Sergei O.",
""
],
[
"Makhalova",
"Tatiana",
""
]
] |
1611.02885 | Martin Diller | Martin Diller, Anthony Hunter | Encoding monotonic multi-set preferences using CI-nets: preliminary
report | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | CP-nets and their variants constitute one of the main AI approaches for
specifying and reasoning about preferences. CI-nets, in particular, are a
CP-inspired formalism for representing ordinal preferences over sets of goods,
which are typically required to be monotonic.
Considering also that goods often come in multi-sets rather than sets, a
natural question is whether CI-nets can be used more or less directly to encode
preferences over multi-sets. We here provide some initial ideas on how to
achieve this, in the sense that at least a restricted form of reasoning on our
framework, which we call "confined reasoning", can be efficiently reduced to
reasoning on CI-nets. Our framework nevertheless allows for encoding
preferences over multi-sets with unbounded multiplicities. We also show the
extent to which it can be used to represent preferences where multiplicites of
the goods are not stated explicitly ("purely qualitative preferences") as well
as a potential use of our generalization of CI-nets as a component of a recent
system for evidence aggregation.
| [
{
"version": "v1",
"created": "Wed, 9 Nov 2016 10:56:42 GMT"
}
] | 1,478,736,000,000 | [
[
"Diller",
"Martin",
""
],
[
"Hunter",
"Anthony",
""
]
] |
1611.03398 | Christophe Lecoutre | Frederic Boussemart and Christophe Lecoutre and Gilles Audemard and
C\'edric Piette | XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained
Problems | 238 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | We propose a major revision of the format XCSP 2.1, called XCSP3, to build
integrated representations of combinatorial constrained problems. This new
format is able to deal with mono/multi optimization, many types of variables,
cost functions, reification, views, annotations, variable quantification,
distributed, probabilistic and qualitative reasoning. The new format is made
compact, highly readable, and rather easy to parse. Interestingly, it captures
the structure of the problem models, through the possibilities of declaring
arrays of variables, and identifying syntactic and semantic groups of
constraints. The number of constraints is kept under control by introducing a
limited set of basic constraint forms, and producing almost automatically some
of their variations through lifting, restriction, sliding, logical combination
and relaxation mechanisms. As a result, XCSP3 encompasses practically all
constraints that can be found in major constraint solvers developed by the CP
community. A website, which is developed conjointly with the format, contains
many models and series of instances. The user can make sophisticated queries
for selecting instances from very precise criteria. The objective of XCSP3 is
to ease the effort required to test and compare different algorithms by
providing a common test-bed of combinatorial constrained instances.
| [
{
"version": "v1",
"created": "Thu, 10 Nov 2016 17:00:56 GMT"
},
{
"version": "v2",
"created": "Fri, 6 Apr 2018 09:06:18 GMT"
},
{
"version": "v3",
"created": "Sat, 16 Jan 2021 12:18:55 GMT"
},
{
"version": "v4",
"created": "Mon, 7 Nov 2022 10:26:49 GMT"
}
] | 1,667,865,600,000 | [
[
"Boussemart",
"Frederic",
""
],
[
"Lecoutre",
"Christophe",
""
],
[
"Audemard",
"Gilles",
""
],
[
"Piette",
"Cédric",
""
]
] |
1611.03977 | Kui Yu | Kui Yu, Jiuyong Li, Lin Liu | A Review on Algorithms for Constraint-based Causal Discovery | This paper has been withdrawn by the author due to further
improvement | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causal discovery studies the problem of mining causal relationships between
variables from data, which is of primary interest in science. During the past
decades, significant amount of progresses have been made toward this
fundamental data mining paradigm. Recent years, as the availability of abundant
large-sized and complex observational data, the constrain-based approaches have
gradually attracted a lot of interest and have been widely applied to many
diverse real-world problems due to the fast running speed and easy generalizing
to the problem of causal insufficiency. In this paper, we aim to review the
constraint-based causal discovery algorithms. Firstly, we discuss the learning
paradigm of the constraint-based approaches. Secondly and primarily, the
state-of-the-art constraint-based casual inference algorithms are surveyed with
the detailed analysis. Thirdly, several related open-source software packages
and benchmark data repositories are briefly summarized. As a conclusion, some
open problems in constraint-based causal discovery are outlined for future
research.
| [
{
"version": "v1",
"created": "Sat, 12 Nov 2016 09:25:38 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Nov 2016 22:33:25 GMT"
}
] | 1,480,291,200,000 | [
[
"Yu",
"Kui",
""
],
[
"Li",
"Jiuyong",
""
],
[
"Liu",
"Lin",
""
]
] |
1611.04146 | Quan Liu | Quan Liu, Hui Jiang, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, Yu Hu | Commonsense Knowledge Enhanced Embeddings for Solving Pronoun
Disambiguation Problems in Winograd Schema Challenge | Winograd Schema Challenge, Pronoun Disambiguation Problems, Neural
Embedding Methods, Commonsense Knowledge | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose commonsense knowledge enhanced embeddings (KEE) for
solving the Pronoun Disambiguation Problems (PDP). The PDP task we investigate
in this paper is a complex coreference resolution task which requires the
utilization of commonsense knowledge. This task is a standard first round test
set in the 2016 Winograd Schema Challenge. In this task, traditional linguistic
features that are useful for coreference resolution, e.g. context and gender
information, are no longer effective anymore. Therefore, the KEE models are
proposed to provide a general framework to make use of commonsense knowledge
for solving the PDP problems. Since the PDP task doesn't have training data,
the KEE models would be used during the unsupervised feature extraction
process. To evaluate the effectiveness of the KEE models, we propose to
incorporate various commonsense knowledge bases, including ConceptNet, WordNet,
and CauseCom, into the KEE training process. We achieved the best performance
by applying the proposed methods to the 2016 Winograd Schema Challenge. In
addition, experiments conducted on the standard PDP task indicate that, the
proposed KEE models could solve the PDP problems by achieving 66.7% accuracy,
which is a new state-of-the-art performance.
| [
{
"version": "v1",
"created": "Sun, 13 Nov 2016 15:38:32 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Dec 2016 02:27:16 GMT"
}
] | 1,482,451,200,000 | [
[
"Liu",
"Quan",
""
],
[
"Jiang",
"Hui",
""
],
[
"Ling",
"Zhen-Hua",
""
],
[
"Zhu",
"Xiaodan",
""
],
[
"Wei",
"Si",
""
],
[
"Hu",
"Yu",
""
]
] |
1611.04363 | Yujie Qian | Yujie Qian, Jie Tang, Kan Wu | Weakly Learning to Match Experts in Online Community | IJCAI 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In online question-and-answer (QA) websites like Quora, one central issue is
to find (invite) users who are able to provide answers to a given question and
at the same time would be unlikely to say "no" to the invitation. The challenge
is how to trade off the matching degree between users' expertise and the
question topic, and the likelihood of positive response from the invited users.
In this paper, we formally formulate the problem and develop a weakly
supervised factor graph (WeakFG) model to address the problem. The model
explicitly captures expertise matching degree between questions and users. To
model the likelihood that an invited user is willing to answer a specific
question, we incorporate a set of correlations based on social identity theory
into the WeakFG model. We use two different genres of datasets: QA-Expert and
Paper-Reviewer, to validate the proposed model. Our experimental results show
that the proposed model can significantly outperform (+1.5-10.7% by MAP) the
state-of-the-art algorithms for matching users (experts) with community
questions. We have also developed an online system to further demonstrate the
advantages of the proposed method.
| [
{
"version": "v1",
"created": "Mon, 14 Nov 2016 12:46:24 GMT"
},
{
"version": "v2",
"created": "Mon, 7 May 2018 21:35:10 GMT"
}
] | 1,525,824,000,000 | [
[
"Qian",
"Yujie",
""
],
[
"Tang",
"Jie",
""
],
[
"Wu",
"Kan",
""
]
] |
1611.05190 | Carmine Dodaro | Carmine Dodaro, Philip Gasteiger, Nicola Leone, Benjamin Musitsch,
Francesco Ricca, and Konstantin Schekotihin | Driving CDCL Search | Paper presented at the 1st Workshop on Trends and Applications of
Answer Set Programming (TAASP 2016), Klagenfurt, Austria, 26 September 2016,
15 pages, LaTeX, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The CDCL algorithm is the leading solution adopted by state-of-the-art
solvers for SAT, SMT, ASP, and others. Experiments show that the performance of
CDCL solvers can be significantly boosted by embedding domain-specific
heuristics, especially on large real-world problems. However, a proper
integration of such criteria in off-the-shelf CDCL implementations is not
obvious. In this paper, we distill the key ingredients that drive the search of
CDCL solvers, and propose a general framework for designing and implementing
new heuristics. We implemented our strategy in an ASP solver, and we
experimented on two industrial domains. On hard problem instances,
state-of-the-art implementations fail to find any solution in acceptable time,
whereas our implementation is very successful and finds all solutions.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 09:13:26 GMT"
}
] | 1,479,340,800,000 | [
[
"Dodaro",
"Carmine",
""
],
[
"Gasteiger",
"Philip",
""
],
[
"Leone",
"Nicola",
""
],
[
"Musitsch",
"Benjamin",
""
],
[
"Ricca",
"Francesco",
""
],
[
"Schekotihin",
"Konstantin",
""
]
] |
1611.05735 | Yaniv Altshuler | Yaniv Altshuler, Alex Pentland, Shlomo Bekhor, Yoram Shiftan, Alfred
Bruckstein | Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of
Reconnaissance UAVs | 35 pages, 19 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current state of the art in the field of UAV activation relies solely on
human operators for the design and adaptation of the drones' flying routes.
Furthermore, this is being done today on an individual level (one vehicle per
operators), with some exceptions of a handful of new systems, that are
comprised of a small number of self-organizing swarms, manually guided by a
human operator.
Drones-based monitoring is of great importance in variety of civilian
domains, such as road safety, homeland security, and even environmental
control. In its military aspect, efficiently detecting evading targets by a
fleet of unmanned drones has an ever increasing impact on the ability of modern
armies to engage in warfare. The latter is true both traditional symmetric
conflicts among armies as well as asymmetric ones. Be it a speeding driver, a
polluting trailer or a covert convoy, the basic challenge remains the same --
how can its detection probability be maximized using as little number of drones
as possible.
In this work we propose a novel approach for the optimization of large scale
swarms of reconnaissance drones -- capable of producing on-demand optimal
coverage strategies for any given search scenario. Given an estimation cost of
the threat's potential damages, as well as types of monitoring drones available
and their comparative performance, our proposed method generates an
analytically provable strategy, stating the optimal number and types of drones
to be deployed, in order to cost-efficiently monitor a pre-defined region for
targets maneuvering using a given roads networks.
We demonstrate our model using a unique dataset of the Israeli transportation
network, on which different deployment schemes for drones deployment are
evaluated.
| [
{
"version": "v1",
"created": "Thu, 17 Nov 2016 15:28:14 GMT"
}
] | 1,479,427,200,000 | [
[
"Altshuler",
"Yaniv",
""
],
[
"Pentland",
"Alex",
""
],
[
"Bekhor",
"Shlomo",
""
],
[
"Shiftan",
"Yoram",
""
],
[
"Bruckstein",
"Alfred",
""
]
] |
1611.05740 | Wacha Bounliphone | Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis
Antonoglou, Arthur Gretton, Matthew B. Blashcko | Fast Non-Parametric Tests of Relative Dependency and Similarity | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce two novel non-parametric statistical hypothesis tests. The first
test, called the relative test of dependency, enables us to determine whether
one source variable is significantly more dependent on a first target variable
or a second. Dependence is measured via the Hilbert-Schmidt Independence
Criterion (HSIC). The second test, called the relative test of similarity, is
use to determine which of the two samples from arbitrary distributions is
significantly closer to a reference sample of interest and the relative measure
of similarity is based on the Maximum Mean Discrepancy (MMD). To construct
these tests, we have used as our test statistics the difference of HSIC
statistics and of MMD statistics, respectively. The resulting tests are
consistent and unbiased, and have favorable convergence properties. The
effectiveness of the relative dependency test is demonstrated on several
real-world problems: we identify languages groups from a multilingual parallel
corpus, and we show that tumor location is more dependent on gene expression
than chromosome imbalance. We also demonstrate the performance of the relative
test of similarity over a broad selection of model comparisons problems in deep
generative models.
| [
{
"version": "v1",
"created": "Thu, 17 Nov 2016 15:36:31 GMT"
}
] | 1,479,427,200,000 | [
[
"Bounliphone",
"Wacha",
""
],
[
"Belilovsky",
"Eugene",
""
],
[
"Tenenhaus",
"Arthur",
""
],
[
"Antonoglou",
"Ioannis",
""
],
[
"Gretton",
"Arthur",
""
],
[
"Blashcko",
"Matthew B.",
""
]
] |
1611.06108 | Daniil Galaktionov | Daniil Galaktionov, Miguel R. Luaces, \'Angeles S. Places | Navigational Rule Derivation: An algorithm to determine the effect of
traffic signs on road networks | This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941. in PACIS 2016 Online
Proceedings | Proceeding of the 20th Pacific Asia Conference on Information
Systems (PACIS 2016). Association for Information Systems. AIS Electronic
Library (AISeL). Paper 94. ISBN: 9789860491029 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present an algorithm to build a road network map enriched
with traffic rules such as one-way streets and forbidden turns, based on the
interpretation of already detected and classified traffic signs. Such algorithm
helps to automatize the elaboration of maps for commercial navigation systems.
Our solution is based on simulating navigation along the road network,
determining at each point of interest the visibility of the signs and their
effect on the roads. We test our approach in a small urban network and discuss
various ways to generalize it to support more complex environments.
| [
{
"version": "v1",
"created": "Thu, 17 Nov 2016 18:39:44 GMT"
}
] | 1,479,772,800,000 | [
[
"Galaktionov",
"Daniil",
""
],
[
"Luaces",
"Miguel R.",
""
],
[
"Places",
"Ángeles S.",
""
]
] |
1611.06174 | Ondrej Kuzelka | Ondrej Kuzelka, Jesse Davis, Steven Schockaert | Stratified Knowledge Bases as Interpretable Probabilistic Models
(Extended Abstract) | Presented at NIPS 2016 Workshop on Interpretable Machine Learning in
Complex Systems | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we advocate the use of stratified logical theories for
representing probabilistic models. We argue that such encodings can be more
interpretable than those obtained in existing frameworks such as Markov logic
networks. Among others, this allows for the use of domain experts to improve
learned models by directly removing, adding, or modifying logical formulas.
| [
{
"version": "v1",
"created": "Fri, 18 Nov 2016 17:51:56 GMT"
}
] | 1,479,686,400,000 | [
[
"Kuzelka",
"Ondrej",
""
],
[
"Davis",
"Jesse",
""
],
[
"Schockaert",
"Steven",
""
]
] |
1611.07478 | Scott Lundberg | Scott Lundberg and Su-In Lee | An unexpected unity among methods for interpreting model predictions | Presented at NIPS 2016 Workshop on Interpretable Machine Learning in
Complex Systems | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding why a model made a certain prediction is crucial in many data
science fields. Interpretable predictions engender appropriate trust and
provide insight into how the model may be improved. However, with large modern
datasets the best accuracy is often achieved by complex models even experts
struggle to interpret, which creates a tension between accuracy and
interpretability. Recently, several methods have been proposed for interpreting
predictions from complex models by estimating the importance of input features.
Here, we present how a model-agnostic additive representation of the importance
of input features unifies current methods. This representation is optimal, in
the sense that it is the only set of additive values that satisfies important
properties. We show how we can leverage these properties to create novel visual
explanations of model predictions. The thread of unity that this representation
weaves through the literature indicates that there are common principles to be
learned about the interpretation of model predictions that apply in many
scenarios.
| [
{
"version": "v1",
"created": "Tue, 22 Nov 2016 19:30:28 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Nov 2016 06:44:36 GMT"
},
{
"version": "v3",
"created": "Thu, 8 Dec 2016 08:24:15 GMT"
}
] | 1,481,241,600,000 | [
[
"Lundberg",
"Scott",
""
],
[
"Lee",
"Su-In",
""
]
] |
1611.08037 | Lantao Liu | Zhibei Ma, Kai Yin, Lantao Liu, Gaurav S. Sukhatme | A Spatio-Temporal Representation for the Orienteering Problem with
Time-Varying Profits | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider an orienteering problem (OP) where an agent needs to visit a
series (possibly a subset) of depots, from which the maximal accumulated
profits are desired within given limited time budget. Different from most
existing works where the profits are assumed to be static, in this work we
investigate a variant that has arbitrary time-dependent profits. Specifically,
the profits to be collected change over time and they follow different (e.g.,
independent) time-varying functions. The problem is of inherent nonlinearity
and difficult to solve by existing methods. To tackle the challenge, we present
a simple and effective framework that incorporates time-variations into the
fundamental planning process. Specifically, we propose a deterministic
spatio-temporal representation where both spatial description and temporal
logic are unified into one routing topology. By employing existing basic
sorting and searching algorithms, the routing solutions can be computed in an
extremely efficient way. The proposed method is easy to implement and extensive
numerical results show that our approach is time efficient and generates
near-optimal solutions.
| [
{
"version": "v1",
"created": "Thu, 24 Nov 2016 00:07:56 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Jul 2017 04:56:41 GMT"
}
] | 1,499,126,400,000 | [
[
"Ma",
"Zhibei",
""
],
[
"Yin",
"Kai",
""
],
[
"Liu",
"Lantao",
""
],
[
"Sukhatme",
"Gaurav S.",
""
]
] |
1611.08103 | Guangming Lang | Guangming Lang | Double-quantitative $\gamma^{\ast}-$fuzzy coverings approximation
operators | It enriches the fuzzy covering rough set theory | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In digital-based information boom, the fuzzy covering rough set model is an
important mathematical tool for artificial intelligence, and how to build the
bridge between the fuzzy covering rough set theory and Pawlak's model is
becoming a hot research topic. In this paper, we first present the
$\gamma-$fuzzy covering based probabilistic and grade approximation operators
and double-quantitative approximation operators. We also study the
relationships among the three types of $\gamma-$fuzzy covering based
approximation operators. Second, we propose the $\gamma^{\ast}-$fuzzy coverings
based multi-granulation probabilistic and grade lower and upper approximation
operators and multi-granulation double-quantitative lower and upper
approximation operators. We also investigate the relationships among these
types of $\gamma-$fuzzy coverings based approximation operators. Finally, we
employ several examples to illustrate how to construct the lower and upper
approximations of fuzzy sets with the absolute and relative quantitative
information.
| [
{
"version": "v1",
"created": "Thu, 24 Nov 2016 09:06:57 GMT"
}
] | 1,480,291,200,000 | [
[
"Lang",
"Guangming",
""
]
] |
1611.08219 | Dylan Hadfield-Menell | Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, Stuart Russell | The Off-Switch Game | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is clear that one of the primary tools we can use to mitigate the
potential risk from a misbehaving AI system is the ability to turn the system
off. As the capabilities of AI systems improve, it is important to ensure that
such systems do not adopt subgoals that prevent a human from switching them
off. This is a challenge because many formulations of rational agents create
strong incentives for self-preservation. This is not caused by a built-in
instinct, but because a rational agent will maximize expected utility and
cannot achieve whatever objective it has been given if it is dead. Our goal is
to study the incentives an agent has to allow itself to be switched off. We
analyze a simple game between a human H and a robot R, where H can press R's
off switch but R can disable the off switch. A traditional agent takes its
reward function for granted: we show that such agents have an incentive to
disable the off switch, except in the special case where H is perfectly
rational. Our key insight is that for R to want to preserve its off switch, it
needs to be uncertain about the utility associated with the outcome, and to
treat H's actions as important observations about that utility. (R also has no
incentive to switch itself off in this setting.) We conclude that giving
machines an appropriate level of uncertainty about their objectives leads to
safer designs, and we argue that this setting is a useful generalization of the
classical AI paradigm of rational agents.
| [
{
"version": "v1",
"created": "Thu, 24 Nov 2016 15:23:48 GMT"
},
{
"version": "v2",
"created": "Thu, 25 May 2017 17:05:16 GMT"
},
{
"version": "v3",
"created": "Fri, 16 Jun 2017 01:41:59 GMT"
}
] | 1,497,830,400,000 | [
[
"Hadfield-Menell",
"Dylan",
""
],
[
"Dragan",
"Anca",
""
],
[
"Abbeel",
"Pieter",
""
],
[
"Russell",
"Stuart",
""
]
] |
1611.08374 | Bj{\o}rn Magnus Mathisen | Bj{\o}rn Magnus Mathisen, Peter Haro, B{\aa}rd Hanssen, Sara Bj\"ork,
St{\aa}le Walderhaug | Decision Support Systems in Fisheries and Aquaculture: A systematic
review | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Decision support systems help decision makers make better decisions in the
face of complex decision problems (e.g. investment or policy decisions).
Fisheries and Aquaculture is a domain where decision makers face such decisions
since they involve factors from many different scientific fields. No systematic
overview of literature describing decision support systems and their
application in fisheries and aquaculture has been conducted. This paper
summarizes scientific literature that describes decision support systems
applied to the domain of Fisheries and Aquaculture. We use an established
systematic mapping survey method to conduct our literature mapping. Our
research questions are: What decision support systems for fisheries and
aquaculture exists? What are the most investigated fishery and aquaculture
decision support systems topics and how have these changed over time? Do any
current DSS for fisheries provide real- time analytics? Do DSSes in Fisheries
and Aquaculture build their models using machine learning done on captured and
grounded data? The paper then detail how we employ the systematic mapping
method in answering these questions. This results in 27 papers being identified
as relevant and gives an exposition on the primary methods concluded in the
study for designing a decision support system. We provide an analysis of the
research done in the studies collected. We discovered that most literature does
not consider multiple aspects for multiple stakeholders in their work. In
addition we observed that little or no work has been done with real-time
analysis in these decision support systems.
| [
{
"version": "v1",
"created": "Fri, 25 Nov 2016 08:13:51 GMT"
}
] | 1,480,291,200,000 | [
[
"Mathisen",
"Bjørn Magnus",
""
],
[
"Haro",
"Peter",
""
],
[
"Hanssen",
"Bård",
""
],
[
"Björk",
"Sara",
""
],
[
"Walderhaug",
"Ståle",
""
]
] |
1611.08499 | Nhien Pham Hoang Bao | Nhien Pham Hoang Bao, Hiroyuki Iida | An Analysis of Tournament Structure | 10 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper explores a novel way for analyzing the tournament structures to
find a best suitable one for the tournament under consideration. It concerns
about three aspects such as tournament conducting cost, competitiveness
development and ranking precision. It then proposes a new method using progress
tree to detect potential throwaway matches. The analysis performed using the
proposed method reveals the strengths and weaknesses of tournament structures.
As a conclusion, single elimination is best if we want to qualify one winner
only, all matches conducted are exciting in term of competitiveness. Double
elimination with proper seeding system is a better choice if we want to qualify
more winners. A reasonable number of extra matches need to be conducted in
exchange of being able to qualify top four winners. Round-robin gives reliable
ranking precision for all participants. However, its conduction cost is very
high, and it fails to maintain competitiveness development.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 07:09:16 GMT"
}
] | 1,480,291,200,000 | [
[
"Bao",
"Nhien Pham Hoang",
""
],
[
"Iida",
"Hiroyuki",
""
]
] |
1611.08555 | Florentin Smarandache | Florentin Smarandache, Surapati Pramanik (Editors) | New Trends in Neutrosophic Theory and Applications | 424 pages | Pons asbl, Brussels, 2016 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neutrosophic theory and applications have been expanding in all directions at
an astonishing rate especially after the introduction the journal entitled
Neutrosophic Sets and Systems. New theories, techniques, algorithms have been
rapidly developed. One of the most striking trends in the neutrosophic theory
is the hybridization of neutrosophic set with other potential sets such as
rough set, bipolar set, soft set, hesitant fuzzy set, etc. The different hybrid
structure such as rough neutrosophic set, single valued neutrosophic rough set,
bipolar neutrosophic set, single valued neutrosophic hesitant fuzzy set, etc.
are proposed in the literature in a short period of time. Neutrosophic set has
been a very important tool in all various areas of data mining, decision
making, e-learning, engineering, medicine, social science, and some more. The
book New Trends in Neutrosophic Theories and Applications focuses on theories,
methods, algorithms for decision making and also applications involving
neutrosophic information. Some topics deal with data mining, decision making,
e-learning, graph theory, medical diagnosis, probability theory, topology, and
some more.
| [
{
"version": "v1",
"created": "Wed, 23 Nov 2016 19:16:49 GMT"
}
] | 1,480,291,200,000 | [
[
"Smarandache",
"Florentin",
"",
"Editors"
],
[
"Pramanik",
"Surapati",
"",
"Editors"
]
] |
1611.08572 | Till Mossakowski | Till Mossakowski and Fabian Neuhaus | Bipolar Weighted Argumentation Graphs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses the semantics of weighted argumentation graphs that are
biplor, i.e. contain both attacks and support graphs. The work builds on
previous work by Amgoud, Ben-Naim et. al., which presents and compares several
semantics for argumentation graphs that contain only supports or only attacks
relationships, respectively.
| [
{
"version": "v1",
"created": "Fri, 25 Nov 2016 20:04:17 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Dec 2016 08:33:34 GMT"
}
] | 1,482,710,400,000 | [
[
"Mossakowski",
"Till",
""
],
[
"Neuhaus",
"Fabian",
""
]
] |
1611.08908 | Thierry Petit | Thierry Petit | "Model and Run" Constraint Networks with a MILP Engine | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Constraint Programming (CP) users need significant expertise in order to
model their problems appropriately, notably to select propagators and search
strategies. This puts the brakes on a broader uptake of CP. In this paper, we
introduce MICE, a complete Java CP modeler that can use any Mixed Integer
Linear Programming (MILP) solver as a solution technique. Our aim is to provide
an alternative tool for democratizing the "CP-style" modeling thanks to its
simplicity of use, with reasonable solving capabilities. Our contributions
include new decompositions of (reified) constraints and constraints on
numerical variables.
| [
{
"version": "v1",
"created": "Sun, 27 Nov 2016 20:43:27 GMT"
}
] | 1,480,377,600,000 | [
[
"Petit",
"Thierry",
""
]
] |
1611.08944 | Jan Leike | Jan Leike | Nonparametric General Reinforcement Learning | PhD thesis | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Reinforcement learning (RL) problems are often phrased in terms of Markov
decision processes (MDPs). In this thesis we go beyond MDPs and consider RL in
environments that are non-Markovian, non-ergodic and only partially observable.
Our focus is not on practical algorithms, but rather on the fundamental
underlying problems: How do we balance exploration and exploitation? How do we
explore optimally? When is an agent optimal? We follow the nonparametric
realizable paradigm.
We establish negative results on Bayesian RL agents, in particular AIXI. We
show that unlucky or adversarial choices of the prior cause the agent to
misbehave drastically. Therefore Legg-Hutter intelligence and balanced Pareto
optimality, which depend crucially on the choice of the prior, are entirely
subjective. Moreover, in the class of all computable environments every policy
is Pareto optimal. This undermines all existing optimality properties for AIXI.
However, there are Bayesian approaches to general RL that satisfy objective
optimality guarantees: We prove that Thompson sampling is asymptotically
optimal in stochastic environments in the sense that its value converges to the
value of the optimal policy. We connect asymptotic optimality to regret given a
recoverability assumption on the environment that allows the agent to recover
from mistakes. Hence Thompson sampling achieves sublinear regret in these
environments.
Our results culminate in a formal solution to the grain of truth problem: A
Bayesian agent acting in a multi-agent environment learns to predict the other
agents' policies if its prior assigns positive probability to them (the prior
contains a grain of truth). We construct a large but limit computable class
containing a grain of truth and show that agents based on Thompson sampling
over this class converge to play Nash equilibria in arbitrary unknown
computable multi-agent environments.
| [
{
"version": "v1",
"created": "Mon, 28 Nov 2016 00:36:40 GMT"
}
] | 1,480,377,600,000 | [
[
"Leike",
"Jan",
""
]
] |
1611.09351 | Jan Bergstra | Jan A. Bergstra | Adams Conditioning and Likelihood Ratio Transfer Mediated Inference | Based on reviewer's comments many minor improvements have been made | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian inference as applied in a legal setting is about belief transfer and
involves a plurality of agents and communication protocols.
A forensic expert (FE) may communicate to a trier of fact (TOF) first its
value of a certain likelihood ratio with respect to FE's belief state as
represented by a probability function on FE's proposition space. Subsequently
FE communicates its recently acquired confirmation that a certain evidence
proposition is true. Then TOF performs likelihood ratio transfer mediated
reasoning thereby revising their own belief state.
The logical principles involved in likelihood transfer mediated reasoning are
discussed in a setting where probabilistic arithmetic is done within a meadow,
and with Adams conditioning placed in a central role.
| [
{
"version": "v1",
"created": "Sat, 26 Nov 2016 22:31:02 GMT"
},
{
"version": "v2",
"created": "Sun, 4 Dec 2016 10:07:29 GMT"
},
{
"version": "v3",
"created": "Sun, 11 Dec 2016 11:17:38 GMT"
},
{
"version": "v4",
"created": "Tue, 18 Dec 2018 23:09:23 GMT"
},
{
"version": "v5",
"created": "Fri, 16 Aug 2019 09:55:15 GMT"
}
] | 1,566,172,800,000 | [
[
"Bergstra",
"Jan A.",
""
]
] |
1612.00092 | Christian Walder Dr | Christian Walder and Dongwoo Kim | Computer Assisted Composition with Recurrent Neural Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequence modeling with neural networks has lead to powerful models of
symbolic music data. We address the problem of exploiting these models to reach
creative musical goals, by combining with human input. To this end we
generalise previous work, which sampled Markovian sequence models under the
constraint that the sequence belong to the language of a given finite state
machine provided by the human. We consider more expressive non-Markov models,
thereby requiring approximate sampling which we provide in the form of an
efficient sequential Monte Carlo method. In addition we provide and compare
with a beam search strategy for conditional probability maximisation.
Our algorithms are capable of convincingly re-harmonising famous musical
works. To demonstrate this we provide visualisations, quantitative experiments,
a human listening test and audio examples. We find both the sampling and
optimisation procedures to be effective, yet complementary in character. For
the case of highly permissive constraint sets, we find that sampling is to be
preferred due to the overly regular nature of the optimisation based results.
The generality of our algorithms permits countless other creative applications.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2016 00:49:19 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Sep 2017 23:38:35 GMT"
}
] | 1,506,988,800,000 | [
[
"Walder",
"Christian",
""
],
[
"Kim",
"Dongwoo",
""
]
] |
1612.00094 | Paul Weng | Hugo Gilbert and Paul Weng and Yan Xu | Optimizing Quantiles in Preference-based Markov Decision Processes | Long version of AAAI 2017 paper. arXiv admin note: text overlap with
arXiv:1611.00862 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the Markov decision process model, policies are usually evaluated by
expected cumulative rewards. As this decision criterion is not always suitable,
we propose in this paper an algorithm for computing a policy optimal for the
quantile criterion. Both finite and infinite horizons are considered. Finally
we experimentally evaluate our approach on random MDPs and on a data center
control problem.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2016 00:55:23 GMT"
}
] | 1,480,636,800,000 | [
[
"Gilbert",
"Hugo",
""
],
[
"Weng",
"Paul",
""
],
[
"Xu",
"Yan",
""
]
] |
1612.00104 | Xiaojian Wu | Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein | Robust Optimization for Tree-Structured Stochastic Network Design | AAAI 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic network design is a general framework for optimizing network
connectivity. It has several applications in computational sustainability
including spatial conservation planning, pre-disaster network preparation, and
river network optimization. A common assumption in previous work has been made
that network parameters (e.g., probability of species colonization) are
precisely known, which is unrealistic in real- world settings. We therefore
address the robust river network design problem where the goal is to optimize
river connectivity for fish movement by removing barriers. We assume that fish
passability probabilities are known only imprecisely, but are within some
interval bounds. We then develop a planning approach that computes the policies
with either high robust ratio or low regret. Empirically, our approach scales
well to large river networks. We also provide insights into the solutions
generated by our robust approach, which has significantly higher robust ratio
than the baseline solution with mean parameter estimates.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2016 01:21:21 GMT"
}
] | 1,480,636,800,000 | [
[
"Wu",
"Xiaojian",
""
],
[
"Kumar",
"Akshat",
""
],
[
"Sheldon",
"Daniel",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1612.00240 | Kleanthi Georgala | Kleanthi Georgala, Micheal Hoffmann and Axel-Cyrille Ngonga Ngomo | An Evaluation of Models for Runtime Approximation in Link Discovery | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Time-efficient link discovery is of central importance to implement the
vision of the Semantic Web. Some of the most rapid Link Discovery approaches
rely internally on planning to execute link specifications. In newer works,
linear models have been used to estimate the runtime the fastest planners.
However, no other category of models has been studied for this purpose so far.
In this paper, we study non-linear runtime estimation functions for runtime
estimation. In particular, we study exponential and mixed models for the
estimation of the runtimes of planners. To this end, we evaluate three
different models for runtime on six datasets using 400 link specifications. We
show that exponential and mixed models achieve better fits when trained but are
only to be preferred in some cases. Our evaluation also shows that the use of
better runtime approximation models has a positive impact on the overall
execution of link specifications.
| [
{
"version": "v1",
"created": "Thu, 1 Dec 2016 13:33:03 GMT"
}
] | 1,480,636,800,000 | [
[
"Georgala",
"Kleanthi",
""
],
[
"Hoffmann",
"Micheal",
""
],
[
"Ngomo",
"Axel-Cyrille Ngonga",
""
]
] |
1612.00742 | Michael Gr. Voskoglou Prof. Dr. | Michael Gr. Voskoglou | Comparison of the COG Defuzzification Technique and Its Variations to
the GPA Index | 11 pages, 5 figures, 2 tables | American Journal of Computational and Applied Mathematics, 6(5),
187-193, 2016 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Center of Gravity (COG) method is one of the most popular defuzzification
techniques of fuzzy mathematics. In earlier works the COG technique was
properly adapted to be used as an assessment model (RFAM)and several variations
of it (GRFAM, TFAM and TpFAM)were also constructed for the same purpose. In
this paper the outcomes of all these models are compared to the corresponding
outcomes of a traditional assessment method of the bi-valued logic, the Grade
Point Average (GPA) Index. Examples are also presented illustrating our
results.
| [
{
"version": "v1",
"created": "Wed, 30 Nov 2016 07:53:15 GMT"
}
] | 1,480,896,000,000 | [
[
"Voskoglou",
"Michael Gr.",
""
]
] |
1612.00916 | Pierre-Luc Bacon | Pierre-Luc Bacon, Doina Precup | A Matrix Splitting Perspective on Planning with Options | The results presented in the previous version of this paper were
found be applicable only to "gating execution" and not "call-and-return". We
made this distinction clear in the text and added an extension to the
call-and-return model | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that the Bellman operator underlying the options framework leads to a
matrix splitting, an approach traditionally used to speed up convergence of
iterative solvers for large linear systems of equations. Based on standard
comparison theorems for matrix splittings, we then show how the asymptotic rate
of convergence varies as a function of the inherent timescales of the options.
This new perspective highlights a trade-off between asymptotic performance and
the cost of computation associated with building a good set of options.
| [
{
"version": "v1",
"created": "Sat, 3 Dec 2016 02:57:36 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Jul 2017 19:28:32 GMT"
}
] | 1,499,817,600,000 | [
[
"Bacon",
"Pierre-Luc",
""
],
[
"Precup",
"Doina",
""
]
] |
1612.01120 | Fabio Cozman | Fabio Gagliardi Cozman, Denis Deratani Mau\'a | The Complexity of Bayesian Networks Specified by Propositional and
Relational Languages | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the complexity of inference in Bayesian networks specified by
logical languages. We consider representations that range from fragments of
propositional logic to function-free first-order logic with equality; in doing
so we cover a variety of plate models and of probabilistic relational models.
We study the complexity of inferences when network, query and domain are the
input (the inferential and the combined complexity), when the network is fixed
and query and domain are the input (the query/data complexity), and when the
network and query are fixed and the domain is the input (the domain
complexity). We draw connections with probabilistic databases and liftability
results, and obtain complexity classes that range from polynomial to
exponential levels.
| [
{
"version": "v1",
"created": "Sun, 4 Dec 2016 13:51:55 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2016 02:00:14 GMT"
},
{
"version": "v3",
"created": "Fri, 6 Jan 2017 13:07:30 GMT"
}
] | 1,483,920,000,000 | [
[
"Cozman",
"Fabio Gagliardi",
""
],
[
"Mauá",
"Denis Deratani",
""
]
] |
1612.01608 | Julian Togelius | Julian Togelius | AI Researchers, Video Games Are Your Friends! | in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springer | null | 10.1007/978-3-319-48506-5_1 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.
| [
{
"version": "v1",
"created": "Tue, 6 Dec 2016 00:46:57 GMT"
}
] | 1,481,068,800,000 | [
[
"Togelius",
"Julian",
""
]
] |
1612.01691 | Arthur Mah\'eo | Arthur Mah\'eo, Tommaso Urli, Philip Kilby | Fleet Size and Mix Split-Delivery Vehicle Routing | Rich Vehicle Routing, Split Delivery, Fleet Size and Mix, Mixed
Integer Programming, Constraint Programming | null | null | EP166439 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the classic Vehicle Routing Problem (VRP) a fleet of of vehicles has to
visit a set of customers while minimising the operations' costs. We study a
rich variant of the VRP featuring split deliveries, an heterogeneous fleet, and
vehicle-commodity incompatibility constraints. Our goal is twofold: define the
cheapest routing and the most adequate fleet.
To do so, we split the problem into two interdependent components: a fleet
design component and a routing component. First, we define two Mixed Integer
Programming (MIP) formulations for each component. Then we discuss several
improvements in the form of valid cuts and symmetry breaking constraints.
The main contribution of this paper is a comparison of the four resulting
models for this Rich VRP. We highlight their strengths and weaknesses with
extensive experiments.
Finally, we explore a lightweight integration with Constraint Programming
(CP). We use a fast CP model which gives good solutions and use the solution to
warm-start our models.
| [
{
"version": "v1",
"created": "Tue, 6 Dec 2016 07:46:41 GMT"
}
] | 1,481,068,800,000 | [
[
"Mahéo",
"Arthur",
""
],
[
"Urli",
"Tommaso",
""
],
[
"Kilby",
"Philip",
""
]
] |
1612.01857 | Alexa Gopaulsingh Mrs. | Alexa Gopaulsingh | On a Well-behaved Relational Generalisation of Rough Set Approximations | 12 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine non-dual relational extensions of rough set approximations and
find an extension which satisfies surprisingly many of the usual rough set
properties. We then use this definition to give an explanation for an
observation made by Samanta and Chakraborty in their recent paper [P. Samanta
and M.K. Chakraborty. Interface of rough set systems and modal logics: A
survey. Transactions on Rough Sets XIX, pages 114-137, 2015].
| [
{
"version": "v1",
"created": "Mon, 5 Dec 2016 08:53:16 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Dec 2016 15:04:20 GMT"
}
] | 1,481,155,200,000 | [
[
"Gopaulsingh",
"Alexa",
""
]
] |
1612.01941 | Paolo Dragone | Stefano Teso and Paolo Dragone and Andrea Passerini | Coactive Critiquing: Elicitation of Preferences and Features | AAAI'17 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When faced with complex choices, users refine their own preference criteria
as they explore the catalogue of options. In this paper we propose an approach
to preference elicitation suited for this scenario. We extend Coactive
Learning, which iteratively collects manipulative feedback, to optionally query
example critiques. User critiques are integrated into the learning model by
dynamically extending the feature space. Our formulation natively supports
constructive learning tasks, where the option catalogue is generated
on-the-fly. We present an upper bound on the average regret suffered by the
learner. Our empirical analysis highlights the promise of our approach.
| [
{
"version": "v1",
"created": "Tue, 6 Dec 2016 18:32:40 GMT"
}
] | 1,481,068,800,000 | [
[
"Teso",
"Stefano",
""
],
[
"Dragone",
"Paolo",
""
],
[
"Passerini",
"Andrea",
""
]
] |
1612.02088 | Shuai Ma | Shuai Ma and Jia Yuan Yu | Transition-based versus State-based Reward Functions for MDPs with
Value-at-Risk | 55th Annual Allerton Conference on Communication, Control, and
Computing (Allerton) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In reinforcement learning, the reward function on current state and action is
widely used. When the objective is about the expectation of the (discounted)
total reward only, it works perfectly. However, if the objective involves the
total reward distribution, the result will be wrong. This paper studies
Value-at-Risk (VaR) problems in short- and long-horizon Markov decision
processes (MDPs) with two reward functions, which share the same expectations.
Firstly we show that with VaR objective, when the real reward function is
transition-based (with respect to action and both current and next states), the
simplified (state-based, with respect to action and current state only) reward
function will change the VaR. Secondly, for long-horizon MDPs, we estimate the
VaR function with the aid of spectral theory and the central limit theorem.
Thirdly, since the estimation method is for a Markov reward process with the
reward function on current state only, we present a transformation algorithm
for the Markov reward process with the reward function on current and next
states, in order to estimate the VaR function with an intact total reward
distribution.
| [
{
"version": "v1",
"created": "Wed, 7 Dec 2016 01:17:26 GMT"
},
{
"version": "v2",
"created": "Sat, 10 Dec 2016 16:32:47 GMT"
},
{
"version": "v3",
"created": "Mon, 27 Feb 2017 23:50:15 GMT"
},
{
"version": "v4",
"created": "Thu, 29 Nov 2018 22:50:03 GMT"
}
] | 1,543,795,200,000 | [
[
"Ma",
"Shuai",
""
],
[
"Yu",
"Jia Yuan",
""
]
] |
1612.02255 | Armando Vieira | Armando Vieira | Knowledge Representation in Graphs using Convolutional Neural Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Graphs (KG) constitute a flexible representation of complex
relationships between entities particularly useful for biomedical data. These
KG, however, are very sparse with many missing edges (facts) and the
visualisation of the mesh of interactions nontrivial. Here we apply a
compositional model to embed nodes and relationships into a vectorised semantic
space to perform graph completion. A visualisation tool based on Convolutional
Neural Networks and Self-Organised Maps (SOM) is proposed to extract high-level
insights from the KG. We apply this technique to a subset of CTD, containing
interactions of compounds with human genes / proteins and show that the
performance is comparable to the one obtained by structural models.
| [
{
"version": "v1",
"created": "Wed, 7 Dec 2016 14:10:56 GMT"
}
] | 1,481,155,200,000 | [
[
"Vieira",
"Armando",
""
]
] |
1612.02587 | Juerg Kohlas | Juerg Kohlas | Inverses, Conditionals and Compositional Operators in Separative
Valuation Algebra | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Compositional models were introduce by Jirousek and Shenoy in the general
framework of valuation-based systems. They based their theory on an axiomatic
system of valuations involving not only the operations of combination and
marginalisation, but also of removal. They claimed that this systems covers
besides the classical case of discrete probability distributions, also the
cases of Gaussian densities and belief functions, and many other systems.
Whereas their results on the compositional operator are correct, the
axiomatic basis is not sufficient to cover the examples claimed above. We
propose here a different axiomatic system of valuation algebras, which permits
a rigorous mathematical theory of compositional operators in valuation-based
systems and covers all the examples mentioned above. It extends the classical
theory of inverses in semigroup theory and places thereby the present theory
into its proper mathematical frame. Also this theory sheds light on the
different structures of valuation-based systems, like regular algebras
(represented by probability potentials), canncellative algebras (Gaussian
potentials) and general separative algebras (density functions).
| [
{
"version": "v1",
"created": "Thu, 8 Dec 2016 10:34:16 GMT"
}
] | 1,481,241,600,000 | [
[
"Kohlas",
"Juerg",
""
]
] |
1612.02757 | Adam Earle | Andrew M. Saxe, Adam Earle, Benjamin Rosman | Hierarchy through Composition with Linearly Solvable Markov Decision
Processes | 9 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hierarchical architectures are critical to the scalability of reinforcement
learning methods. Current hierarchical frameworks execute actions serially,
with macro-actions comprising sequences of primitive actions. We propose a
novel alternative to these control hierarchies based on concurrent execution of
many actions in parallel. Our scheme uses the concurrent compositionality
provided by the linearly solvable Markov decision process (LMDP) framework,
which naturally enables a learning agent to draw on several macro-actions
simultaneously to solve new tasks. We introduce the Multitask LMDP module,
which maintains a parallel distributed representation of tasks and may be
stacked to form deep hierarchies abstracted in space and time.
| [
{
"version": "v1",
"created": "Thu, 8 Dec 2016 18:25:31 GMT"
}
] | 1,481,241,600,000 | [
[
"Saxe",
"Andrew M.",
""
],
[
"Earle",
"Adam",
""
],
[
"Rosman",
"Benjamin",
""
]
] |
1612.02904 | Davoud Mougouei | Davoud Mougouei and David Powers | GOTM: a Goal-Oriented Framework for Capturing Uncertainty of Medical
Treatments | Idea Paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has been widely recognized that uncertainty is an inevitable aspect of
diagnosis and treatment of medical disorders. Such uncertainties hence, need to
be considered in computerized medical models. The existing medical modeling
techniques however, have mainly focused on capturing uncertainty associated
with diagnosis of medical disorders while ignoring uncertainty of treatments.
To tackle this issue, we have proposed using a fuzzy-based modeling and
description technique for capturing uncertainties in treatment plans. We have
further contributed a formal framework which allows for goal-oriented modeling
and analysis of medical treatments.
| [
{
"version": "v1",
"created": "Fri, 9 Dec 2016 04:02:34 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Oct 2020 05:51:20 GMT"
}
] | 1,603,411,200,000 | [
[
"Mougouei",
"Davoud",
""
],
[
"Powers",
"David",
""
]
] |
1612.03055 | Jessa Bekker | Jessa Bekker, Arjen Hommersom, Martijn Lappenschaar, Jesse Davis | Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian
Networks | Machine Learning for Health @ NIPS 2016 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Managing patients with multimorbidity often results in polypharmacy: the
prescription of multiple drugs. However, the long-term effects of specific
combinations of drugs and diseases are typically unknown. In particular, drugs
prescribed for one condition may result in adverse effects for the other. To
investigate which types of drugs may affect the further progression of
multimorbidity, we query models of diseases and prescriptions that are learned
from primary care data. State-of-the-art tractable Bayesian network
representations, on which such complex queries can be computed efficiently, are
employed for these large medical networks. Our results confirm that
prescriptions may lead to unintended negative consequences in further
development of multimorbidity in cardiovascular diseases. Moreover, a drug
treatment for one disease group may affect diseases of another group.
| [
{
"version": "v1",
"created": "Fri, 9 Dec 2016 15:25:03 GMT"
}
] | 1,481,500,800,000 | [
[
"Bekker",
"Jessa",
""
],
[
"Hommersom",
"Arjen",
""
],
[
"Lappenschaar",
"Martijn",
""
],
[
"Davis",
"Jesse",
""
]
] |
1612.03353 | Seiji Isotani | Judson Bandeira, Ig Ibert Bittencourt, Patricia Espinheira and Seiji
Isotani | FOCA: A Methodology for Ontology Evaluation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modeling an ontology is a hard and time-consuming task. Although
methodologies are useful for ontologists to create good ontologies, they do not
help with the task of evaluating the quality of the ontology to be reused. For
these reasons, it is imperative to evaluate the quality of the ontology after
constructing it or before reusing it. Few studies usually present only a set of
criteria and questions, but no guidelines to evaluate the ontology. The effort
to evaluate an ontology is very high as there is a huge dependence on the
evaluator's expertise to understand the criteria and questions in depth.
Moreover, the evaluation is still very subjective. This study presents a novel
methodology for ontology evaluation, taking into account three fundamental
principles: i) it is based on the Goal, Question, Metric approach for empirical
evaluation; ii) the goals of the methodologies are based on the roles of
knowledge representations combined with specific evaluation criteria; iii) each
ontology is evaluated according to the type of ontology. The methodology was
empirically evaluated using different ontologists and ontologies of the same
domain. The main contributions of this study are: i) defining a step-by-step
approach to evaluate the quality of an ontology; ii) proposing an evaluation
based on the roles of knowledge representations; iii) the explicit difference
of the evaluation according to the type of the ontology iii) a questionnaire to
evaluate the ontologies; iv) a statistical model that automatically calculates
the quality of the ontologies.
| [
{
"version": "v1",
"created": "Sat, 10 Dec 2016 22:38:42 GMT"
},
{
"version": "v2",
"created": "Sat, 2 Sep 2017 18:21:55 GMT"
}
] | 1,504,569,600,000 | [
[
"Bandeira",
"Judson",
""
],
[
"Bittencourt",
"Ig Ibert",
""
],
[
"Espinheira",
"Patricia",
""
],
[
"Isotani",
"Seiji",
""
]
] |
1612.03801 | Stig Petersen | Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus
Wainwright, Heinrich K\"uttler, Andrew Lefrancq, Simon Green, V\'ictor
Vald\'es, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max
Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis,
Shane Legg and Stig Petersen | DeepMind Lab | 11 pages, 8 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | DeepMind Lab is a first-person 3D game platform designed for research and
development of general artificial intelligence and machine learning systems.
DeepMind Lab can be used to study how autonomous artificial agents may learn
complex tasks in large, partially observed, and visually diverse worlds.
DeepMind Lab has a simple and flexible API enabling creative task-designs and
novel AI-designs to be explored and quickly iterated upon. It is powered by a
fast and widely recognised game engine, and tailored for effective use by the
research community.
| [
{
"version": "v1",
"created": "Mon, 12 Dec 2016 17:32:49 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Dec 2016 12:19:48 GMT"
}
] | 1,481,673,600,000 | [
[
"Beattie",
"Charles",
""
],
[
"Leibo",
"Joel Z.",
""
],
[
"Teplyashin",
"Denis",
""
],
[
"Ward",
"Tom",
""
],
[
"Wainwright",
"Marcus",
""
],
[
"Küttler",
"Heinrich",
""
],
[
"Lefrancq",
"Andrew",
""
],
[
"Green",
"Simon",
""
],
[
"Valdés",
"Víctor",
""
],
[
"Sadik",
"Amir",
""
],
[
"Schrittwieser",
"Julian",
""
],
[
"Anderson",
"Keith",
""
],
[
"York",
"Sarah",
""
],
[
"Cant",
"Max",
""
],
[
"Cain",
"Adam",
""
],
[
"Bolton",
"Adrian",
""
],
[
"Gaffney",
"Stephen",
""
],
[
"King",
"Helen",
""
],
[
"Hassabis",
"Demis",
""
],
[
"Legg",
"Shane",
""
],
[
"Petersen",
"Stig",
""
]
] |
1612.04469 | Kenrick Kenrick | Kenrick | Web-based Argumentation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Assumption-Based Argumentation (ABA) is an argumentation framework that has
been proposed in the late 20th century. Since then, there was still no solver
implemented in a programming language which is easy to setup and no solver have
been interfaced to the web, which impedes the interests of the public. This
project aims to implement an ABA solver in a modern programming language that
performs reasonably well and interface it to the web for easier access by the
public. This project has demonstrated the novelty of development of an ABA
solver, that computes conflict-free, stable, admissible, grounded, ideal, and
complete semantics, in Python programming language which can be used via an
easy-to-use web interface for visualization of the argument and dispute trees.
Experiments were conducted to determine the project's best configurations and
to compare this project with proxdd, a state-of-the-art ABA solver, which has
no web interface and computes less number of semantics. From the results of the
experiments, this project's best configuration is achieved by utilizing
"pickle" technique and tree caching technique. Using this project's best
configuration, this project achieved a lower average runtime compared to
proxdd. On other aspect, this project encountered more cases with exceptions
compared to proxdd, which might be caused by this project computing more
semantics and hence requires more resources to do so. Hence, it can be said
that this project run comparably well to the state-of-the-art ABA solver
proxdd. Future works of this project include computational complexity analysis
and efficiency analysis of algorithms implemented, implementation of more
semantics in argumentation framework, and usability testing of the web
interface.
| [
{
"version": "v1",
"created": "Wed, 14 Dec 2016 03:21:32 GMT"
}
] | 1,481,760,000,000 | [
[
"Kenrick",
"",
""
]
] |
1612.04791 | Patrick Rodler | Patrick Rodler and Wolfgang Schmid and Kostyantyn Shchekotykhin | Scalable Computation of Optimized Queries for Sequential Diagnosis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many model-based diagnosis applications it is impossible to provide such a
set of observations and/or measurements that allow to identify the real cause
of a fault. Therefore, diagnosis systems often return many possible candidates,
leaving the burden of selecting the correct diagnosis to a user. Sequential
diagnosis techniques solve this problem by automatically generating a sequence
of queries to some oracle. The answers to these queries provide additional
information necessary to gradually restrict the search space by removing
diagnosis candidates inconsistent with the answers.
During query computation, existing sequential diagnosis methods often require
the generation of many unnecessary query candidates and strongly rely on
expensive logical reasoners. We tackle this issue by devising efficient
heuristic query search methods. The proposed methods enable for the first time
a completely reasoner-free query generation while at the same time guaranteeing
optimality conditions, e.g. minimal cardinality or best understandability, of
the returned query that existing methods cannot realize. Hence, the performance
of this approach is independent of the (complexity of the) diagnosed system.
Experiments conducted using real-world problems show that the new approach is
highly scalable and outperforms existing methods by orders of magnitude.
| [
{
"version": "v1",
"created": "Wed, 14 Dec 2016 20:15:36 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Dec 2016 18:24:55 GMT"
},
{
"version": "v3",
"created": "Fri, 16 Dec 2016 17:26:02 GMT"
}
] | 1,482,105,600,000 | [
[
"Rodler",
"Patrick",
""
],
[
"Schmid",
"Wolfgang",
""
],
[
"Shchekotykhin",
"Kostyantyn",
""
]
] |
1612.04876 | Memo Akten | Memo Akten and Mick Grierson | Collaborative creativity with Monte-Carlo Tree Search and Convolutional
Neural Networks | Presented at the Constructive Machine Learning workshop at NIPS 2016
as a poster and spotlight talk. 8 pages including 2 page references, 2 page
appendix, 3 figures. Blog post (including videos) at
https://medium.com/@memoakten/collaborative-creativity-with-monte-carlo-tree-search-and-convolutional-neural-networks-and-other-69d7107385a0 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate a human-machine collaborative drawing environment in which an
autonomous agent sketches images while optionally allowing a user to directly
influence the agent's trajectory. We combine Monte Carlo Tree Search with image
classifiers and test both shallow models (e.g. multinomial logistic regression)
and deep Convolutional Neural Networks (e.g. LeNet, Inception v3). We found
that using the shallow model, the agent produces a limited variety of images,
which are noticably recogonisable by humans. However, using the deeper models,
the agent produces a more diverse range of images, and while the agent remains
very confident (99.99%) in having achieved its objective, to humans they mostly
resemble unrecognisable 'random' noise. We relate this to recent research which
also discovered that 'deep neural networks are easily fooled' \cite{Nguyen2015}
and we discuss possible solutions and future directions for the research.
| [
{
"version": "v1",
"created": "Wed, 14 Dec 2016 23:13:26 GMT"
}
] | 1,481,846,400,000 | [
[
"Akten",
"Memo",
""
],
[
"Grierson",
"Mick",
""
]
] |
1612.05028 | Oliver Kutz | Mihai Codescu, Eugen Kuksa, Oliver Kutz, Till Mossakowski, Fabian
Neuhaus | Ontohub: A semantic repository for heterogeneous ontologies | Preprint, journal special issue | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ontohub is a repository engine for managing distributed heterogeneous
ontologies. The distributed nature enables communities to share and exchange
their contributions easily. The heterogeneous nature makes it possible to
integrate ontologies written in various ontology languages. Ontohub supports a
wide range of formal logical and ontology languages, as well as various
structuring and modularity constructs and inter-theory (concept) mappings,
building on the OMG-standardized DOL language. Ontohub repositories are
organised as Git repositories, thus inheriting all features of this popular
version control system. Moreover, Ontohub is the first repository engine
meeting a substantial amount of the requirements formulated in the context of
the Open Ontology Repository (OOR) initiative, including an API for federation
as well as support for logical inference and axiom selection.
| [
{
"version": "v1",
"created": "Thu, 15 Dec 2016 11:48:13 GMT"
}
] | 1,481,846,400,000 | [
[
"Codescu",
"Mihai",
""
],
[
"Kuksa",
"Eugen",
""
],
[
"Kutz",
"Oliver",
""
],
[
"Mossakowski",
"Till",
""
],
[
"Neuhaus",
"Fabian",
""
]
] |
1612.05497 | Wen Jiang | Wen Jiang | A correlation coefficient of belief functions | 19 pages, 1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How to manage conflict is still an open issue in Dempster-Shafer evidence
theory. The correlation coefficient can be used to measure the similarity of
evidence in Dempster-Shafer evidence theory. However, existing correlation
coefficients of belief functions have some shortcomings. In this paper, a new
correlation coefficient is proposed with many desirable properties. One of its
applications is to measure the conflict degree among belief functions. Some
numerical examples and comparisons demonstrate the effectiveness of the
correlation coefficient.
| [
{
"version": "v1",
"created": "Fri, 16 Dec 2016 14:58:17 GMT"
},
{
"version": "v2",
"created": "Thu, 2 Feb 2017 03:29:42 GMT"
}
] | 1,486,080,000,000 | [
[
"Jiang",
"Wen",
""
]
] |
1612.06528 | Sarmimala Saikia | Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff,
Puneet Agarwal, Richa Rawat | Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs | 9 pages, 7 figures, Cognitive Computation: Integrating Neural and
Symbolic Approaches (Workshop at 30th Conference on Neural Information
Processing Systems (NIPS 2016), Barcelona, Spain.),
http://daselab.cs.wright.edu/nesy/CoCo2016/coco_nips_2016_pre-proceedings.pdf
(page 78-86). arXiv admin note: substantial text overlap with
arXiv:1608.01093 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate solving discrete optimisation problems using the estimation of
distribution (EDA) approach via a novel combination of deep belief
networks(DBN) and inductive logic programming (ILP).While DBNs are used to
learn the structure of successively better feasible solutions,ILP enables the
incorporation of domain-based background knowledge related to the goodness of
solutions.Recent work showed that ILP could be an effective way to use domain
knowledge in an EDA scenario.However,in a purely ILP-based EDA,sampling
successive populations is either inefficient or not straightforward.In our
Neuro-symbolic EDA,an ILP engine is used to construct a model for good
solutions using domain-based background knowledge.These rules are introduced as
Boolean features in the last hidden layer of DBNs used for EDA-based
optimization.This incorporation of logical ILP features requires some changes
while training and sampling from DBNs: (a)our DBNs need to be trained with data
for units at the input layer as well as some units in an otherwise hidden
layer, and (b)we would like the samples generated to be drawn from instances
entailed by the logical model.We demonstrate the viability of our approach on
instances of two optimisation problems: predicting optimal depth-of-win for the
KRK endgame,and jobshop scheduling.Our results are promising: (i)On each
iteration of distribution estimation,samples obtained with an ILP-assisted DBN
have a substantially greater proportion of good solutions than samples
generated using a DBN without ILP features, and (ii)On termination of
distribution estimation,samples obtained using an ILP-assisted DBN contain more
near-optimal samples than samples from a DBN without ILP features.These results
suggest that the use of ILP-constructed theories could be useful for
incorporating complex domain-knowledge into deep models for estimation of
distribution based procedures.
| [
{
"version": "v1",
"created": "Tue, 20 Dec 2016 06:56:12 GMT"
}
] | 1,483,228,800,000 | [
[
"Saikia",
"Sarmimala",
""
],
[
"Vig",
"Lovekesh",
""
],
[
"Srinivasan",
"Ashwin",
""
],
[
"Shroff",
"Gautam",
""
],
[
"Agarwal",
"Puneet",
""
],
[
"Rawat",
"Richa",
""
]
] |
1612.06915 | Neil Burch | Neil Burch, Martin Schmid, Matej Morav\v{c}\'ik, Michael Bowling | AIVAT: A New Variance Reduction Technique for Agent Evaluation in
Imperfect Information Games | To appear at AAAI-17 Workshop on Computer Poker and Imperfect
Information Games | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evaluating agent performance when outcomes are stochastic and agents use
randomized strategies can be challenging when there is limited data available.
The variance of sampled outcomes may make the simple approach of Monte Carlo
sampling inadequate. This is the case for agents playing heads-up no-limit
Texas hold'em poker, where man-machine competitions have involved multiple days
of consistent play and still not resulted in statistically significant
conclusions even when the winner's margin is substantial. In this paper, we
introduce AIVAT, a low variance, provably unbiased value assessment tool that
uses an arbitrary heuristic estimate of state value, as well as the explicit
strategy of a subset of the agents. Unlike existing techniques which reduce the
variance from chance events, or only consider game ending actions, AIVAT
reduces the variance both from choices by nature and by players with a known
strategy. The resulting estimator in no-limit poker can reduce the number of
hands needed to draw statistical conclusions by more than a factor of 10.
| [
{
"version": "v1",
"created": "Tue, 20 Dec 2016 23:09:40 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Jan 2017 21:22:12 GMT"
}
] | 1,485,129,600,000 | [
[
"Burch",
"Neil",
""
],
[
"Schmid",
"Martin",
""
],
[
"Moravčík",
"Matej",
""
],
[
"Bowling",
"Michael",
""
]
] |
1612.07555 | J. G. Wolff | J Gerard Wolff | The SP Theory of Intelligence as a Foundation for the Development of a
General, Human-Level Thinking Machine | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper summarises how the "SP theory of intelligence" and its realisation
in the "SP computer model" simplifies and integrates concepts across artificial
intelligence and related areas, and thus provides a promising foundation for
the development of a general, human-level thinking machine, in accordance with
the main goal of research in artificial general intelligence.
The key to this simplification and integration is the powerful concept of
"multiple alignment", borrowed and adapted from bioinformatics. This concept
has the potential to be the "double helix" of intelligence, with as much
significance for human-level intelligence as has DNA for biological sciences.
Strengths of the SP system include: versatility in the representation of
diverse kinds of knowledge; versatility in aspects of intelligence (including:
strengths in unsupervised learning; the processing of natural language; pattern
recognition at multiple levels of abstraction that is robust in the face of
errors in data; several kinds of reasoning (including: one-step `deductive'
reasoning; chains of reasoning; abductive reasoning; reasoning with
probabilistic networks and trees; reasoning with 'rules'; nonmonotonic
reasoning and reasoning with default values; Bayesian reasoning with
'explaining away'; and more); planning; problem solving; and more); seamless
integration of diverse kinds of knowledge and diverse aspects of intelligence
in any combination; and potential for application in several areas (including:
helping to solve nine problems with big data; helping to develop human-level
intelligence in autonomous robots; serving as a database with intelligence and
with versatility in the representation and integration of several forms of
knowledge; serving as a vehicle for medical knowledge and as an aid to medical
diagnosis; and several more).
| [
{
"version": "v1",
"created": "Thu, 22 Dec 2016 11:50:47 GMT"
}
] | 1,482,451,200,000 | [
[
"Wolff",
"J Gerard",
""
]
] |
1612.07589 | Wolfgang Faber | Wolfgang Faber, Mauro Vallati, Federico Cerutti, Massimiliano Giacomin | Solving Set Optimization Problems by Cardinality Optimization via Weak
Constraints with an Application to Argumentation | Informal proceedings of the 1st Workshop on Trends and Applications
of Answer Set Programming (TAASP 2016), Klagenfurt, Austria, 26 September
2016 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimization - minimization or maximization - in the lattice of subsets is a
frequent operation in Artificial Intelligence tasks. Examples are
subset-minimal model-based diagnosis, nonmonotonic reasoning by means of
circumscription, or preferred extensions in abstract argumentation. Finding the
optimum among many admissible solutions is often harder than finding admissible
solutions with respect to both computational complexity and methodology. This
paper addresses the former issue by means of an effective method for finding
subset-optimal solutions. It is based on the relationship between
cardinality-optimal and subset-optimal solutions, and the fact that many
logic-based declarative programming systems provide constructs for finding
cardinality-optimal solutions, for example maximum satisfiability (MaxSAT) or
weak constraints in Answer Set Programming (ASP). Clearly each
cardinality-optimal solution is also a subset-optimal one, and if the language
also allows for the addition of particular restricting constructs (both MaxSAT
and ASP do) then all subset-optimal solutions can be found by an iterative
computation of cardinality-optimal solutions. As a showcase, the computation of
preferred extensions of abstract argumentation frameworks using the proposed
method is studied.
| [
{
"version": "v1",
"created": "Thu, 22 Dec 2016 13:20:02 GMT"
}
] | 1,482,451,200,000 | [
[
"Faber",
"Wolfgang",
""
],
[
"Vallati",
"Mauro",
""
],
[
"Cerutti",
"Federico",
""
],
[
"Giacomin",
"Massimiliano",
""
]
] |
1612.08657 | Olivier Auber | Antoine Saillenfest, Jean-Louis Dessalles, Olivier Auber | Role of Simplicity in Creative Behaviour: The Case of the Poietic
Generator | This study was supported by grants from the programme Futur&Ruptures
and from the 'Chaire Modelisation des Imaginaires, Innovation et Creation',
http://www.computationalcreativity.net/iccc2016/posters-and-demos/ | Proceedings of the Seventh International Conference on
Computational Creativity (ICCC-2016). Paris, France | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose to apply Simplicity Theory (ST) to model interest in creative
situations. ST has been designed to describe and predict interest in
communication. Here we use ST to derive a decision rule that we apply to a
simplified version of a creative game, the Poietic Generator. The decision rule
produces what can be regarded as an elementary form of creativity. This study
is meant as a proof of principle. It suggests that some creative actions may be
motivated by the search for unexpected simplicity.
| [
{
"version": "v1",
"created": "Thu, 22 Dec 2016 12:56:07 GMT"
}
] | 1,482,883,200,000 | [
[
"Saillenfest",
"Antoine",
""
],
[
"Dessalles",
"Jean-Louis",
""
],
[
"Auber",
"Olivier",
""
]
] |
1612.08777 | Joshua Friedman | Joshua S. Friedman | Automated timetabling for small colleges and high schools using huge
integer programs | Errors corrected from version 1 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We formulate an integer program to solve a highly constrained academic
timetabling problem at the United States Merchant Marine Academy. The IP
instance that results from our real case study has approximately both 170,000
rows and columns and solves to optimality in 4--24 hours using a commercial
solver on a portable computer (near optimal feasible solutions were often found
in 4--12 hours). Our model is applicable to both high schools and small
colleges who wish to deviate from group scheduling. We also solve a necessary
preprocessing student subgrouping problem, which breaks up big groups of
students into small groups so they can optimally fit into small capacity
classes.
| [
{
"version": "v1",
"created": "Wed, 28 Dec 2016 00:50:16 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Jan 2017 18:24:48 GMT"
}
] | 1,483,488,000,000 | [
[
"Friedman",
"Joshua S.",
""
]
] |
1612.09212 | Rouven Bauer | Rouven Bauer | A hybrid approach to supervised machine learning for algorithmic melody
composition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we present an algorithm for composing monophonic melodies
similar in style to those of a given, phrase annotated, sample of melodies. For
implementation, a hybrid approach incorporating parametric Markov models of
higher order and a contour concept of phrases is used. This work is based on
the master thesis of Thayabaran Kathiresan (2015). An online listening test
conducted shows that enhancing a pure Markov model with musically relevant
context, like count and planed melody contour, improves the result
significantly.
| [
{
"version": "v1",
"created": "Thu, 29 Dec 2016 17:36:05 GMT"
}
] | 1,483,056,000,000 | [
[
"Bauer",
"Rouven",
""
]
] |
1612.09591 | Matthias Nickles | Matthias Nickles | PrASP Report | Technical Report | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This technical report describes the usage, syntax, semantics and core
algorithms of the probabilistic inductive logic programming framework PrASP.
PrASP is a research software which integrates non-monotonic reasoning based on
Answer Set Programming (ASP), probabilistic inference and parameter learning.
In contrast to traditional approaches to Probabilistic (Inductive) Logic
Programming, our framework imposes only little restrictions on probabilistic
logic programs. In particular, PrASP allows for ASP as well as First-Order
Logic syntax, and for the annotation of formulas with point probabilities as
well as interval probabilities. A range of widely configurable inference
algorithms can be combined in a pipeline-like fashion, in order to cover a
variety of use cases.
| [
{
"version": "v1",
"created": "Fri, 30 Dec 2016 20:45:28 GMT"
}
] | 1,483,315,200,000 | [
[
"Nickles",
"Matthias",
""
]
] |
1612.09593 | Hamid Reza Hassanzadeh | Hamid Reza Hassanzadeh, Hadi Sadoghi Yazdi, Abedin Vahedian | Fuzzy Constraints Linear Discriminant Analysis | null | 3rd Iranian Joint Congress on Intelligent Systems and Fuzzy
Systems, 2009 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we introduce a fuzzy constraint linear discriminant analysis
(FC-LDA). The FC-LDA tries to minimize misclassification error based on
modified perceptron criterion that benefits handling the uncertainty near the
decision boundary by means of a fuzzy linear programming approach with fuzzy
resources. The method proposed has low computational complexity because of its
linear characteristics and the ability to deal with noisy data with different
degrees of tolerance. Obtained results verify the success of the algorithm when
dealing with different problems. Comparing FC-LDA and LDA shows superiority in
classification task.
| [
{
"version": "v1",
"created": "Fri, 30 Dec 2016 20:48:33 GMT"
}
] | 1,483,315,200,000 | [
[
"Hassanzadeh",
"Hamid Reza",
""
],
[
"Yazdi",
"Hadi Sadoghi",
""
],
[
"Vahedian",
"Abedin",
""
]
] |
1701.00287 | Caelan Garrett | Caelan Reed Garrett, Tom\'as Lozano-P\'erez, and Leslie Pack Kaelbling | STRIPS Planning in Infinite Domains | 11 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many robotic planning applications involve continuous actions with highly
non-linear constraints, which cannot be modeled using modern planners that
construct a propositional representation. We introduce STRIPStream: an
extension of the STRIPS language which can model these domains by supporting
the specification of blackbox generators to handle complex constraints. The
outputs of these generators interact with actions through possibly infinite
streams of objects and static predicates. We provide two algorithms which both
reduce STRIPStream problems to a sequence of finite-domain planning problems.
The representation and algorithms are entirely domain independent. We
demonstrate our framework on simple illustrative domains, and then on a
high-dimensional, continuous robotic task and motion planning domain.
| [
{
"version": "v1",
"created": "Sun, 1 Jan 2017 20:37:51 GMT"
},
{
"version": "v2",
"created": "Sun, 28 May 2017 01:08:00 GMT"
}
] | 1,496,102,400,000 | [
[
"Garrett",
"Caelan Reed",
""
],
[
"Lozano-Pérez",
"Tomás",
""
],
[
"Kaelbling",
"Leslie Pack",
""
]
] |
1701.00349 | Rohitash Chandra | Rohitash Chandra | An affective computational model for machine consciousness | under review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the past, several models of consciousness have become popular and have led
to the development of models for machine consciousness with varying degrees of
success and challenges for simulation and implementations. Moreover, affective
computing attributes that involve emotions, behavior and personality have not
been the focus of models of consciousness as they lacked motivation for
deployment in software applications and robots. The affective attributes are
important factors for the future of machine consciousness with the rise of
technologies that can assist humans. Personality and affection hence can give
an additional flavor for the computational model of consciousness in humanoid
robotics. Recent advances in areas of machine learning with a focus on deep
learning can further help in developing aspects of machine consciousness in
areas that can better replicate human sensory perceptions such as speech
recognition and vision. With such advancements, one encounters further
challenges in developing models that can synchronize different aspects of
affective computing. In this paper, we review some existing models of
consciousnesses and present an affective computational model that would enable
the human touch and feel for robotic systems.
| [
{
"version": "v1",
"created": "Mon, 2 Jan 2017 09:48:47 GMT"
}
] | 1,483,401,600,000 | [
[
"Chandra",
"Rohitash",
""
]
] |
1701.00464 | Antonio Lieto | Antonio Lieto, Antonio Chella, Marcello Frixione | Conceptual Spaces for Cognitive Architectures: A Lingua Franca for
Different Levels of Representation | 31 pages, 3 figures in Biologically Inspired Cognitive Architectures,
2017 | null | 10.1016/j.bica.2016.10.005 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During the last decades, many cognitive architectures (CAs) have been
realized adopting different assumptions about the organization and the
representation of their knowledge level. Some of them (e.g. SOAR [Laird
(2012)]) adopt a classical symbolic approach, some (e.g. LEABRA [O'Reilly and
Munakata (2000)]) are based on a purely connectionist model, while others (e.g.
CLARION [Sun (2006)] adopt a hybrid approach combining connectionist and
symbolic representational levels. Additionally, some attempts (e.g. biSOAR)
trying to extend the representational capacities of CAs by integrating
diagrammatical representations and reasoning are also available [Kurup and
Chandrasekaran (2007)]. In this paper we propose a reflection on the role that
Conceptual Spaces, a framework developed by Peter G\"ardenfors [G\"ardenfors
(2000)] more than fifteen years ago, can play in the current development of the
Knowledge Level in Cognitive Systems and Architectures. In particular, we claim
that Conceptual Spaces offer a lingua franca that allows to unify and
generalize many aspects of the symbolic, sub-symbolic and diagrammatic
approaches (by overcoming some of their typical problems) and to integrate them
on a common ground. In doing so we extend and detail some of the arguments
explored by G\"ardenfors [G\"ardenfors (1997)] for defending the need of a
conceptual, intermediate, representation level between the symbolic and the
sub-symbolic one.
| [
{
"version": "v1",
"created": "Mon, 2 Jan 2017 17:35:34 GMT"
}
] | 1,483,401,600,000 | [
[
"Lieto",
"Antonio",
""
],
[
"Chella",
"Antonio",
""
],
[
"Frixione",
"Marcello",
""
]
] |
1701.00642 | Paul Weng | Dajian Li and Paul Weng and Orkun Karabasoglu | Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs | accepted at MIWAI 2017 | null | 10.1007/978-3-319-49397-8_9 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we tackle the problem of risk-averse route planning in a
transportation network with time-dependent and stochastic costs. To solve this
problem, we propose an adaptation of the A* algorithm that accommodates any
risk measure or decision criterion that is monotonic with first-order
stochastic dominance. We also present a case study of our algorithm on the
Manhattan, NYC, transportation network.
| [
{
"version": "v1",
"created": "Tue, 3 Jan 2017 10:47:35 GMT"
}
] | 1,483,488,000,000 | [
[
"Li",
"Dajian",
""
],
[
"Weng",
"Paul",
""
],
[
"Karabasoglu",
"Orkun",
""
]
] |
1701.00646 | Paul Weng | Paul Weng | From Preference-Based to Multiobjective Sequential Decision-Making | accepted at MIWAI 2017 | null | 10.1007/978-3-319-49397-8_20 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a link between preference-based and multiobjective
sequential decision-making. While transforming a multiobjective problem to a
preference-based one is quite natural, the other direction is a bit less
obvious. We present how this transformation (from preference-based to
multiobjective) can be done under the classic condition that preferences over
histories can be represented by additively decomposable utilities and that the
decision criterion to evaluate policies in a state is based on expectation.
This link yields a new source of multiobjective sequential decision-making
problems (i.e., when reward values are unknown) and justifies the use of
solving methods developed in one setting in the other one.
| [
{
"version": "v1",
"created": "Tue, 3 Jan 2017 10:57:06 GMT"
}
] | 1,483,488,000,000 | [
[
"Weng",
"Paul",
""
]
] |
1701.00833 | Tshilidzi Marwala | I. Boulkaibet, T. Marwala, M.I. Friswell, H. Haddad Khodaparast and S.
Adhikari | Fuzzy finite element model updating using metaheuristic optimization
algorithms | This article was accepted by the 2017 International Modal Analysis
Conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a non-probabilistic method based on fuzzy logic is used to
update finite element models (FEMs). Model updating techniques use the measured
data to improve the accuracy of numerical models of structures. However, the
measured data are contaminated with experimental noise and the models are
inaccurate due to randomness in the parameters. This kind of aleatory
uncertainty is irreducible, and may decrease the accuracy of the finite element
model updating process. However, uncertainty quantification methods can be used
to identify the uncertainty in the updating parameters. In this paper, the
uncertainties associated with the modal parameters are defined as fuzzy
membership functions, while the model updating procedure is defined as an
optimization problem at each {\alpha}-cut level. To determine the membership
functions of the updated parameters, an objective function is defined and
minimized using two metaheuristic optimization algorithms: ant colony
optimization (ACO) and particle swarm optimization (PSO). A structural example
is used to investigate the accuracy of the fuzzy model updating strategy using
the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy
finite element model updating are compared with the Bayesian model updating
results.
| [
{
"version": "v1",
"created": "Tue, 3 Jan 2017 20:58:55 GMT"
}
] | 1,483,574,400,000 | [
[
"Boulkaibet",
"I.",
""
],
[
"Marwala",
"T.",
""
],
[
"Friswell",
"M. I.",
""
],
[
"Khodaparast",
"H. Haddad",
""
],
[
"Adhikari",
"S.",
""
]
] |
1701.00867 | Abhishek Mishra | Nithyanand Kota, Abhishek Mishra, Sunil Srinivasa, Xi (Peter) Chen,
Pieter Abbeel | A K-fold Method for Baseline Estimation in Policy Gradient Algorithms | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The high variance issue in unbiased policy-gradient methods such as VPG and
REINFORCE is typically mitigated by adding a baseline. However, the baseline
fitting itself suffers from the underfitting or the overfitting problem. In
this paper, we develop a K-fold method for baseline estimation in policy
gradient algorithms. The parameter K is the baseline estimation hyperparameter
that can adjust the bias-variance trade-off in the baseline estimates. We
demonstrate the usefulness of our approach via two state-of-the-art policy
gradient algorithms on three MuJoCo locomotive control tasks.
| [
{
"version": "v1",
"created": "Tue, 3 Jan 2017 23:29:04 GMT"
}
] | 1,483,574,400,000 | [
[
"Kota",
"Nithyanand",
"",
"Peter"
],
[
"Mishra",
"Abhishek",
"",
"Peter"
],
[
"Srinivasa",
"Sunil",
"",
"Peter"
],
[
"Xi",
"",
"",
"Peter"
],
[
"Chen",
"",
""
],
[
"Abbeel",
"Pieter",
""
]
] |
1701.01048 | Roni Khardon | Roni Khardon and Scott Sanner | Stochastic Planning and Lifted Inference | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming
for lifted stochastic planning (Boutilier et al, 2001) were introduced around
the same time as algorithmic efforts to use abstraction in stochastic systems.
Over the years, these ideas evolved into two distinct lines of research, each
supported by a rich literature. Lifted probabilistic inference focused on
efficient arithmetic operations on template-based graphical models under a
finite domain assumption while symbolic dynamic programming focused on
supporting sequential decision-making in rich quantified logical action models
and on open domain reasoning. Given their common motivation but different focal
points, both lines of research have yielded highly complementary innovations.
In this chapter, we aim to help close the gap between these two research areas
by providing an overview of lifted stochastic planning from the perspective of
probabilistic inference, showing strong connections to other chapters in this
book. This also allows us to define Generalized Lifted Inference as a paradigm
that unifies these areas and elucidates open problems for future research that
can benefit both lifted inference and stochastic planning.
| [
{
"version": "v1",
"created": "Wed, 4 Jan 2017 15:37:29 GMT"
}
] | 1,483,574,400,000 | [
[
"Khardon",
"Roni",
""
],
[
"Sanner",
"Scott",
""
]
] |
1701.01724 | Michael Bowling | Matej Morav\v{c}\'ik, Martin Schmid, Neil Burch, Viliam Lis\'y, Dustin
Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael
Bowling | DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker | null | null | 10.1126/science.aam6960 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence has seen several breakthroughs in recent years, with
games often serving as milestones. A common feature of these games is that
players have perfect information. Poker is the quintessential game of imperfect
information, and a longstanding challenge problem in artificial intelligence.
We introduce DeepStack, an algorithm for imperfect information settings. It
combines recursive reasoning to handle information asymmetry, decomposition to
focus computation on the relevant decision, and a form of intuition that is
automatically learned from self-play using deep learning. In a study involving
44,000 hands of poker, DeepStack defeated with statistical significance
professional poker players in heads-up no-limit Texas hold'em. The approach is
theoretically sound and is shown to produce more difficult to exploit
strategies than prior approaches.
| [
{
"version": "v1",
"created": "Fri, 6 Jan 2017 18:56:49 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Jan 2017 04:35:28 GMT"
},
{
"version": "v3",
"created": "Fri, 3 Mar 2017 21:17:05 GMT"
}
] | 1,488,844,800,000 | [
[
"Moravčík",
"Matej",
""
],
[
"Schmid",
"Martin",
""
],
[
"Burch",
"Neil",
""
],
[
"Lisý",
"Viliam",
""
],
[
"Morrill",
"Dustin",
""
],
[
"Bard",
"Nolan",
""
],
[
"Davis",
"Trevor",
""
],
[
"Waugh",
"Kevin",
""
],
[
"Johanson",
"Michael",
""
],
[
"Bowling",
"Michael",
""
]
] |
1701.02388 | Gabriel Murray | Gabriel Murray | Stoic Ethics for Artificial Agents | Final accepted version submitted to Canadian A.I. 2017 conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a position paper advocating the notion that Stoic philosophy and
ethics can inform the development of ethical A.I. systems. This is in sharp
contrast to most work on building ethical A.I., which has focused on
Utilitarian or Deontological ethical theories. We relate ethical A.I. to
several core Stoic notions, including the dichotomy of control, the four
cardinal virtues, the ideal Sage, Stoic practices, and Stoic perspectives on
emotion or affect. More generally, we put forward an ethical view of A.I. that
focuses more on internal states of the artificial agent rather than on external
actions of the agent. We provide examples relating to near-term A.I. systems as
well as hypothetical superintelligent agents.
| [
{
"version": "v1",
"created": "Mon, 9 Jan 2017 23:25:43 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Mar 2017 23:59:25 GMT"
}
] | 1,490,832,000,000 | [
[
"Murray",
"Gabriel",
""
]
] |
1701.02543 | Junbo Zhang | Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi, Tianrui Li | Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual
Networks | 21 pages, 16 figures. arXiv admin note: substantial text overlap with
arXiv:1610.00081 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Forecasting the flow of crowds is of great importance to traffic management
and public safety, and very challenging as it is affected by many complex
factors, including spatial dependencies (nearby and distant), temporal
dependencies (closeness, period, trend), and external conditions (e.g., weather
and events). We propose a deep-learning-based approach, called ST-ResNet, to
collectively forecast two types of crowd flows (i.e. inflow and outflow) in
each and every region of a city. We design an end-to-end structure of ST-ResNet
based on unique properties of spatio-temporal data. More specifically, we
employ the residual neural network framework to model the temporal closeness,
period, and trend properties of crowd traffic. For each property, we design a
branch of residual convolutional units, each of which models the spatial
properties of crowd traffic. ST-ResNet learns to dynamically aggregate the
output of the three residual neural networks based on data, assigning different
weights to different branches and regions. The aggregation is further combined
with external factors, such as weather and day of the week, to predict the
final traffic of crowds in each and every region. We have developed a real-time
system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd
flow monitoring and forecasting in Guiyang City of China. In addition, we
present an extensive experimental evaluation using two types of crowd flows in
Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known
baselines.
| [
{
"version": "v1",
"created": "Tue, 10 Jan 2017 12:12:39 GMT"
}
] | 1,484,092,800,000 | [
[
"Zhang",
"Junbo",
""
],
[
"Zheng",
"Yu",
""
],
[
"Qi",
"Dekang",
""
],
[
"Li",
"Ruiyuan",
""
],
[
"Yi",
"Xiuwen",
""
],
[
"Li",
"Tianrui",
""
]
] |
1701.02545 | Daniel Meana-Llori\'an | Daniel Meana-Llori\'an, Cristian Gonz\'alez Garc\'ia, B. Cristina
Pelayo G-Bustelo, Juan Manuel Cueva Lovelle, Nestor Garcia-Fernandez | IoFClime: The fuzzy logic and the Internet of Things to control indoor
temperature regarding the outdoor ambient conditions | null | null | 10.1016/j.future.2016.11.020 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Internet of Things is arriving to our homes or cities through fields
already known like Smart Homes, Smart Cities, or Smart Towns. The monitoring of
environmental conditions of cities can help to adapt the indoor locations of
the cities in order to be more comfortable for people who stay there. A way to
improve the indoor conditions is an efficient temperature control, however, it
depends on many factors like the different combinations of outdoor temperature
and humidity. Therefore, adjusting the indoor temperature is not setting a
value according to other value. There are many more factors to take into
consideration, hence the traditional logic based in binary states cannot be
used. Many problems cannot be solved with a set of binary solutions and we need
a new way of development. Fuzzy logic is able to interpret many states, more
than two states, giving to computers the capacity to react in a similar way to
people. In this paper we will propose a new approach to control the temperature
using the Internet of Things together its platforms and fuzzy logic regarding
not only the indoor temperature but also the outdoor temperature and humidity
in order to save energy and to set a more comfortable environment for their
users. Finally, we will conclude that the fuzzy approach allows us to achieve
an energy saving around 40% and thus, save money.
| [
{
"version": "v1",
"created": "Tue, 10 Jan 2017 12:15:59 GMT"
}
] | 1,484,092,800,000 | [
[
"Meana-Llorián",
"Daniel",
""
],
[
"García",
"Cristian González",
""
],
[
"G-Bustelo",
"B. Cristina Pelayo",
""
],
[
"Lovelle",
"Juan Manuel Cueva",
""
],
[
"Garcia-Fernandez",
"Nestor",
""
]
] |
1701.03000 | Athanasios Karapantelakis | Aneta Vulgarakis Feljan, Athanasios Karapantelakis, Leonid Mokrushin,
Hongxin Liang, Rafia Inam, Elena Fersman, Carlos R.B. Azevedo, Klaus Raizer,
Ricardo S. Souza | A Framework for Knowledge Management and Automated Reasoning Applied on
Intelligent Transport Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cyber-Physical Systems in general, and Intelligent Transport Systems (ITS) in
particular use heterogeneous data sources combined with problem solving
expertise in order to make critical decisions that may lead to some form of
actions e.g., driver notifications, change of traffic light signals and braking
to prevent an accident. Currently, a major part of the decision process is done
by human domain experts, which is time-consuming, tedious and error-prone.
Additionally, due to the intrinsic nature of knowledge possession this decision
process cannot be easily replicated or reused. Therefore, there is a need for
automating the reasoning processes by providing computational systems a formal
representation of the domain knowledge and a set of methods to process that
knowledge. In this paper, we propose a knowledge model that can be used to
express both declarative knowledge about the systems' components, their
relations and their current state, as well as procedural knowledge representing
possible system behavior. In addition, we introduce a framework for knowledge
management and automated reasoning (KMARF). The idea behind KMARF is to
automatically select an appropriate problem solver based on formalized
reasoning expertise in the knowledge base, and convert a problem definition to
the corresponding format. This approach automates reasoning, thus reducing
operational costs, and enables reusability of knowledge and methods across
different domains. We illustrate the approach on a transportation planning use
case.
| [
{
"version": "v1",
"created": "Wed, 11 Jan 2017 15:03:18 GMT"
}
] | 1,484,179,200,000 | [
[
"Feljan",
"Aneta Vulgarakis",
""
],
[
"Karapantelakis",
"Athanasios",
""
],
[
"Mokrushin",
"Leonid",
""
],
[
"Liang",
"Hongxin",
""
],
[
"Inam",
"Rafia",
""
],
[
"Fersman",
"Elena",
""
],
[
"Azevedo",
"Carlos R. B.",
""
],
[
"Raizer",
"Klaus",
""
],
[
"Souza",
"Ricardo S.",
""
]
] |
1701.03037 | Yutaka Nagashima | Yutaka Nagashima | Towards Smart Proof Search for Isabelle | Accepted at AITP2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the recent progress in automatic theorem provers, proof engineers are
still suffering from the lack of powerful proof automation. In this position
paper we first report our proof strategy language based on a meta-tool
approach. Then, we propose an AI-based approach to drastically improve proof
automation for Isabelle, while identifying three major challenges we plan to
address for this objective.
| [
{
"version": "v1",
"created": "Tue, 10 Jan 2017 08:52:31 GMT"
}
] | 1,484,179,200,000 | [
[
"Nagashima",
"Yutaka",
""
]
] |
1701.03322 | Yi Zhou Dr. | Yi Zhou | From First-Order Logic to Assertional Logic | arXiv admin note: text overlap with arXiv:1603.03511 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | First-Order Logic (FOL) is widely regarded as one of the most important
foundations for knowledge representation. Nevertheless, in this paper, we argue
that FOL has several critical issues for this purpose. Instead, we propose an
alternative called assertional logic, in which all syntactic objects are
categorized as set theoretic constructs including individuals, concepts and
operators, and all kinds of knowledge are formalized by equality assertions. We
first present a primitive form of assertional logic that uses minimal assumed
knowledge and constructs. Then, we show how to extend it by definitions, which
are special kinds of knowledge, i.e., assertions. We argue that assertional
logic, although simpler, is more expressive and extensible than FOL. As a case
study, we show how assertional logic can be used to unify logic and
probability, and more building blocks in AI.
| [
{
"version": "v1",
"created": "Thu, 12 Jan 2017 12:25:42 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2017 06:09:21 GMT"
}
] | 1,493,596,800,000 | [
[
"Zhou",
"Yi",
""
]
] |
1701.03500 | Grant Molnar | Grant Molnar | A Savage-Like Axiomatization for Nonstandard Expected Utility | The alleged result of this paper is incorrect, the transfer principle
applies only to first-order statements over standard structures, but I
attempted to apply it over second-order statements as well. I believe a proof
in the same vein as the one in this paper could be developed, but much
greater care would need to be taken to respect the difference between
internal and external sets | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since Leonard Savage's epoch-making "Foundations of Statistics", Subjective
Expected Utility Theory has been the presumptive model for decision-making.
Savage provided an act-based axiomatization of standard expected utility
theory. In this article, we provide a Savage-like axiomatization of nonstandard
expected utility theory. It corresponds to a weakening of Savage's 6th axiom.
| [
{
"version": "v1",
"created": "Thu, 12 Jan 2017 20:39:03 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Jan 2017 00:27:55 GMT"
},
{
"version": "v3",
"created": "Mon, 30 Jan 2017 01:18:29 GMT"
},
{
"version": "v4",
"created": "Fri, 3 Mar 2017 16:48:00 GMT"
},
{
"version": "v5",
"created": "Sun, 22 Oct 2017 22:55:38 GMT"
},
{
"version": "v6",
"created": "Mon, 13 Nov 2017 16:32:00 GMT"
},
{
"version": "v7",
"created": "Thu, 8 Feb 2018 20:54:04 GMT"
}
] | 1,518,393,600,000 | [
[
"Molnar",
"Grant",
""
]
] |
1701.03571 | Oleksii Tyshchenko Dr | Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Viktoriia
O. Samitova | Fuzzy Clustering Data Given in the Ordinal Scale | null | I.J. Intelligent Systems and Applications, 2017, Vol. 9, No. 1,
pp. 67-74 | 10.5815/ijisa.2017.01.07 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A fuzzy clustering algorithm for multidimensional data is proposed in this
article. The data is described by vectors whose components are linguistic
variables defined in an ordinal scale. The obtained results confirm the
efficiency of the proposed approach.
| [
{
"version": "v1",
"created": "Fri, 13 Jan 2017 06:32:14 GMT"
}
] | 1,484,524,800,000 | [
[
"Hu",
"Zhengbing",
""
],
[
"Bodyanskiy",
"Yevgeniy V.",
""
],
[
"Tyshchenko",
"Oleksii K.",
""
],
[
"Samitova",
"Viktoriia O.",
""
]
] |
1701.03714 | Nir Oren | Zimi Li and Nir Oren and Simon Parsons | On the links between argumentation-based reasoning and nonmonotonic
reasoning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we investigate the links between instantiated argumentation
systems and the axioms for non-monotonic reasoning described in [9] with the
aim of characterising the nature of argument based reasoning. In doing so, we
consider two possible interpretations of the consequence relation, and describe
which axioms are met by ASPIC+ under each of these interpretations. We then
consider the links between these axioms and the rationality postulates. Our
results indicate that argument based reasoning as characterised by ASPIC+ is -
according to the axioms of [9] - non-cumulative and non-monotonic, and
therefore weaker than the weakest non-monotonic reasoning systems they
considered possible. This weakness underpins ASPIC+'s success in modelling
other reasoning systems, and we conclude by considering the relationship
between ASPIC+ and other weak logical systems.
| [
{
"version": "v1",
"created": "Fri, 13 Jan 2017 16:33:52 GMT"
}
] | 1,484,524,800,000 | [
[
"Li",
"Zimi",
""
],
[
"Oren",
"Nir",
""
],
[
"Parsons",
"Simon",
""
]
] |
1701.03868 | Steven Hansen | Steven Stenberg Hansen | Minimally Naturalistic Artificial Intelligence | Accepted into the NIPS 2016 Workshop on Machine Intelligence
(M.A.I.N.) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rapid advancement of machine learning techniques has re-energized
research into general artificial intelligence. While the idea of
domain-agnostic meta-learning is appealing, this emerging field must come to
terms with its relationship to human cognition and the statistics and structure
of the tasks humans perform. The position of this article is that only by
aligning our agents' abilities and environments with those of humans do we
stand a chance at developing general artificial intelligence (GAI). A broad
reading of the famous 'No Free Lunch' theorem is that there is no universally
optimal inductive bias or, equivalently, bias-free learning is impossible. This
follows from the fact that there are an infinite number of ways to extrapolate
data, any of which might be the one used by the data generating environment; an
inductive bias prefers some of these extrapolations to others, which lowers
performance in environments using these adversarial extrapolations. We may
posit that the optimal GAI is the one that maximally exploits the statistics of
its environment to create its inductive bias; accepting the fact that this
agent is guaranteed to be extremely sub-optimal for some alternative
environments. This trade-off appears benign when thinking about the environment
as being the physical universe, as performance on any fictive universe is
obviously irrelevant. But, we should expect a sharper inductive bias if we
further constrain our environment. Indeed, we implicitly do so by defining GAI
in terms of accomplishing that humans consider useful. One common version of
this is need the for 'common-sense reasoning', which implicitly appeals to the
statistics of physical universe as perceived by humans.
| [
{
"version": "v1",
"created": "Sat, 14 Jan 2017 01:57:31 GMT"
}
] | 1,484,611,200,000 | [
[
"Hansen",
"Steven Stenberg",
""
]
] |
1701.04569 | Timothy Ganesan PhD | T.Ganesan, P.Vasant, I.Elamvazuthi | Multiobjective Optimization of Solar Powered Irrigation System with
Fuzzy Type-2 Noise Modelling | 27 pages, 12 Figures | 2016, Emerging Research on Applied Fuzzy Sets and Intuitionistic
Fuzzy Matrices, IGI Global, 189 pages | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimization is becoming a crucial element in industrial applications
involving sustainable alternative energy systems. During the design of such
systems, the engineer/decision maker would often encounter noise factors (e.g.
solar insolation and ambient temperature fluctuations) when their system
interacts with the environment. In this chapter, the sizing and design
optimization of the solar powered irrigation system was considered. This
problem is multivariate, noisy, nonlinear and multiobjective. This design
problem was tackled by first using the Fuzzy Type II approach to model the
noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the
context of a weighted sum framework) was employed to solve this multiobjective
fuzzy design problem. This method was then used to construct the approximate
Pareto frontier as well as to identify the best solution option in a fuzzy
setting. Comprehensive analyses and discussions were performed on the generated
numerical results with respect to the implemented solution methods.
| [
{
"version": "v1",
"created": "Tue, 17 Jan 2017 08:52:48 GMT"
}
] | 1,484,697,600,000 | [
[
"Ganesan",
"T.",
""
],
[
"Vasant",
"P.",
""
],
[
"Elamvazuthi",
"I.",
""
]
] |
1701.04663 | Varun Raj Kompella | Varun Raj Kompella and Laurenz Wiskott | Intrinsically Motivated Acquisition of Modular Slow Features for
Humanoids in Continuous and Non-Stationary Environments | 8 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A compact information-rich representation of the environment, also called a
feature abstraction, can simplify a robot's task of mapping its raw sensory
inputs to useful action sequences. However, in environments that are
non-stationary and only partially observable, a single abstraction is probably
not sufficient to encode most variations. Therefore, learning multiple sets of
spatially or temporally local, modular abstractions of the inputs would be
beneficial. How can a robot learn these local abstractions without a teacher?
More specifically, how can it decide from where and when to start learning a
new abstraction? A recently proposed algorithm called Curious Dr. MISFA
addresses this problem. The algorithm is based on two underlying learning
principles called artificial curiosity and slowness. The former is used to make
the robot self-motivated to explore by rewarding itself whenever it makes
progress learning an abstraction; the later is used to update the abstraction
by extracting slowly varying components from raw sensory inputs. Curious Dr.
MISFA's application is, however, limited to discrete domains constrained by a
pre-defined state space and has design limitations that make it unstable in
certain situations. This paper presents a significant improvement that is
applicable to continuous environments, is computationally less expensive,
simpler to use with fewer hyper parameters, and stable in certain
non-stationary environments. We demonstrate the efficacy and stability of our
method in a vision-based robot simulator.
| [
{
"version": "v1",
"created": "Tue, 17 Jan 2017 13:24:37 GMT"
}
] | 1,484,697,600,000 | [
[
"Kompella",
"Varun Raj",
""
],
[
"Wiskott",
"Laurenz",
""
]
] |
1701.05059 | Abir M'Baya | Abir M 'Baya (DISP), Jannik Laval (DISP), Nejib Moalla (DISP), Yacine
Ouzrout (DISP), Abdelaziz Bouras | Ontology based system to guide internship assignment process | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Internship assignment is a complicated process for universities since it is
necessary to take into account a multiplicity of variables to establish a
compromise between companies' requirements and student competencies acquired
during the university training. These variables build up a complex relations
map that requires the formulation of an exhaustive and rigorous conceptual
scheme. In this research a domain ontological model is presented as support to
the student's decision making for opportunities of University studies level of
the University Lumiere Lyon 2 (ULL) education system. The ontology is designed
and created using methodological approach offering the possibility of improving
the progressive creation, capture and knowledge articulation. In this paper, we
draw a balance taking the demands of the companies across the capabilities of
the students. This will be done through the establishment of an ontological
model of an educational learners' profile and the internship postings which are
written in a free text and using uncontrolled vocabulary. Furthermore, we
outline the process of semantic matching which improves the quality of query
results.
| [
{
"version": "v1",
"created": "Wed, 18 Jan 2017 13:38:36 GMT"
}
] | 1,484,784,000,000 | [
[
"'Baya",
"Abir M",
"",
"DISP"
],
[
"Laval",
"Jannik",
"",
"DISP"
],
[
"Moalla",
"Nejib",
"",
"DISP"
],
[
"Ouzrout",
"Yacine",
"",
"DISP"
],
[
"Bouras",
"Abdelaziz",
""
]
] |
1701.05226 | Tarek Richard Besold | Tarek R. Besold, Artur d'Avila Garcez, Keith Stenning, Leendert van
der Torre, Michiel van Lambalgen | Reasoning in Non-Probabilistic Uncertainty: Logic Programming and
Neural-Symbolic Computing as Examples | Forthcoming with DOI 10.1007/s11023-017-9428-3 in the Special Issue
"Reasoning with Imperfect Information and Knowledge" of Minds and Machines
(2017). The final publication will be available at http://link.springer.com.
--- Changes to previous version: Fixed some typos and a broken reference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article aims to achieve two goals: to show that probability is not the
only way of dealing with uncertainty (and even more, that there are kinds of
uncertainty which are for principled reasons not addressable with probabilistic
means); and to provide evidence that logic-based methods can well support
reasoning with uncertainty. For the latter claim, two paradigmatic examples are
presented: Logic Programming with Kleene semantics for modelling reasoning from
information in a discourse, to an interpretation of the state of affairs of the
intended model, and a neural-symbolic implementation of Input/Output logic for
dealing with uncertainty in dynamic normative contexts.
| [
{
"version": "v1",
"created": "Wed, 18 Jan 2017 20:38:55 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2017 15:36:37 GMT"
}
] | 1,488,412,800,000 | [
[
"Besold",
"Tarek R.",
""
],
[
"Garcez",
"Artur d'Avila",
""
],
[
"Stenning",
"Keith",
""
],
[
"van der Torre",
"Leendert",
""
],
[
"van Lambalgen",
"Michiel",
""
]
] |
1701.05291 | Zhipeng Huang | Zhipeng Huang and Nikos Mamoulis | Heterogeneous Information Network Embedding for Meta Path based
Proximity | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A network embedding is a representation of a large graph in a low-dimensional
space, where vertices are modeled as vectors. The objective of a good embedding
is to preserve the proximity between vertices in the original graph. This way,
typical search and mining methods can be applied in the embedded space with the
help of off-the-shelf multidimensional indexing approaches. Existing network
embedding techniques focus on homogeneous networks, where all vertices are
considered to belong to a single class.
| [
{
"version": "v1",
"created": "Thu, 19 Jan 2017 04:00:46 GMT"
}
] | 1,484,870,400,000 | [
[
"Huang",
"Zhipeng",
""
],
[
"Mamoulis",
"Nikos",
""
]
] |
1701.06049 | James MacGlashan | James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, Guan Wang, David
Roberts, Matthew E. Taylor, Michael L. Littman | Interactive Learning from Policy-Dependent Human Feedback | 8 pages + references, 5 figures | International Conference on Machine Learning. PMLR, 2017 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the problem of interactively learning behaviors
communicated by a human teacher using positive and negative feedback. Much
previous work on this problem has made the assumption that people provide
feedback for decisions that is dependent on the behavior they are teaching and
is independent from the learner's current policy. We present empirical results
that show this assumption to be false -- whether human trainers give a positive
or negative feedback for a decision is influenced by the learner's current
policy. Based on this insight, we introduce {\em Convergent Actor-Critic by
Humans} (COACH), an algorithm for learning from policy-dependent feedback that
converges to a local optimum. Finally, we demonstrate that COACH can
successfully learn multiple behaviors on a physical robot.
| [
{
"version": "v1",
"created": "Sat, 21 Jan 2017 16:37:41 GMT"
},
{
"version": "v2",
"created": "Sat, 28 Jan 2023 17:02:34 GMT"
}
] | 1,675,123,200,000 | [
[
"MacGlashan",
"James",
""
],
[
"Ho",
"Mark K",
""
],
[
"Loftin",
"Robert",
""
],
[
"Peng",
"Bei",
""
],
[
"Wang",
"Guan",
""
],
[
"Roberts",
"David",
""
],
[
"Taylor",
"Matthew E.",
""
],
[
"Littman",
"Michael L.",
""
]
] |
1701.06167 | \c{C}a\u{g}r{\i} Latifo\u{g}lu | \c{C}a\u{g}r{\i} Latifo\u{g}lu | Binary Matrix Guessing Problem | 9 pages, 4 tables reason for withdrawal: Paper will be rewritten with
experiments replicated on verified and validated hardware and software | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the Binary Matrix Guessing Problem and provide two algorithms to
solve this problem. The first algorithm we introduce is Elementwise Probing
Algorithm (EPA) which is very fast under a score which utilizes Frobenius
Distance. The second algorithm is Additive Reinforcement Learning Algorithm
which combines ideas from perceptron algorithm and reinforcement learning
algorithm. This algorithm is significantly slower compared to first one, but
less restrictive and generalizes better. We compare computational performance
of both algorithms and provide numerical results.
reason for withdrawal: Paper will be rewritten with experiments replicated on
verified and validated hardware and software.
| [
{
"version": "v1",
"created": "Sun, 22 Jan 2017 14:19:25 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Oct 2018 10:33:17 GMT"
}
] | 1,539,734,400,000 | [
[
"Latifoğlu",
"Çağrı",
""
]
] |
1701.06388 | Emmanuel Hebrard | Emmanuel H\'ebrard (LAAS-ROC), Marie-Jos\'e Huguet (LAAS-ROC), Daniel
Veysseire (LAAS-ROC), Ludivine Sauvan (LAAS-ROC), Bertrand Cabon | Constraint programming for planning test campaigns of communications
satellites | null | Constraints, Springer Verlag, 2017, 22, pp.73 - 89 | 10.1007/s10601-016-9254-x | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The payload of communications satellites must go through a series of tests to
assert their ability to survive in space. Each test involves some equipment of
the payload to be active, which has an impact on the temperature of the
payload. Sequencing these tests in a way that ensures the thermal stability of
the payload and minimizes the overall duration of the test campaign is a very
important objective for satellite manufacturers. The problem can be decomposed
in two sub-problems corresponding to two objectives: First, the number of
distinct configurations necessary to run the tests must be minimized. This can
be modeled as packing the tests into configurations, and we introduce a set of
implied constraints to improve the lower bound of the model. Second, tests must
be sequenced so that the number of times an equipment unit has to be switched
on or off is minimized. We model this aspect using the constraint Switch, where
a buffer with limited capacity represents the currently active equipment units,
and we introduce an improvement of the propagation algorithm for this
constraint. We then introduce a search strategy in which we sequentially solve
the sub-problems (packing and sequencing). Experiments conducted on real and
random instances show the respective interest of our contributions.
| [
{
"version": "v1",
"created": "Mon, 23 Jan 2017 13:48:35 GMT"
}
] | 1,485,216,000,000 | [
[
"Hébrard",
"Emmanuel",
"",
"LAAS-ROC"
],
[
"Huguet",
"Marie-José",
"",
"LAAS-ROC"
],
[
"Veysseire",
"Daniel",
"",
"LAAS-ROC"
],
[
"Sauvan",
"Ludivine",
"",
"LAAS-ROC"
],
[
"Cabon",
"Bertrand",
""
]
] |
1701.06635 | Abhinav Jauhri | Abhinav Jauhri, Brian Foo, Jerome Berclaz, Chih Chi Hu, Radek
Grzeszczuk, Vasu Parameswaran, John Paul Shen | Space-Time Graph Modeling of Ride Requests Based on Real-World Data | Accepted at AAAI-17 Workshop on AI and OR for Social Good
(AIORSocGood-17) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper focuses on modeling ride requests and their variations over
location and time, based on analyzing extensive real-world data from a
ride-sharing service. We introduce a graph model that captures the spatial and
temporal variability of ride requests and the potentials for ride pooling. We
discover these ride request graphs exhibit a well known property called
densification power law often found in real graphs modelling human behaviors.
We show the pattern of ride requests and the potential of ride pooling for a
city can be characterized by the densification factor of the ride request
graphs. Previous works have shown that it is possible to automatically generate
synthetic versions of these graphs that exhibit a given densification factor.
We present an algorithm for automatic generation of synthetic ride request
graphs that match quite well the densification factor of ride request graphs
from actual ride request data.
| [
{
"version": "v1",
"created": "Mon, 23 Jan 2017 21:18:33 GMT"
}
] | 1,485,302,400,000 | [
[
"Jauhri",
"Abhinav",
""
],
[
"Foo",
"Brian",
""
],
[
"Berclaz",
"Jerome",
""
],
[
"Hu",
"Chih Chi",
""
],
[
"Grzeszczuk",
"Radek",
""
],
[
"Parameswaran",
"Vasu",
""
],
[
"Shen",
"John Paul",
""
]
] |
1701.06699 | Jeremy Morton | Alex Kuefler, Jeremy Morton, Tim Wheeler, Mykel Kochenderfer | Imitating Driver Behavior with Generative Adversarial Networks | 8 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.
| [
{
"version": "v1",
"created": "Tue, 24 Jan 2017 00:59:42 GMT"
}
] | 1,485,302,400,000 | [
[
"Kuefler",
"Alex",
""
],
[
"Morton",
"Jeremy",
""
],
[
"Wheeler",
"Tim",
""
],
[
"Kochenderfer",
"Mykel",
""
]
] |
1701.07657 | Giovanni Sileno | Giovanni Sileno | Operationalizing Declarative and Procedural Knowledge: a Benchmark on
Logic Programming Petri Nets (LPPNs) | draft version -- updated | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modelling, specifying and reasoning about complex systems requires to process
in an integrated fashion declarative and procedural aspects of the target
domain. The paper reports on an experiment conducted with a propositional
version of Logic Programming Petri Nets (LPPNs), a notation extending Petri
Nets with logic programming constructs. Two semantics are presented: a
denotational semantics that fully maps the notation to ASP via Event Calculus;
and a hybrid operational semantics that process separately the causal
mechanisms via Petri nets, and the constraints associated to objects and to
events via Answer Set Programming (ASP). These two alternative specifications
enable an empirical evaluation in terms of computational efficiency.
Experimental results show that the hybrid semantics is more efficient w.r.t.
sequences, whereas the two semantics follows the same behaviour w.r.t.
branchings (although the denotational one performs better in absolute terms).
| [
{
"version": "v1",
"created": "Thu, 26 Jan 2017 11:21:50 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Jul 2020 23:08:48 GMT"
}
] | 1,596,499,200,000 | [
[
"Sileno",
"Giovanni",
""
]
] |
1701.08306 | Zohreh Shams | Zohreh Shams, Marina De Vos, Julian Padget and Wamberto W. Vasconcelos | Practical Reasoning with Norms for Autonomous Software Agents (Full
Edition) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autonomous software agents operating in dynamic environments need to
constantly reason about actions in pursuit of their goals, while taking into
consideration norms which might be imposed on those actions. Normative
practical reasoning supports agents making decisions about what is best for
them to (not) do in a given situation. What makes practical reasoning
challenging is the interplay between goals that agents are pursuing and the
norms that the agents are trying to uphold. We offer a formalisation to allow
agents to plan for multiple goals and norms in the presence of durative actions
that can be executed concurrently. We compare plans based on decision-theoretic
notions (i.e. utility) such that the utility gain of goals and utility loss of
norm violations are the basis for this comparison. The set of optimal plans
consists of plans that maximise the overall utility, each of which can be
chosen by the agent to execute. We provide an implementation of our proposal in
Answer Set Programming, thus allowing us to state the original problem in terms
of a logic program that can be queried for solutions with specific properties.
The implementation is proven to be sound and complete.
| [
{
"version": "v1",
"created": "Sat, 28 Jan 2017 17:55:04 GMT"
}
] | 1,485,820,800,000 | [
[
"Shams",
"Zohreh",
""
],
[
"De Vos",
"Marina",
""
],
[
"Padget",
"Julian",
""
],
[
"Vasconcelos",
"Wamberto W.",
""
]
] |
1701.08317 | Sarath Sreedharan | Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang and Subbarao
Kambhampati | Plan Explanations as Model Reconciliation: Moving Beyond Explanation as
Soliloquy | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When AI systems interact with humans in the loop, they are often called on to
provide explanations for their plans and behavior. Past work on plan
explanations primarily involved the AI system explaining the correctness of its
plan and the rationale for its decision in terms of its own model. Such
soliloquy is wholly inadequate in most realistic scenarios where the humans
have domain and task models that differ significantly from that used by the AI
system. We posit that the explanations are best studied in light of these
differing models. In particular, we show how explanation can be seen as a
"model reconciliation problem" (MRP), where the AI system in effect suggests
changes to the human's model, so as to make its plan be optimal with respect to
that changed human model. We will study the properties of such explanations,
present algorithms for automatically computing them, and evaluate the
performance of the algorithms.
| [
{
"version": "v1",
"created": "Sat, 28 Jan 2017 19:22:52 GMT"
},
{
"version": "v2",
"created": "Sun, 26 Feb 2017 22:39:38 GMT"
},
{
"version": "v3",
"created": "Mon, 24 Apr 2017 15:54:37 GMT"
},
{
"version": "v4",
"created": "Sun, 28 May 2017 03:24:37 GMT"
},
{
"version": "v5",
"created": "Tue, 30 May 2017 21:31:24 GMT"
}
] | 1,496,275,200,000 | [
[
"Chakraborti",
"Tathagata",
""
],
[
"Sreedharan",
"Sarath",
""
],
[
"Zhang",
"Yu",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
1701.08546 | Marc Sol\'e Sim\'o | Marc Sol\'e, Victor Munt\'es-Mulero, Annie Ibrahim Rana, Giovani
Estrada | Survey on Models and Techniques for Root-Cause Analysis | 18 pages, 222 references | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automation and computer intelligence to support complex human decisions
becomes essential to manage large and distributed systems in the Cloud and IoT
era. Understanding the root cause of an observed symptom in a complex system
has been a major problem for decades. As industry dives into the IoT world and
the amount of data generated per year grows at an amazing speed, an important
question is how to find appropriate mechanisms to determine root causes that
can handle huge amounts of data or may provide valuable feedback in real-time.
While many survey papers aim at summarizing the landscape of techniques for
modelling system behavior and infering the root cause of a problem based in the
resulting models, none of those focuses on analyzing how the different
techniques in the literature fit growing requirements in terms of performance
and scalability. In this survey, we provide a review of root-cause analysis,
focusing on these particular aspects. We also provide guidance to choose the
best root-cause analysis strategy depending on the requirements of a particular
system and application.
| [
{
"version": "v1",
"created": "Mon, 30 Jan 2017 11:17:14 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Jul 2017 13:01:07 GMT"
}
] | 1,499,126,400,000 | [
[
"Solé",
"Marc",
""
],
[
"Muntés-Mulero",
"Victor",
""
],
[
"Rana",
"Annie Ibrahim",
""
],
[
"Estrada",
"Giovani",
""
]
] |
1701.08665 | Xiaodong Pan | Xiaodong Pan, Yang Xu | Redefinition of the concept of fuzzy set based on vague partition from
the perspective of axiomatization | 25 pages. arXiv admin note: substantial text overlap with
arXiv:1506.07821 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Based on the in-depth analysis of the essence and features of vague
phenomena, this paper focuses on establishing the axiomatical foundation of
membership degree theory for vague phenomena, presents an axiomatic system to
govern membership degrees and their interconnections. On this basis, the
concept of vague partition is introduced, further, the concept of fuzzy set
introduced by Zadeh in 1965 is redefined based on vague partition from the
perspective of axiomatization. The thesis defended in this paper is that the
relationship among vague attribute values should be the starting point to
recognize and model vague phenomena from a quantitative view.
| [
{
"version": "v1",
"created": "Fri, 27 Jan 2017 11:27:45 GMT"
}
] | 1,486,339,200,000 | [
[
"Pan",
"Xiaodong",
""
],
[
"Xu",
"Yang",
""
]
] |
1701.08709 | Fred Glover | Fred Glover | Diversification Methods for Zero-One Optimization | 28 pages, 7 illustrations, 4 pseudocodes | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce new diversification methods for zero-one optimization that
significantly extend strategies previously introduced in the setting of
metaheuristic search. Our methods incorporate easily implemented strategies for
partitioning assignments of values to variables, accompanied by processes
called augmentation and shifting which create greater flexibility and
generality. We then show how the resulting collection of diversified solutions
can be further diversified by means of permutation mappings, which equally can
be used to generate diversified collections of permutations for applications
such as scheduling and routing. These methods can be applied to non-binary
vectors by the use of binarization procedures and by Diversification-Based
Learning (DBL) procedures which also provide connections to applications in
clustering and machine learning. Detailed pseudocode and numerical
illustrations are provided to show the operation of our methods and the
collections of solutions they create.
| [
{
"version": "v1",
"created": "Mon, 30 Jan 2017 17:01:31 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Mar 2017 04:19:25 GMT"
}
] | 1,490,313,600,000 | [
[
"Glover",
"Fred",
""
]
] |
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