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1707.04775 | Tathagata Chakraborti | Tathagata Chakraborti, Subbarao Kambhampati, Matthias Scheutz, Yu
Zhang | AI Challenges in Human-Robot Cognitive Teaming | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Among the many anticipated roles for robots in the future is that of being a
human teammate. Aside from all the technological hurdles that have to be
overcome with respect to hardware and control to make robots fit to work with
humans, the added complication here is that humans have many conscious and
subconscious expectations of their teammates - indeed, we argue that teaming is
mostly a cognitive rather than physical coordination activity. This introduces
new challenges for the AI and robotics community and requires fundamental
changes to the traditional approach to the design of autonomy. With this in
mind, we propose an update to the classical view of the intelligent agent
architecture, highlighting the requirements for mental modeling of the human in
the deliberative process of the autonomous agent. In this article, we outline
briefly the recent efforts of ours, and others in the community, towards
developing cognitive teammates along these guidelines.
| [
{
"version": "v1",
"created": "Sat, 15 Jul 2017 18:42:16 GMT"
},
{
"version": "v2",
"created": "Sun, 13 Aug 2017 00:14:32 GMT"
}
] | 1,502,755,200,000 | [
[
"Chakraborti",
"Tathagata",
""
],
[
"Kambhampati",
"Subbarao",
""
],
[
"Scheutz",
"Matthias",
""
],
[
"Zhang",
"Yu",
""
]
] |
1707.04828 | Chang-Shing Lee | Chang-Shing Lee, Mei-Hui Wang, Sheng-Chi Yang, Pi-Hsia Hung, Su-Wei
Lin, Nan Shuo, Naoyuki Kubota, Chun-Hsun Chou, Ping-Chiang Chou, and
Chia-Hsiu Kao | FML-based Dynamic Assessment Agent for Human-Machine Cooperative System
on Game of Go | 26 pages, 14 figures | null | 10.1142/S0218488517500295 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we demonstrate the application of Fuzzy Markup Language (FML)
to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an
FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The
proposed FDAA comprises an intelligent decision-making and learning mechanism,
an intelligent game bot, a proximal development agent, and an intelligent
agent. The intelligent game bot is based on the open-source code of Facebook
Darkforest, and it features a representational state transfer application
programming interface mechanism. The proximal development agent contains a
dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a
fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket
engine and a summarization agent that is based on the estimated win rate,
real-time simulation number, and matching degree of predicted moves.
Additionally, the FML for player performance evaluation and linguistic
descriptions for game results commentary are presented. We experimentally
verify and validate the performance of the FDAA and variants of the FHMCS by
testing five games in 2016 and 60 games of Google Master Go, a new version of
the AlphaGo program, in January 2017. The experimental results demonstrate that
the proposed FDAA can work effectively for Go applications.
| [
{
"version": "v1",
"created": "Sun, 16 Jul 2017 06:20:19 GMT"
}
] | 1,555,286,400,000 | [
[
"Lee",
"Chang-Shing",
""
],
[
"Wang",
"Mei-Hui",
""
],
[
"Yang",
"Sheng-Chi",
""
],
[
"Hung",
"Pi-Hsia",
""
],
[
"Lin",
"Su-Wei",
""
],
[
"Shuo",
"Nan",
""
],
[
"Kubota",
"Naoyuki",
""
],
[
"Chou",
"Chun-Hsun",
""
],
[
"Chou",
"Ping-Chiang",
""
],
[
"Kao",
"Chia-Hsiu",
""
]
] |
1707.04903 | Antoine Cornu\'ejols | Antoine Cornu\'ejols, Andr\'ee Tiberghien and G\'erard Collet | Tunnel Effects in Cognition: A new Mechanism for Scientific Discovery
and Education | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is quite exceptional, if it ever happens, that a new conceptual domain be
built from scratch. Usually, it is developed and mastered in interaction, both
positive and negative, with other more operational existing domains. Few
reasoning mechanisms have been proposed to account for the interplay of
different conceptual domains and the transfer of information from one to
another. Analogical reasoning is one, blending is another. This paper presents
a new mechanism, called 'tunnel effect', that may explain, in part, how
scientists and students reason while constructing a new conceptual domain. One
experimental study with high school students and analyses from the history of
science, particularly about the birth of classical thermodynamics, provide
evidence and illustrate this mechanism. The knowledge organization, processes
and conditions for its appearance are detailed and put into the perspective of
a computational model. Specifically, we put forward the hypothesis that two
levels of knowledge, notional and conceptual, cooperate in the scientific
discovery process when a new conceptual domain is being built. The type of
conceptual learning that can be associated with tunnel effect is discussed and
a thorough comparison is made with analogical reasoning in order to underline
the main features of the new proposed mechanism.
| [
{
"version": "v1",
"created": "Sun, 16 Jul 2017 16:05:21 GMT"
}
] | 1,500,336,000,000 | [
[
"Cornuéjols",
"Antoine",
""
],
[
"Tiberghien",
"Andrée",
""
],
[
"Collet",
"Gérard",
""
]
] |
1707.04943 | Michael Mayo Dr | Michael Mayo and Eibe Frank | Improving Naive Bayes for Regression with Optimised Artificial Surrogate
Data | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Can we evolve better training data for machine learning algorithms? To
investigate this question we use population-based optimisation algorithms to
generate artificial surrogate training data for naive Bayes for regression. We
demonstrate that the generalisation performance of naive Bayes for regression
models is enhanced by training them on the artificial data as opposed to the
real data. These results are important for two reasons. Firstly, naive Bayes
models are simple and interpretable but frequently underperform compared to
more complex "black box" models, and therefore new methods of enhancing
accuracy are called for. Secondly, the idea of using the real training data
indirectly in the construction of the artificial training data, as opposed to
directly for model training, is a novel twist on the usual machine learning
paradigm.
| [
{
"version": "v1",
"created": "Sun, 16 Jul 2017 20:53:06 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Oct 2017 19:43:09 GMT"
},
{
"version": "v3",
"created": "Tue, 27 Nov 2018 21:04:24 GMT"
}
] | 1,543,449,600,000 | [
[
"Mayo",
"Michael",
""
],
[
"Frank",
"Eibe",
""
]
] |
1707.04957 | Zhuo Chen | Zhuo Chen, Elmer Salazar, Kyle Marple, Gopal Gupta, Lakshman Tamil,
Sandeep Das, Alpesh Amin | Improving Adherence to Heart Failure Management Guidelines via Abductive
Reasoning | Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017
15 pages, LaTeX | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Management of chronic diseases such as heart failure (HF) is a major public
health problem. A standard approach to managing chronic diseases by medical
community is to have a committee of experts develop guidelines that all
physicians should follow. Due to their complexity, these guidelines are
difficult to implement and are adopted slowly by the medical community at
large. We have developed a physician advisory system that codes the entire set
of clinical practice guidelines for managing HF using answer set
programming(ASP). In this paper we show how abductive reasoning can be deployed
to find missing symptoms and conditions that the patient must exhibit in order
for a treatment prescribed by a physician to work effectively. Thus, if a
physician does not make an appropriate recommendation or makes a non-adherent
recommendation, our system will advise the physician about symptoms and
conditions that must be in effect for that recommendation to apply. It is under
consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Sun, 16 Jul 2017 22:55:53 GMT"
}
] | 1,500,336,000,000 | [
[
"Chen",
"Zhuo",
""
],
[
"Salazar",
"Elmer",
""
],
[
"Marple",
"Kyle",
""
],
[
"Gupta",
"Gopal",
""
],
[
"Tamil",
"Lakshman",
""
],
[
"Das",
"Sandeep",
""
],
[
"Amin",
"Alpesh",
""
]
] |
1707.05001 | Zhaoyi Pei Mr | Zhaoyi Pei, Songhao Piao, Mohammed Ei Souidi | Coalition formation for Multi-agent Pursuit based on Neural Network and
AGRMF Model | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An approach for coalition formation of multi-agent pursuit based on neural
network and AGRMF model is proposed.This paper constructs a novel neural work
called AGRMF-ANN which consists of feature extraction part and group generation
part. On one hand,The convolutional layers of feature extraction part can
abstract the features of agent group role membership function(AGRMF) for all of
the groups,on the other hand,those features will be fed to the group generation
part based on self-organizing map(SOM) layer which is used to group the
pursuers with similar features in the same group. Besides, we also come up the
group attractiveness function(GAF) to evaluate the quality of groups and the
pursuers contribution in order to adjust the main ability indicators of AGRMF
and other weight of all neural network. The simulation experiment showed that
this proposal can improve the effectiveness of coalition formation for
multi-agent pursuit and ability to adopt pursuit-evasion problem with the scale
of pursuer team growing.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2017 04:41:25 GMT"
}
] | 1,500,336,000,000 | [
[
"Pei",
"Zhaoyi",
""
],
[
"Piao",
"Songhao",
""
],
[
"Souidi",
"Mohammed Ei",
""
]
] |
1707.05152 | Ricardo Gon\c{c}alves | Ricardo Gon\c{c}alves (1), Matthias Knorr (1), Jo\~ao Leite (1),
Stefan Woltran (2) ((1) NOVA LINCS, Universidade Nova de Lisboa, Portugal,
(2) TU Wien, Austria) | When You Must Forget: beyond strong persistence when forgetting in
answer set programming | Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1,
2017, 15 pages, LaTeX (arXiv:YYMM.NNNNN) | null | 10.1017/S1471068417000382 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Among the myriad of desirable properties discussed in the context of
forgetting in Answer Set Programming (ASP), strong persistence naturally
captures its essence. Recently, it has been shown that it is not always
possible to forget a set of atoms from a program while obeying this property,
and a precise criterion regarding what can be forgotten has been presented,
accompanied by a class of forgetting operators that return the correct result
when forgetting is possible.
However, it is an open question what to do when we have to forget a set of
atoms, but cannot without violating this property. In this paper, we address
this issue and investigate three natural alternatives to forget when forgetting
without violating strong persistence is not possible, which turn out to
correspond to the different possible relaxations of the characterization of
strong persistence. Additionally, we discuss their preferable usage, shed light
on the relation between forgetting and notions of relativized equivalence
established earlier in the context of ASP, and present a detailed study on
their computational complexity.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2017 13:41:26 GMT"
}
] | 1,564,617,600,000 | [
[
"Gonçalves",
"Ricardo",
""
],
[
"Knorr",
"Matthias",
""
],
[
"Leite",
"João",
""
],
[
"Woltran",
"Stefan",
""
]
] |
1707.05165 | Lucas Bechberger | Lucas Bechberger and Kai-Uwe K\"uhnberger | A Comprehensive Implementation of Conceptual Spaces | Accepted at AIC 2017 (http://www.di.unito.it/~lieto/AIC2017/), final
paper available at http://ceur-ws.org/Vol-2090/. arXiv admin note:
substantial text overlap with arXiv:1707.02292, arXiv:1801.03929,
arXiv:1706.06366 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points and concepts
are represented by regions in a (potentially) high-dimensional space. Based on
our recent formalization, we present a comprehensive implementation of the
conceptual spaces framework that is not only capable of representing concepts
with inter-domain correlations, but that also offers a variety of operations on
these concepts.
| [
{
"version": "v1",
"created": "Fri, 14 Jul 2017 09:22:23 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Sep 2017 13:27:45 GMT"
},
{
"version": "v3",
"created": "Mon, 23 Apr 2018 06:28:22 GMT"
}
] | 1,524,614,400,000 | [
[
"Bechberger",
"Lucas",
""
],
[
"Kühnberger",
"Kai-Uwe",
""
]
] |
1707.05308 | Amit Sheth | Amit Sheth, Sujan Perera, Sanjaya Wijeratne, Krishnaprasad
Thirunarayan | Knowledge will Propel Machine Understanding of Content: Extrapolating
from Current Examples | Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.07708 | null | 10.1145/3106426.3109448 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.
| [
{
"version": "v1",
"created": "Fri, 14 Jul 2017 19:01:15 GMT"
}
] | 1,500,422,400,000 | [
[
"Sheth",
"Amit",
""
],
[
"Perera",
"Sujan",
""
],
[
"Wijeratne",
"Sanjaya",
""
],
[
"Thirunarayan",
"Krishnaprasad",
""
]
] |
1707.05654 | Zeno Toffano | Zeno Toffano (L2S), Fran\c{c}ois Dubois (LM-Orsay) | Eigenlogic: Interpretable Quantum Observables with applications to Fuzzy
Behavior of Vehicular Robots | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work proposes a formulation of propositional logic, named Eigenlogic,
using quantum observables as propositions. The eigenvalues of these operators
are the truth-values and the associated eigenvectors the interpretations of the
propositional system. Fuzzy logic arises naturally when considering vectors
outside the eigensystem, the fuzzy membership function is obtained by the Born
rule of the logical observable.This approach is then applied in the context of
quantum robots using simple behavioral agents represented by Braitenberg
vehicles. Processing with non-classical logic such as multivalued logic, fuzzy
logic and the quantum Eigenlogic permits to enlarge the behavior possibilities
and the associated decisions of these simple agents.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2017 09:39:05 GMT"
}
] | 1,500,422,400,000 | [
[
"Toffano",
"Zeno",
"",
"L2S"
],
[
"Dubois",
"François",
"",
"LM-Orsay"
]
] |
1707.05858 | Marco Gavanelli | Marco Gavanelli, Maddalena Nonato, Andrea Peano and Davide Bertozzi | Logic Programming approaches for routing fault-free and
maximally-parallel Wavelength Routed Optical Networks on Chip (Application
paper) | Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1,
2017. 16 pages, LaTeX, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One promising trend in digital system integration consists of boosting
on-chip communication performance by means of silicon photonics, thus
materializing the so-called Optical Networks-on-Chip (ONoCs). Among them,
wavelength routing can be used to route a signal to destination by univocally
associating a routing path to the wavelength of the optical carrier. Such
wavelengths should be chosen so to minimize interferences among optical
channels and to avoid routing faults. As a result, physical parameter selection
of such networks requires the solution of complex constrained optimization
problems. In previous work, published in the proceedings of the International
Conference on Computer-Aided Design, we proposed and solved the problem of
computing the maximum parallelism obtainable in the communication between any
two endpoints while avoiding misrouting of optical signals. The underlying
technology, only quickly mentioned in that paper, is Answer Set Programming
(ASP). In this work, we detail the ASP approach we used to solve such problem.
Another important design issue is to select the wavelengths of optical
carriers such that they are spread across the available spectrum, in order to
reduce the likelihood that, due to imperfections in the manufacturing process,
unintended routing faults arise. We show how to address such problem in
Constraint Logic Programming on Finite Domains (CLP(FD)).
This paper is under consideration for possible publication on Theory and
Practice of Logic Programming.
| [
{
"version": "v1",
"created": "Tue, 18 Jul 2017 21:12:26 GMT"
}
] | 1,500,508,800,000 | [
[
"Gavanelli",
"Marco",
""
],
[
"Nonato",
"Maddalena",
""
],
[
"Peano",
"Andrea",
""
],
[
"Bertozzi",
"Davide",
""
]
] |
1707.06446 | Max Schr\"oder | Max Schr\"oder, Stefan L\"udtke, Sebastian Bader, Frank Kr\"uger,
Thomas Kirste | Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems | 7 pages, 3 figures, accepted at UAI-17 Statistical Relational AI
(StarAI) workshop | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Bayesian Filtering for plan and activity recognition is challenging for
scenarios that contain many observation equivalent entities (i.e. entities that
produce the same observations). This is due to the combinatorial explosion in
the number of hypotheses that need to be tracked. However, this class of
problems exhibits a certain symmetry that can be exploited for state space
representation and inference. We analyze current state of the art methods and
find that none of them completely fits the requirements arising in this problem
class. We sketch a novel inference algorithm that provides a solution by
incorporating concepts from Lifted Inference algorithms, Probabilistic Multiset
Rewriting Systems, and Computational State Space Models. Two experiments
confirm that this novel algorithm has the potential to perform efficient
probabilistic inference on this problem class.
| [
{
"version": "v1",
"created": "Thu, 20 Jul 2017 11:14:20 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Aug 2017 15:31:56 GMT"
}
] | 1,502,755,200,000 | [
[
"Schröder",
"Max",
""
],
[
"Lüdtke",
"Stefan",
""
],
[
"Bader",
"Sebastian",
""
],
[
"Krüger",
"Frank",
""
],
[
"Kirste",
"Thomas",
""
]
] |
1707.06766 | Irene Teinemaa | Irene Teinemaa, Marlon Dumas, Marcello La Rosa, and Fabrizio Maria
Maggi | Outcome-Oriented Predictive Process Monitoring: Review and Benchmark | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predictive business process monitoring refers to the act of making
predictions about the future state of ongoing cases of a business process,
based on their incomplete execution traces and logs of historical (completed)
traces. Motivated by the increasingly pervasive availability of fine-grained
event data about business process executions, the problem of predictive process
monitoring has received substantial attention in the past years. In particular,
a considerable number of methods have been put forward to address the problem
of outcome-oriented predictive process monitoring, which refers to classifying
each ongoing case of a process according to a given set of possible categorical
outcomes - e.g., Will the customer complain or not? Will an order be delivered,
canceled or withdrawn? Unfortunately, different authors have used different
datasets, experimental settings, evaluation measures and baselines to assess
their proposals, resulting in poor comparability and an unclear picture of the
relative merits and applicability of different methods. To address this gap,
this article presents a systematic review and taxonomy of outcome-oriented
predictive process monitoring methods, and a comparative experimental
evaluation of eleven representative methods using a benchmark covering 24
predictive process monitoring tasks based on nine real-life event logs.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2017 06:25:31 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Aug 2017 00:22:49 GMT"
},
{
"version": "v3",
"created": "Tue, 19 Jun 2018 19:56:16 GMT"
},
{
"version": "v4",
"created": "Tue, 23 Oct 2018 15:10:07 GMT"
}
] | 1,540,339,200,000 | [
[
"Teinemaa",
"Irene",
""
],
[
"Dumas",
"Marlon",
""
],
[
"La Rosa",
"Marcello",
""
],
[
"Maggi",
"Fabrizio Maria",
""
]
] |
1707.06895 | Pawel Gomoluch | Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo, Antonio Bucchiarone | Towards learning domain-independent planning heuristics | Accepted for the IJCAI-17 Workshop on Architectures for Generality
and Autonomy | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated planning remains one of the most general paradigms in Artificial
Intelligence, providing means of solving problems coming from a wide variety of
domains. One of the key factors restricting the applicability of planning is
its computational complexity resulting from exponentially large search spaces.
Heuristic approaches are necessary to solve all but the simplest problems. In
this work, we explore the possibility of obtaining domain-independent heuristic
functions using machine learning. This is a part of a wider research program
whose objective is to improve practical applicability of planning in systems
for which the planning domains evolve at run time. The challenge is therefore
the learning of (corrections of) domain-independent heuristics that can be
reused across different planning domains.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2017 13:39:24 GMT"
}
] | 1,500,854,400,000 | [
[
"Gomoluch",
"Pawel",
""
],
[
"Alrajeh",
"Dalal",
""
],
[
"Russo",
"Alessandra",
""
],
[
"Bucchiarone",
"Antonio",
""
]
] |
1707.06959 | Davide Fusc\`a | Francesco Calimeri, Davide Fusc\`a, Stefano Germano, Simona Perri and
Jessica Zangari | A Framework for Easing the Development of Applications Embedding Answer
Set Programming | null | null | 10.1145/2967973.2968594 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Answer Set Programming (ASP) is a well-established declarative problem
solving paradigm which became widely used in AI and recognized as a powerful
tool for knowledge representation and reasoning (KRR), especially for its high
expressiveness and the ability to deal also with incomplete knowledge.
Recently, thanks to the availability of a number of robust and efficient
implementations, ASP has been increasingly employed in a number of different
domains, and used for the development of industrial-level and enterprise
applications. This made clear the need for proper development tools and
interoperability mechanisms for easing interaction and integration with
external systems in the widest range of real-world scenarios, including mobile
applications and educational contexts.
In this work we present a framework for integrating the KRR capabilities of
ASP into generic applications. We show the use of the framework by illustrating
proper specializations for some relevant ASP systems over different platforms,
including the mobile setting; furthermore, the potential of the framework for
educational purposes is illustrated by means of the development of several
ASP-based applications.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2017 16:15:31 GMT"
}
] | 1,500,854,400,000 | [
[
"Calimeri",
"Francesco",
""
],
[
"Fuscà",
"Davide",
""
],
[
"Germano",
"Stefano",
""
],
[
"Perri",
"Simona",
""
],
[
"Zangari",
"Jessica",
""
]
] |
1707.07298 | Youssef Hamadi | Youssef Hamadi, Souhila Kaci | Preference Reasoning in Matching Procedures: Application to the
Admission Post-Baccalaureat Platform | 24 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Because preferences naturally arise and play an important role in many
real-life decisions, they are at the backbone of various fields. In particular
preferences are increasingly used in almost all matching procedures-based
applications. In this work we highlight the benefit of using AI insights on
preferences in a large scale application, namely the French Admission
Post-Baccalaureat Platform (APB). Each year APB allocates hundreds of thousands
first year applicants to universities. This is done automatically by matching
applicants preferences to university seats. In practice, APB can be unable to
distinguish between applicants which leads to the introduction of random
selection. This has created frustration in the French public since randomness,
even used as a last mean does not fare well with the republican egalitarian
principle. In this work, we provide a solution to this problem. We take
advantage of recent AI Preferences Theory results to show how to enhance APB in
order to improve expressiveness of applicants preferences and reduce their
exposure to random decisions.
| [
{
"version": "v1",
"created": "Sun, 23 Jul 2017 14:01:08 GMT"
},
{
"version": "v2",
"created": "Sat, 29 Jul 2017 03:48:50 GMT"
},
{
"version": "v3",
"created": "Mon, 25 Mar 2019 09:36:34 GMT"
}
] | 1,553,558,400,000 | [
[
"Hamadi",
"Youssef",
""
],
[
"Kaci",
"Souhila",
""
]
] |
1707.07596 | Pasquale Minervini | Pasquale Minervini, Thomas Demeester, Tim Rockt\"aschel, Sebastian
Riedel | Adversarial Sets for Regularising Neural Link Predictors | Proceedings of the 33rd Conference on Uncertainty in Artificial
Intelligence (UAI), 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In adversarial training, a set of models learn together by pursuing competing
goals, usually defined on single data instances. However, in relational
learning and other non-i.i.d domains, goals can also be defined over sets of
instances. For example, a link predictor for the is-a relation needs to be
consistent with the transitivity property: if is-a(x_1, x_2) and is-a(x_2, x_3)
hold, is-a(x_1, x_3) needs to hold as well. Here we use such assumptions for
deriving an inconsistency loss, measuring the degree to which the model
violates the assumptions on an adversarially-generated set of examples. The
training objective is defined as a minimax problem, where an adversary finds
the most offending adversarial examples by maximising the inconsistency loss,
and the model is trained by jointly minimising a supervised loss and the
inconsistency loss on the adversarial examples. This yields the first method
that can use function-free Horn clauses (as in Datalog) to regularise any
neural link predictor, with complexity independent of the domain size. We show
that for several link prediction models, the optimisation problem faced by the
adversary has efficient closed-form solutions. Experiments on link prediction
benchmarks indicate that given suitable prior knowledge, our method can
significantly improve neural link predictors on all relevant metrics.
| [
{
"version": "v1",
"created": "Mon, 24 Jul 2017 15:00:55 GMT"
}
] | 1,500,940,800,000 | [
[
"Minervini",
"Pasquale",
""
],
[
"Demeester",
"Thomas",
""
],
[
"Rocktäschel",
"Tim",
""
],
[
"Riedel",
"Sebastian",
""
]
] |
1707.07763 | Seyed Mehran Kazemi | Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole | Domain Recursion for Lifted Inference with Existential Quantifiers | 7 pages, 1 figure, Accepted at Statistical Relational AI (StarAI)
Workshop 2017 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In recent work, we proved that the domain recursion inference rule makes
domain-lifted inference possible on several relational probability models
(RPMs) for which the best known time complexity used to be exponential. We also
identified two classes of RPMs for which inference becomes domain lifted when
using domain recursion. These two classes subsume the largest lifted classes
that were previously known. In this paper, we show that domain recursion can
also be applied to models with existential quantifiers. Currently, all lifted
inference algorithms assume that existential quantifiers have been removed in
pre-processing by Skolemization. We show that besides introducing potentially
inconvenient negative weights, Skolemization may increase the time complexity
of inference. We give two example models where domain recursion can replace
Skolemization, avoids the need for dealing with negative numbers, and reduces
the time complexity of inference. These two examples may be interesting from
three theoretical aspects: 1- they provide a better and deeper understanding of
domain recursion and, in general, (lifted) inference, 2- they may serve as
evidence that there are larger classes of models for which domain recursion can
satisfyingly replace Skolemization, and 3- they may serve as evidence that
better Skolemization techniques exist.
| [
{
"version": "v1",
"created": "Mon, 24 Jul 2017 22:29:24 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Jul 2017 23:42:22 GMT"
}
] | 1,501,459,200,000 | [
[
"Kazemi",
"Seyed Mehran",
""
],
[
"Kimmig",
"Angelika",
""
],
[
"Broeck",
"Guy Van den",
""
],
[
"Poole",
"David",
""
]
] |
1707.07907 | Markus Wulfmeier | Markus Wulfmeier, Ingmar Posner, Pieter Abbeel | Mutual Alignment Transfer Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training robots for operation in the real world is a complex, time consuming
and potentially expensive task. Despite significant success of reinforcement
learning in games and simulations, research in real robot applications has not
been able to match similar progress. While sample complexity can be reduced by
training policies in simulation, such policies can perform sub-optimally on the
real platform given imperfect calibration of model dynamics. We present an
approach -- supplemental to fine tuning on the real robot -- to further benefit
from parallel access to a simulator during training and reduce sample
requirements on the real robot. The developed approach harnesses auxiliary
rewards to guide the exploration for the real world agent based on the
proficiency of the agent in simulation and vice versa. In this context, we
demonstrate empirically that the reciprocal alignment for both agents provides
further benefit as the agent in simulation can adjust to optimize its behaviour
for states commonly visited by the real-world agent.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2017 10:43:35 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Sep 2017 08:51:42 GMT"
},
{
"version": "v3",
"created": "Tue, 26 Sep 2017 18:26:06 GMT"
}
] | 1,506,556,800,000 | [
[
"Wulfmeier",
"Markus",
""
],
[
"Posner",
"Ingmar",
""
],
[
"Abbeel",
"Pieter",
""
]
] |
1707.07999 | Kuang Zhou | Kuang Zhou (1), Arnaud Martin (2), Quan Pan (1) ((1) NPU (2) DRUID) | Evidence combination for a large number of sources | 2017 20th International Conference on Information Fusion (FUSION),
Jul 2017, Xi'an, China | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theory of belief functions is an effective tool to deal with the multiple
uncertain information. In recent years, many evidence combination rules have
been proposed in this framework, such as the conjunctive rule, the cautious
rule, the PCR (Proportional Conflict Redistribution) rules and so on. These
rules can be adopted for different types of sources. However, most of these
rules are not applicable when the number of sources is large. This is due to
either the complexity or the existence of an absorbing element (such as the
total conflict mass function for the conjunctive-based rules when applied on
unreliable evidence). In this paper, based on the assumption that the majority
of sources are reliable, a combination rule for a large number of sources,
named LNS (stands for Large Number of Sources), is proposed on the basis of a
simple idea: the more common ideas one source shares with others, the
morereliable the source is. This rule is adaptable for aggregating a large
number of sources among which some are unreliable. It will keep the spirit of
the conjunctive rule to reinforce the belief on the focal elements with which
the sources are in agreement. The mass on the empty set will be kept as an
indicator of the conflict. Moreover, it can be used to elicit the major opinion
among the experts. The experimental results on synthetic mass functionsverify
that the rule can be effectively used to combine a large number of mass
functions and to elicit the major opinion.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2017 13:52:40 GMT"
}
] | 1,501,027,200,000 | [
[
"Zhou",
"Kuang",
"",
"NPU"
],
[
"Martin",
"Arnaud",
"",
"DRUID"
],
[
"Pan",
"Quan",
"",
"NPU"
]
] |
1707.08000 | Gilles Falquet | Sahar Aljalbout (1), Gilles Falquet (1) ((1) CUI) | Un mod\`ele pour la repr\'esentation des connaissances temporelles dans
les documents historiques | in French, IC\_2017 - 28\`emes Journ\'ees francophones d'Ing\'enierie
des Connaissances, Jul 2017, Caen, France | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Processing and publishing the data of the historical sciences in the semantic
web is an interesting challenge in which the representation of temporal aspects
plays a key role. We propose in this paper a model of temporal knowledge
representation adapted to work on historical documents. This model is based on
the notion of fluent that is represented in RDF graphs. We show how this model
allows to represent the knowledge necessary to the historians and how it can be
used to reason on this knowledge using the SWRL and SPARQL languages. This
model is being used in a project to digitize, study and publish the manuscripts
of linguist Ferdinand de Saussure.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2017 13:54:45 GMT"
}
] | 1,501,027,200,000 | [
[
"Aljalbout",
"Sahar",
"",
"CUI"
],
[
"Falquet",
"Gilles",
"",
"CUI"
]
] |
1707.08151 | Francisco Henrique Otte Vieira de Faria | Francisco H. O. V. de Faria, Arthur C. Gusm\~ao, Fabio G. Cozman,
Denis D. Mau\'a | Speeding-up ProbLog's Parameter Learning | StarAI - International Workshop on Statistical Relational AI | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | ProbLog is a state-of-art combination of logic programming and probabilities;
in particular ProbLog offers parameter learning through a variant of the EM
algorithm. However, the resulting learning algorithm is rather slow, even when
the data are complete. In this short paper we offer some insights that lead to
orders of magnitude improvements in ProbLog's parameter learning speed with
complete data.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2017 18:47:18 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Aug 2017 20:49:52 GMT"
}
] | 1,501,718,400,000 | [
[
"de Faria",
"Francisco H. O. V.",
""
],
[
"Gusmão",
"Arthur C.",
""
],
[
"Cozman",
"Fabio G.",
""
],
[
"Mauá",
"Denis D.",
""
]
] |
1707.08234 | Jeremy Morton | Jeremy Morton, Tim A. Wheeler, Mykel J. Kochenderfer | Closed-Loop Policies for Operational Tests of Safety-Critical Systems | 12 pages, 5 figures, 5 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Manufacturers of safety-critical systems must make the case that their
product is sufficiently safe for public deployment. Much of this case often
relies upon critical event outcomes from real-world testing, requiring
manufacturers to be strategic about how they allocate testing resources in
order to maximize their chances of demonstrating system safety. This work
frames the partially observable and belief-dependent problem of test scheduling
as a Markov decision process, which can be solved efficiently to yield
closed-loop manufacturer testing policies. By solving for policies over a wide
range of problem formulations, we are able to provide high-level guidance for
manufacturers and regulators on issues relating to the testing of
safety-critical systems. This guidance spans an array of topics, including
circumstances under which manufacturers should continue testing despite
observed incidents, when manufacturers should test aggressively, and when
regulators should increase or reduce the real-world testing requirements for an
autonomous vehicle.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2017 21:48:58 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Dec 2017 18:20:38 GMT"
},
{
"version": "v3",
"created": "Sat, 19 May 2018 20:34:54 GMT"
}
] | 1,526,947,200,000 | [
[
"Morton",
"Jeremy",
""
],
[
"Wheeler",
"Tim A.",
""
],
[
"Kochenderfer",
"Mykel J.",
""
]
] |
1707.08342 | Yann Dauxais | Thomas Guyet (1), Andr\'e Happe, Yann Dauxais (2) ((1) LACODAM, (2)
UR1) | Declarative Sequential Pattern Mining of Care Pathways | null | Conference on Artificial Intelligence in Medicine in Europe, Jun
2017, Vienna, Austria. 24, pp.1161 - 266, 2017 | 10.1002/pds.3879 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequential pattern mining algorithms are widely used to explore care pathways
database, but they generate a deluge of patterns, mostly redundant or useless.
Clinicians need tools to express complex mining queries in order to generate
less but more significant patterns. These algorithms are not versatile enough
to answer complex clinician queries. This article proposes to apply a
declarative pattern mining approach based on Answer Set Programming paradigm.
It is exemplified by a pharmaco-epidemiological study investigating the
possible association between hospitalization for seizure and antiepileptic drug
switch from a french medico-administrative database.
| [
{
"version": "v1",
"created": "Wed, 26 Jul 2017 09:49:21 GMT"
}
] | 1,501,113,600,000 | [
[
"Guyet",
"Thomas",
""
],
[
"Happe",
"André",
""
],
[
"Dauxais",
"Yann",
""
]
] |
1707.08468 | Rafael Pe\~naloza | Alessandro Artale, Enrico Franconi, Rafael Pe\~naloza and Francesco
Sportelli | A Decidable Very Expressive Description Logic for Databases (Extended
Version) | 20 pages. Extended version of paper appearing in the International
Semantic Web Conference (ISWC 2017). arXiv admin note: text overlap with
arXiv:1604.00799 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally
closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples
(generalising the positional notation), projections of relations, and global
and local objectification of relations, able to express inclusion, functional,
key, and external uniqueness dependencies. The logic is equipped with both TBox
and ABox axioms. We show how a simple syntactic restriction on the appearance
of projections sharing common attributes in a $\mathcal{DLR}^+$ knowledge base
makes reasoning in the language decidable with the same computational
complexity as $\mathcal{DLR}$. The obtained $\mathcal{DLR}^\pm$ n-ary
description logic is able to encode more thoroughly conceptual data models such
as EER, UML, and ORM.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2017 12:46:24 GMT"
}
] | 1,501,113,600,000 | [
[
"Artale",
"Alessandro",
""
],
[
"Franconi",
"Enrico",
""
],
[
"Peñaloza",
"Rafael",
""
],
[
"Sportelli",
"Francesco",
""
]
] |
1707.08704 | Rodrigo de Salvo Braz | Gabriel Azevedo Ferreira, Quentin Bertrand, Charles Maussion, Rodrigo
de Salvo Braz | Anytime Exact Belief Propagation | Submission to StaRAI-17 workshop at UAI-17 conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Statistical Relational Models and, more recently, Probabilistic Programming,
have been making strides towards an integration of logic and probabilistic
reasoning. A natural expectation for this project is that a probabilistic logic
reasoning algorithm reduces to a logic reasoning algorithm when provided a
model that only involves 0-1 probabilities, exhibiting all the advantages of
logic reasoning such as short-circuiting, intelligibility, and the ability to
provide proof trees for a query answer. In fact, we can take this further and
require that these characteristics be present even for probabilistic models
with probabilities \emph{near} 0 and 1, with graceful degradation as the model
becomes more uncertain. We also seek inference that has amortized constant time
complexity on a model's size (even if still exponential in the induced width of
a more directly relevant portion of it) so that it can be applied to huge
knowledge bases of which only a relatively small portion is relevant to typical
queries. We believe that, among the probabilistic reasoning algorithms, Belief
Propagation is the most similar to logic reasoning: messages are propagated
among neighboring variables, and the paths of message-passing are similar to
proof trees. However, Belief Propagation is either only applicable to tree
models, or approximate (and without guarantees) for precision and convergence.
In this paper we present work in progress on an Anytime Exact Belief
Propagation algorithm that is very similar to Belief Propagation but is exact
even for graphical models with cycles, while exhibiting soft short-circuiting,
amortized constant time complexity in the model size, and which can provide
probabilistic proof trees.
| [
{
"version": "v1",
"created": "Thu, 27 Jul 2017 04:31:34 GMT"
}
] | 1,501,200,000,000 | [
[
"Ferreira",
"Gabriel Azevedo",
""
],
[
"Bertrand",
"Quentin",
""
],
[
"Maussion",
"Charles",
""
],
[
"Braz",
"Rodrigo de Salvo",
""
]
] |
1707.08740 | EPTCS | Weiwei Chen (Sun Yat-sen University. ILLC, University of Amsterdam),
Ulle Endriss (ILLC, University of Amsterdam) | Preservation of Semantic Properties during the Aggregation of Abstract
Argumentation Frameworks | In Proceedings TARK 2017, arXiv:1707.08250 | EPTCS 251, 2017, pp. 118-133 | 10.4204/EPTCS.251.9 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An abstract argumentation framework can be used to model the argumentative
stance of an agent at a high level of abstraction, by indicating for every pair
of arguments that is being considered in a debate whether the first attacks the
second. When modelling a group of agents engaged in a debate, we may wish to
aggregate their individual argumentation frameworks to obtain a single such
framework that reflects the consensus of the group. Even when agents disagree
on many details, there may well be high-level agreement on important semantic
properties, such as the acceptability of a given argument. Using techniques
from social choice theory, we analyse under what circumstances such semantic
properties agreed upon by the individual agents can be preserved under
aggregation.
| [
{
"version": "v1",
"created": "Thu, 27 Jul 2017 07:47:12 GMT"
}
] | 1,501,200,000,000 | [
[
"Chen",
"Weiwei",
"",
"Sun Yat-sen University. ILLC, University of Amsterdam"
],
[
"Endriss",
"Ulle",
"",
"ILLC, University of Amsterdam"
]
] |
1707.08817 | Mel Vecerik | Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier
Pietquin, Bilal Piot, Nicolas Heess, Thomas Roth\"orl, Thomas Lampe, Martin
Riedmiller | Leveraging Demonstrations for Deep Reinforcement Learning on Robotics
Problems with Sparse Rewards | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a general and model-free approach for Reinforcement Learning (RL)
on real robotics with sparse rewards. We build upon the Deep Deterministic
Policy Gradient (DDPG) algorithm to use demonstrations. Both demonstrations and
actual interactions are used to fill a replay buffer and the sampling ratio
between demonstrations and transitions is automatically tuned via a prioritized
replay mechanism. Typically, carefully engineered shaping rewards are required
to enable the agents to efficiently explore on high dimensional control
problems such as robotics. They are also required for model-based acceleration
methods relying on local solvers such as iLQG (e.g. Guided Policy Search and
Normalized Advantage Function). The demonstrations replace the need for
carefully engineered rewards, and reduce the exploration problem encountered by
classical RL approaches in these domains. Demonstrations are collected by a
robot kinesthetically force-controlled by a human demonstrator. Results on four
simulated insertion tasks show that DDPG from demonstrations out-performs DDPG,
and does not require engineered rewards. Finally, we demonstrate the method on
a real robotics task consisting of inserting a clip (flexible object) into a
rigid object.
| [
{
"version": "v1",
"created": "Thu, 27 Jul 2017 11:16:53 GMT"
},
{
"version": "v2",
"created": "Mon, 8 Oct 2018 13:38:52 GMT"
}
] | 1,539,043,200,000 | [
[
"Vecerik",
"Mel",
""
],
[
"Hester",
"Todd",
""
],
[
"Scholz",
"Jonathan",
""
],
[
"Wang",
"Fumin",
""
],
[
"Pietquin",
"Olivier",
""
],
[
"Piot",
"Bilal",
""
],
[
"Heess",
"Nicolas",
""
],
[
"Rothörl",
"Thomas",
""
],
[
"Lampe",
"Thomas",
""
],
[
"Riedmiller",
"Martin",
""
]
] |
1707.08879 | Ankit Anand | Ankit Anand, Ritesh Noothigattu, Parag Singla and Mausam | Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models | 9 pages, 5 figures | Proceedings of the 20th International Conference on Artificial
Intelligence and Statistics, PMLR 54: 1541-1549 (2017) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lifted inference algorithms commonly exploit symmetries in a probabilistic
graphical model (PGM) for efficient inference. However, existing algorithms for
Boolean-valued domains can identify only those pairs of states as symmetric, in
which the number of ones and zeros match exactly (count symmetries). Moreover,
algorithms for lifted inference in multi-valued domains also compute a
multi-valued extension of count symmetries only. These algorithms miss many
symmetries in a domain. In this paper, we present first algorithms to compute
non-count symmetries in both Boolean-valued and multi-valued domains. Our
methods can also find symmetries between multi-valued variables that have
different domain cardinalities. The key insight in the algorithms is that they
change the unit of symmetry computation from a variable to a variable-value
(VV) pair. Our experiments find that exploiting these symmetries in MCMC can
obtain substantial computational gains over existing algorithms.
| [
{
"version": "v1",
"created": "Thu, 27 Jul 2017 14:28:04 GMT"
}
] | 1,501,200,000,000 | [
[
"Anand",
"Ankit",
""
],
[
"Noothigattu",
"Ritesh",
""
],
[
"Singla",
"Parag",
""
],
[
"Mausam",
"",
""
]
] |
1707.08901 | Alessandro Valitutti | Alessandro Valitutti and Giuseppe Trautteur | Providing Self-Aware Systems with Reflexivity | 12 pages plus bibliography, appendices with code description, code of
the proof-of-concept implementation, and examples of execution | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new type of self-aware systems inspired by ideas from
higher-order theories of consciousness. First, we discussed the crucial
distinction between introspection and reflexion. Then, we focus on
computational reflexion as a mechanism by which a computer program can inspect
its own code at every stage of the computation. Finally, we provide a formal
definition and a proof-of-concept implementation of computational reflexion,
viewed as an enriched form of program interpretation and a way to dynamically
"augment" a computational process.
| [
{
"version": "v1",
"created": "Thu, 27 Jul 2017 15:05:27 GMT"
}
] | 1,501,200,000,000 | [
[
"Valitutti",
"Alessandro",
""
],
[
"Trautteur",
"Giuseppe",
""
]
] |
1707.09079 | Anestis Fachantidis | Anestis Fachantidis, Matthew E. Taylor, and Ioannis Vlahavas | Learning to Teach Reinforcement Learning Agents | null | null | 10.3390/make1010002 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article we study the transfer learning model of action advice under a
budget. We focus on reinforcement learning teachers providing action advice to
heterogeneous students playing the game of Pac-Man under a limited advice
budget. First, we examine several critical factors affecting advice quality in
this setting, such as the average performance of the teacher, its variance and
the importance of reward discounting in advising. The experiments show the
non-trivial importance of the coefficient of variation (CV) as a statistic for
choosing policies that generate advice. The CV statistic relates variance to
the corresponding mean. Second, the article studies policy learning for
distributing advice under a budget. Whereas most methods in the relevant
literature rely on heuristics for advice distribution we formulate the problem
as a learning one and propose a novel RL algorithm capable of learning when to
advise, adapting to the student and the task at hand. Furthermore, we argue
that learning to advise under a budget is an instance of a more generic
learning problem: Constrained Exploitation Reinforcement Learning.
| [
{
"version": "v1",
"created": "Fri, 28 Jul 2017 00:33:53 GMT"
}
] | 1,513,036,800,000 | [
[
"Fachantidis",
"Anestis",
""
],
[
"Taylor",
"Matthew E.",
""
],
[
"Vlahavas",
"Ioannis",
""
]
] |
1707.09324 | Sylwia Polberg | Sylwia Polberg and Anthony Hunter | Empirical Evaluation of Abstract Argumentation: Supporting the Need for
Bipolar and Probabilistic Approaches | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In dialogical argumentation it is often assumed that the involved parties
always correctly identify the intended statements posited by each other,
realize all of the associated relations, conform to the three acceptability
states (accepted, rejected, undecided), adjust their views when new and correct
information comes in, and that a framework handling only attack relations is
sufficient to represent their opinions. Although it is natural to make these
assumptions as a starting point for further research, removing them or even
acknowledging that such removal should happen is more challenging for some of
these concepts than for others. Probabilistic argumentation is one of the
approaches that can be harnessed for more accurate user modelling. The
epistemic approach allows us to represent how much a given argument is believed
by a given person, offering us the possibility to express more than just three
agreement states. It is equipped with a wide range of postulates, including
those that do not make any restrictions concerning how initial arguments should
be viewed, thus potentially being more adequate for handling beliefs of the
people that have not fully disclosed their opinions in comparison to Dung's
semantics. The constellation approach can be used to represent the views of
different people concerning the structure of the framework we are dealing with,
including cases in which not all relations are acknowledged or when they are
seen differently than intended. Finally, bipolar argumentation frameworks can
be used to express both positive and negative relations between arguments. In
this paper we describe the results of an experiment in which participants
judged dialogues in terms of agreement and structure. We compare our findings
with the aforementioned assumptions as well as with the constellation and
epistemic approaches to probabilistic argumentation and bipolar argumentation.
| [
{
"version": "v1",
"created": "Fri, 28 Jul 2017 16:51:00 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Dec 2017 14:38:56 GMT"
}
] | 1,512,950,400,000 | [
[
"Polberg",
"Sylwia",
""
],
[
"Hunter",
"Anthony",
""
]
] |
1707.09627 | Kevin Ellis | Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Joshua B. Tenenbaum | Learning to Infer Graphics Programs from Hand-Drawn Images | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a model that learns to convert simple hand drawings into
graphics programs written in a subset of \LaTeX. The model combines techniques
from deep learning and program synthesis. We learn a convolutional neural
network that proposes plausible drawing primitives that explain an image. These
drawing primitives are like a trace of the set of primitive commands issued by
a graphics program. We learn a model that uses program synthesis techniques to
recover a graphics program from that trace. These programs have constructs like
variable bindings, iterative loops, or simple kinds of conditionals. With a
graphics program in hand, we can correct errors made by the deep network,
measure similarity between drawings by use of similar high-level geometric
structures, and extrapolate drawings. Taken together these results are a step
towards agents that induce useful, human-readable programs from perceptual
input.
| [
{
"version": "v1",
"created": "Sun, 30 Jul 2017 14:46:14 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Aug 2017 00:34:18 GMT"
},
{
"version": "v3",
"created": "Tue, 29 Aug 2017 17:39:23 GMT"
},
{
"version": "v4",
"created": "Sun, 5 Nov 2017 17:47:27 GMT"
},
{
"version": "v5",
"created": "Fri, 26 Oct 2018 22:39:45 GMT"
}
] | 1,540,857,600,000 | [
[
"Ellis",
"Kevin",
""
],
[
"Ritchie",
"Daniel",
""
],
[
"Solar-Lezama",
"Armando",
""
],
[
"Tenenbaum",
"Joshua B.",
""
]
] |
1707.09661 | Michael Cook | Michael Cook | A Vision For Continuous Automated Game Design | Published in the proceedings of the Experimental AI in Games workshop
at AIIDE 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | ANGELINA is an automated game design system which has previously been built
as a single software block which designs games from start to finish. In this
paper we outline a roadmap for the development of a new version of ANGELINA,
designed to iterate on games in different ways to produce a continuous creative
process that will improve the quality of its work, but more importantly improve
the perception of the software as being an independently creative piece of
software. We provide an initial report of the system's structure here as well
as results from the first working module of the system.
| [
{
"version": "v1",
"created": "Sun, 30 Jul 2017 19:53:40 GMT"
}
] | 1,501,545,600,000 | [
[
"Cook",
"Michael",
""
]
] |
1707.09704 | Liang Zhou | Liang Zhou | Cost and Actual Causation | 37 pages, 2 Appendixes | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I propose the purpose our concept of actual causation serves is minimizing
various cost in intervention practice. Actual causation has three features:
nonredundant sufficiency, continuity and abnormality; these features correspond
to the minimization of exploitative cost, exploratory cost and risk cost in
intervention practice. Incorporating these three features, a definition of
actual causation is given. I test the definition in 66 causal cases from actual
causation literature and show that this definition's application fit intuition
better than some other causal modelling based definitions.
| [
{
"version": "v1",
"created": "Mon, 31 Jul 2017 03:02:44 GMT"
}
] | 1,501,545,600,000 | [
[
"Zhou",
"Liang",
""
]
] |
1708.00109 | Regis Riveret | Regis Riveret, Pietro Baroni, Yang Gao, Guido Governatori, Antonino
Rotolo, Giovanni Sartor | A Labelling Framework for Probabilistic Argumentation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The combination of argumentation and probability paves the way to new
accounts of qualitative and quantitative uncertainty, thereby offering new
theoretical and applicative opportunities. Due to a variety of interests,
probabilistic argumentation is approached in the literature with different
frameworks, pertaining to structured and abstract argumentation, and with
respect to diverse types of uncertainty, in particular the uncertainty on the
credibility of the premises, the uncertainty about which arguments to consider,
and the uncertainty on the acceptance status of arguments or statements.
Towards a general framework for probabilistic argumentation, we investigate a
labelling-oriented framework encompassing a basic setting for rule-based
argumentation and its (semi-) abstract account, along with diverse types of
uncertainty. Our framework provides a systematic treatment of various kinds of
uncertainty and of their relationships and allows us to back or question
assertions from the literature.
| [
{
"version": "v1",
"created": "Tue, 1 Aug 2017 00:12:58 GMT"
},
{
"version": "v2",
"created": "Fri, 9 Mar 2018 01:59:56 GMT"
}
] | 1,520,812,800,000 | [
[
"Riveret",
"Regis",
""
],
[
"Baroni",
"Pietro",
""
],
[
"Gao",
"Yang",
""
],
[
"Governatori",
"Guido",
""
],
[
"Rotolo",
"Antonino",
""
],
[
"Sartor",
"Giovanni",
""
]
] |
1708.00376 | Svetlin Penkov | Svetlin Penkov and Subramanian Ramamoorthy | Using Program Induction to Interpret Transition System Dynamics | Presented at 2017 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2017), Sydney, NSW, Australia. arXiv admin note: substantial
text overlap with arXiv:1705.08320 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explaining and reasoning about processes which underlie observed black-box
phenomena enables the discovery of causal mechanisms, derivation of suitable
abstract representations and the formulation of more robust predictions. We
propose to learn high level functional programs in order to represent abstract
models which capture the invariant structure in the observed data. We introduce
the $\pi$-machine (program-induction machine) -- an architecture able to induce
interpretable LISP-like programs from observed data traces. We propose an
optimisation procedure for program learning based on backpropagation, gradient
descent and A* search. We apply the proposed method to two problems: system
identification of dynamical systems and explaining the behaviour of a DQN
agent. Our results show that the $\pi$-machine can efficiently induce
interpretable programs from individual data traces.
| [
{
"version": "v1",
"created": "Wed, 26 Jul 2017 12:49:04 GMT"
}
] | 1,501,632,000,000 | [
[
"Penkov",
"Svetlin",
""
],
[
"Ramamoorthy",
"Subramanian",
""
]
] |
1708.00463 | Adam Earle | Adam C. Earle, Andrew M. Saxe, Benjamin Rosman | Hierarchical Subtask Discovery With Non-Negative Matrix Factorization | 7 pages, Accepted at Lifelong Learning: A Reinforcement Learning
Approach Workshop, ICML, Sydney, Australia, 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hierarchical reinforcement learning methods offer a powerful means of
planning flexible behavior in complicated domains. However, learning an
appropriate hierarchical decomposition of a domain into subtasks remains a
substantial challenge. We present a novel algorithm for subtask discovery,
based on the recently introduced multitask linearly-solvable Markov decision
process (MLMDP) framework. The MLMDP can perform never-before-seen tasks by
representing them as a linear combination of a previously learned basis set of
tasks. In this setting, the subtask discovery problem can naturally be posed as
finding an optimal low-rank approximation of the set of tasks the agent will
face in a domain. We use non-negative matrix factorization to discover this
minimal basis set of tasks, and show that the technique learns intuitive
decompositions in a variety of domains. Our method has several qualitatively
desirable features: it is not limited to learning subtasks with single goal
states, instead learning distributed patterns of preferred states; it learns
qualitatively different hierarchical decompositions in the same domain
depending on the ensemble of tasks the agent will face; and it may be
straightforwardly iterated to obtain deeper hierarchical decompositions.
| [
{
"version": "v1",
"created": "Tue, 1 Aug 2017 18:19:40 GMT"
}
] | 1,501,718,400,000 | [
[
"Earle",
"Adam C.",
""
],
[
"Saxe",
"Andrew M.",
""
],
[
"Rosman",
"Benjamin",
""
]
] |
1708.00543 | Sarath Sreedharan | Tathagata Chakraborti, Sarath Sreedharan and Subbarao Kambhampati | Balancing Explicability and Explanation in Human-Aware Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human aware planning requires an agent to be aware of the intentions,
capabilities and mental model of the human in the loop during its decision
process. This can involve generating plans that are explicable to a human
observer as well as the ability to provide explanations when such plans cannot
be generated. This has led to the notion "multi-model planning" which aim to
incorporate effects of human expectation in the deliberative process of a
planner - either in the form of explicable task planning or explanations
produced thereof. In this paper, we bring these two concepts together and show
how a planner can account for both these needs and achieve a trade-off during
the plan generation process itself by means of a model-space search method
MEGA. This in effect provides a comprehensive perspective of what it means for
a decision making agent to be "human-aware" by bringing together existing
principles of planning under the umbrella of a single plan generation process.
We situate our discussion specifically keeping in mind the recent work on
explicable planning and explanation generation, and illustrate these concepts
in modified versions of two well known planning domains, as well as a
demonstration on a robot involved in a typical search and reconnaissance task
with an external supervisor.
| [
{
"version": "v1",
"created": "Tue, 1 Aug 2017 22:47:42 GMT"
},
{
"version": "v2",
"created": "Sat, 3 Feb 2018 19:04:43 GMT"
}
] | 1,517,875,200,000 | [
[
"Chakraborti",
"Tathagata",
""
],
[
"Sreedharan",
"Sarath",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
1708.01035 | Charmgil Hong | Charmgil Hong, Siqi Liu, Milos Hauskrecht | Detection of Abnormal Input-Output Associations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study a novel outlier detection problem that aims to identify abnormal
input-output associations in data, whose instances consist of multi-dimensional
input (context) and output (responses) pairs. We present our approach that
works by analyzing data in the conditional (input--output) relation space,
captured by a decomposable probabilistic model. Experimental results
demonstrate the ability of our approach in identifying multivariate conditional
outliers.
| [
{
"version": "v1",
"created": "Thu, 3 Aug 2017 07:41:55 GMT"
}
] | 1,501,804,800,000 | [
[
"Hong",
"Charmgil",
""
],
[
"Liu",
"Siqi",
""
],
[
"Hauskrecht",
"Milos",
""
]
] |
1708.01791 | Ole-Christoffer Granmo | Sondre Glimsdal, Ole-Christoffer Granmo | Thompson Sampling Guided Stochastic Searching on the Line for Deceptive
Environments with Applications to Root-Finding Problems | 17 pages, 2 figures. A preliminary version of some of the results of
this paper appears in the Proceedings of AIAI'15 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The multi-armed bandit problem forms the foundation for solving a wide range
of on-line stochastic optimization problems through a simple, yet effective
mechanism. One simply casts the problem as a gambler that repeatedly pulls one
out of N slot machine arms, eliciting random rewards. Learning of reward
probabilities is then combined with reward maximization, by carefully balancing
reward exploration against reward exploitation. In this paper, we address a
particularly intriguing variant of the multi-armed bandit problem, referred to
as the {\it Stochastic Point Location (SPL) Problem}. The gambler is here only
told whether the optimal arm (point) lies to the "left" or to the "right" of
the arm pulled, with the feedback being erroneous with probability $1-\pi$.
This formulation thus captures optimization in continuous action spaces with
both {\it informative} and {\it deceptive} feedback. To tackle this class of
problems, we formulate a compact and scalable Bayesian representation of the
solution space that simultaneously captures both the location of the optimal
arm as well as the probability of receiving correct feedback. We further
introduce the accompanying Thompson Sampling guided Stochastic Point Location
(TS-SPL) scheme for balancing exploration against exploitation. By learning
$\pi$, TS-SPL also supports {\it deceptive} environments that are lying about
the direction of the optimal arm. This, in turn, allows us to solve the
fundamental Stochastic Root Finding (SRF) Problem. Empirical results
demonstrate that our scheme deals with both deceptive and informative
environments, significantly outperforming competing algorithms both for SRF and
SPL.
| [
{
"version": "v1",
"created": "Sat, 5 Aug 2017 17:23:01 GMT"
}
] | 1,502,150,400,000 | [
[
"Glimsdal",
"Sondre",
""
],
[
"Granmo",
"Ole-Christoffer",
""
]
] |
1708.02139 | Zeming Lin | Zeming Lin, Jonas Gehring, Vasil Khalidov, Gabriel Synnaeve | STARDATA: A StarCraft AI Research Dataset | To be presented at AIIDE17 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We release a dataset of 65646 StarCraft replays that contains 1535 million
frames and 496 million player actions. We provide full game state data along
with the original replays that can be viewed in StarCraft. The game state data
was recorded every 3 frames which ensures suitability for a wide variety of
machine learning tasks such as strategy classification, inverse reinforcement
learning, imitation learning, forward modeling, partial information extraction,
and others. We use TorchCraft to extract and store the data, which standardizes
the data format for both reading from replays and reading directly from the
game. Furthermore, the data can be used on different operating systems and
platforms. The dataset contains valid, non-corrupted replays only and its
quality and diversity was ensured by a number of heuristics. We illustrate the
diversity of the data with various statistics and provide examples of tasks
that benefit from the dataset. We make the dataset available at
https://github.com/TorchCraft/StarData . En Taro Adun!
| [
{
"version": "v1",
"created": "Mon, 7 Aug 2017 14:47:47 GMT"
}
] | 1,506,988,800,000 | [
[
"Lin",
"Zeming",
""
],
[
"Gehring",
"Jonas",
""
],
[
"Khalidov",
"Vasil",
""
],
[
"Synnaeve",
"Gabriel",
""
]
] |
1708.02153 | Jakub Sliwinski | Jakub Sliwinski, Martin Strobel, Yair Zick | Axiomatic Characterization of Data-Driven Influence Measures for
Classification | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the following problem: given a labeled dataset and a specific
datapoint x, how did the i-th feature influence the classification for x? We
identify a family of numerical influence measures - functions that, given a
datapoint x, assign a numeric value phi_i(x) to every feature i, corresponding
to how altering i's value would influence the outcome for x. This family, which
we term monotone influence measures (MIM), is uniquely derived from a set of
desirable properties, or axioms. The MIM family constitutes a provably sound
methodology for measuring feature influence in classification domains; the
values generated by MIM are based on the dataset alone, and do not make any
queries to the classifier. While this requirement naturally limits the scope of
our framework, we demonstrate its effectiveness on data.
| [
{
"version": "v1",
"created": "Mon, 7 Aug 2017 15:09:01 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Nov 2018 09:35:51 GMT"
}
] | 1,542,326,400,000 | [
[
"Sliwinski",
"Jakub",
""
],
[
"Strobel",
"Martin",
""
],
[
"Zick",
"Yair",
""
]
] |
1708.02378 | David Von Dollen | David Von Dollen | Investigating Reinforcement Learning Agents for Continuous State Space
Environments | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given an environment with continuous state spaces and discrete actions, we
investigate using a Double Deep Q-learning Reinforcement Agent to find optimal
policies using the LunarLander-v2 OpenAI gym environment.
| [
{
"version": "v1",
"created": "Tue, 8 Aug 2017 05:44:12 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Aug 2017 22:16:08 GMT"
},
{
"version": "v3",
"created": "Mon, 11 Mar 2019 20:17:09 GMT"
}
] | 1,552,435,200,000 | [
[
"Von Dollen",
"David",
""
]
] |
1708.02838 | Pieter Van Molle | Pieter Van Molle, Tim Verbelen, Steven Bohez, Sam Leroux, Pieter
Simoens, Bart Dhoedt | Decoupled Learning of Environment Characteristics for Safe Exploration | 4 pages, 4 figures, ICML 2017 workshop on Reliable Machine Learning
in the Wild | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning is a proven technique for an agent to learn a task.
However, when learning a task using reinforcement learning, the agent cannot
distinguish the characteristics of the environment from those of the task. This
makes it harder to transfer skills between tasks in the same environment.
Furthermore, this does not reduce risk when training for a new task. In this
paper, we introduce an approach to decouple the environment characteristics
from the task-specific ones, allowing an agent to develop a sense of survival.
We evaluate our approach in an environment where an agent must learn a sequence
of collection tasks, and show that decoupled learning allows for a safer
utilization of prior knowledge.
| [
{
"version": "v1",
"created": "Wed, 9 Aug 2017 13:51:47 GMT"
}
] | 1,502,323,200,000 | [
[
"Van Molle",
"Pieter",
""
],
[
"Verbelen",
"Tim",
""
],
[
"Bohez",
"Steven",
""
],
[
"Leroux",
"Sam",
""
],
[
"Simoens",
"Pieter",
""
],
[
"Dhoedt",
"Bart",
""
]
] |
1708.02851 | Anthony Hunter | Anthony Hunter | Measuring Inconsistency in Argument Graphs | 29 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There have been a number of developments in measuring inconsistency in
logic-based representations of knowledge. In contrast, the development of
inconsistency measures for computational models of argument has been limited.
To address this shortcoming, this paper provides a general framework for
measuring inconsistency in abstract argumentation, together with some proposals
for specific measures, and a consideration of measuring inconsistency in
logic-based instantiations of argument graphs, including a review of some
existing proposals and a consideration of how existing logic-based measures of
inconsistency can be applied.
| [
{
"version": "v1",
"created": "Wed, 9 Aug 2017 14:02:51 GMT"
}
] | 1,502,323,200,000 | [
[
"Hunter",
"Anthony",
""
]
] |
1708.03019 | Lavindra de Silva | Lavindra de Silva and Sebastian Sardina and Lin Padgham | Addendum to: Summary Information for Reasoning About Hierarchical Plans | This paper is a more detailed version of the following publication:
Lavindra de Silva, Sebastian Sardina, Lin Padgham: Summary Information for
Reasoning About Hierarchical Plans. ECAI 2016: 1300-1308 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hierarchically structured agent plans are important for efficient planning
and acting, and they also serve (among other things) to produce "richer"
classical plans, composed not just of a sequence of primitive actions, but also
"abstract" ones representing the supplied hierarchies. A crucial step for this
and other approaches is deriving precondition and effect "summaries" from a
given plan hierarchy. This paper provides mechanisms to do this for more
pragmatic and conventional hierarchies than in the past. To this end, we
formally define the notion of a precondition and an effect for a hierarchical
plan; we present data structures and algorithms for automatically deriving this
information; and we analyse the properties of the presented algorithms. We
conclude the paper by detailing how our algorithms may be used together with a
classical planner in order to obtain abstract plans.
| [
{
"version": "v1",
"created": "Wed, 9 Aug 2017 21:27:29 GMT"
}
] | 1,502,409,600,000 | [
[
"de Silva",
"Lavindra",
""
],
[
"Sardina",
"Sebastian",
""
],
[
"Padgham",
"Lin",
""
]
] |
1708.03209 | Thomas C King | Thomas C. King, Ak{\i}n G\"unay, Amit K. Chopra, Munindar P. Singh | Tosca: Operationalizing Commitments Over Information Protocols | null | null | 10.24963/ijcai.2017/37 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The notion of commitment is widely studied as a high-level abstraction for
modeling multiagent interaction. An important challenge is supporting flexible
decentralized enactments of commitment specifications. In this paper, we
combine recent advances on specifying commitments and information protocols.
Specifically, we contribute Tosca, a technique for automatically synthesizing
information protocols from commitment specifications. Our main result is that
the synthesized protocols support commitment alignment, which is the idea that
agents must make compatible inferences about their commitments despite
decentralization.
| [
{
"version": "v1",
"created": "Thu, 10 Aug 2017 13:39:59 GMT"
}
] | 1,502,409,600,000 | [
[
"King",
"Thomas C.",
""
],
[
"Günay",
"Akın",
""
],
[
"Chopra",
"Amit K.",
""
],
[
"Singh",
"Munindar P.",
""
]
] |
1708.03310 | Sudip Mittal | Sudip Mittal, Anupam Joshi, Tim Finin | Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graphs and vector space models are robust knowledge representation
techniques with individual strengths and weaknesses. Vector space models excel
at determining similarity between concepts, but are severely constrained when
evaluating complex dependency relations and other logic-based operations that
are a strength of knowledge graphs. We describe the VKG structure that helps
unify knowledge graphs and vector representation of entities, and enables
powerful inference methods and search capabilities that combine their
complementary strengths. We analogize this to thinking `fast' in vector space
along with thinking 'slow' and `deeply' by reasoning over the knowledge graph.
We have created a query processing engine that takes complex queries and
decomposes them into subqueries optimized to run on the respective knowledge
graph or vector view of a VKG. We show that the VKG structure can process
specific queries that are not efficiently handled by vector spaces or knowledge
graphs alone. We also demonstrate and evaluate the VKG structure and the query
processing engine by developing a system called Cyber-All-Intel for knowledge
extraction, representation and querying in an end-to-end pipeline grounded in
the cybersecurity informatics domain.
| [
{
"version": "v1",
"created": "Thu, 10 Aug 2017 17:39:55 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Aug 2017 01:49:05 GMT"
}
] | 1,503,360,000,000 | [
[
"Mittal",
"Sudip",
""
],
[
"Joshi",
"Anupam",
""
],
[
"Finin",
"Tim",
""
]
] |
1708.04196 | Kiana Roshan Zamir | Kiana Roshan Zamir, Ali Shafahi, Ali Haghani | Understanding and Visualizing the District of Columbia Capital Bikeshare
System Using Data Analysis for Balancing Purposes | Submitted to TRB2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bike sharing systems' popularity has consistently been rising during the past
years. Managing and maintaining these emerging systems are indispensable parts
of these systems. Visualizing the current operations can assist in getting a
better grasp on the performance of the system. In this paper, a data mining
approach is used to identify and visualize some important factors related to
bike-share operations and management. To consolidate the data, we cluster
stations that have a similar pickup and drop-off profiles during weekdays and
weekends. We provide the temporal profile of the center of each cluster which
can be used as a simple and practical approach for approximating the number of
pickups and drop-offs of the stations. We also define two indices based on
stations' shortages and surpluses that reflect the degree of balancing aid a
station needs. These indices can help stakeholders improve the quality of the
bike-share user experience in at-least two ways. It can act as a complement to
balancing optimization efforts, and it can identify stations that need
expansion. We mine the District of Columbia's regional bike-share data and
discuss the findings of this data set. We examine the bike-share system during
different quarters of the year and during both peak and non-peak hours.
Findings reflect that on weekdays most of the pickups and drop-offs happen
during the morning and evening peaks whereas on weekends pickups and drop-offs
are spread out throughout the day. We also show that throughout the day, more
than 40% of the stations are relatively self-balanced. Not worrying about these
stations during ordinary days can allow the balancing efforts to focus on a
fewer stations and therefore potentially improve the efficiency of the
balancing optimization models.
| [
{
"version": "v1",
"created": "Mon, 14 Aug 2017 16:16:24 GMT"
}
] | 1,502,755,200,000 | [
[
"Zamir",
"Kiana Roshan",
""
],
[
"Shafahi",
"Ali",
""
],
[
"Haghani",
"Ali",
""
]
] |
1708.04352 | Peter Henderson | Peter Henderson, Wei-Di Chang, Florian Shkurti, Johanna Hansen, David
Meger, Gregory Dudek | Benchmark Environments for Multitask Learning in Continuous Domains | Accepted at Lifelong Learning: A Reinforcement Learning Approach
Workshop @ ICML, Sydney, Australia, 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As demand drives systems to generalize to various domains and problems, the
study of multitask, transfer and lifelong learning has become an increasingly
important pursuit. In discrete domains, performance on the Atari game suite has
emerged as the de facto benchmark for assessing multitask learning. However, in
continuous domains there is a lack of agreement on standard multitask
evaluation environments which makes it difficult to compare different
approaches fairly. In this work, we describe a benchmark set of tasks that we
have developed in an extendable framework based on OpenAI Gym. We run a simple
baseline using Trust Region Policy Optimization and release the framework
publicly to be expanded and used for the systematic comparison of multitask,
transfer, and lifelong learning in continuous domains.
| [
{
"version": "v1",
"created": "Mon, 14 Aug 2017 22:55:03 GMT"
}
] | 1,502,841,600,000 | [
[
"Henderson",
"Peter",
""
],
[
"Chang",
"Wei-Di",
""
],
[
"Shkurti",
"Florian",
""
],
[
"Hansen",
"Johanna",
""
],
[
"Meger",
"David",
""
],
[
"Dudek",
"Gregory",
""
]
] |
1708.04806 | Kieran Greer Dr | Kieran Greer | New Ideas for Brain Modelling 4 | null | BRAIN. Broad Research in Artificial Intelligence and Neuroscience,
Vol. 9, No. 2, pp. 155-167. ISSN 2067-3957 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper continues the research that considers a new cognitive model based
strongly on the human brain. In particular, it considers the neural binding
structure of an earlier paper. It also describes some new methods in the areas
of image processing and behaviour simulation. The work is all based on earlier
research by the author and the new additions are intended to fit in with the
overall design. For image processing, a grid-like structure is used with 'full
linking'. Each cell in the classifier grid stores a list of all other cells it
gets associated with and this is used as the learned image that new input is
compared to. For the behaviour metric, a new prediction equation is suggested,
as part of a simulation, that uses feedback and history to dynamically
determine its course of action. While the new methods are from widely different
topics, both can be compared with the binary-analog type of interface that is
the main focus of the paper. It is suggested that the simplest of linking
between a tree and ensemble can explain neural binding and variable signal
strengths.
| [
{
"version": "v1",
"created": "Wed, 16 Aug 2017 08:32:03 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Sep 2017 13:41:41 GMT"
},
{
"version": "v3",
"created": "Wed, 28 Feb 2018 21:19:44 GMT"
},
{
"version": "v4",
"created": "Mon, 12 Mar 2018 15:51:06 GMT"
}
] | 1,544,486,400,000 | [
[
"Greer",
"Kieran",
""
]
] |
1708.04846 | Jun Mei | Jun Mei, Yong Jiang, Kewei Tu | Maximum A Posteriori Inference in Sum-Product Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sum-product networks (SPNs) are a class of probabilistic graphical models
that allow tractable marginal inference. However, the maximum a posteriori
(MAP) inference in SPNs is NP-hard. We investigate MAP inference in SPNs from
both theoretical and algorithmic perspectives. For the theoretical part, we
reduce general MAP inference to its special case without evidence and hidden
variables; we also show that it is NP-hard to approximate the MAP problem to
$2^{n^\epsilon}$ for fixed $0 \leq \epsilon < 1$, where $n$ is the input size.
For the algorithmic part, we first present an exact MAP solver that runs
reasonably fast and could handle SPNs with up to 1k variables and 150k arcs in
our experiments. We then present a new approximate MAP solver with a good
balance between speed and accuracy, and our comprehensive experiments on
real-world datasets show that it has better overall performance than existing
approximate solvers.
| [
{
"version": "v1",
"created": "Wed, 16 Aug 2017 11:05:48 GMT"
},
{
"version": "v2",
"created": "Fri, 18 Aug 2017 07:07:01 GMT"
},
{
"version": "v3",
"created": "Mon, 20 Nov 2017 03:08:16 GMT"
}
] | 1,511,222,400,000 | [
[
"Mei",
"Jun",
""
],
[
"Jiang",
"Yong",
""
],
[
"Tu",
"Kewei",
""
]
] |
1708.04927 | Mark Stalzer | Mark A. Stalzer and Chao Ju | TheoSea: Marching Theory to Light | 8 pages, 3 figures. arXiv admin note: text overlap with
arXiv:1706.06975 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is sufficient information in the far-field of a radiating dipole
antenna to rediscover the Maxwell Equations and the wave equations of light,
including the speed of light $c.$ TheoSea is a Julia program that does this in
about a second, and the key insight is that the compactness of theories drives
the search. The program is a computational embodiment of the scientific method:
observation, consideration of candidate theories, and validation.
| [
{
"version": "v1",
"created": "Mon, 14 Aug 2017 22:06:49 GMT"
}
] | 1,502,928,000,000 | [
[
"Stalzer",
"Mark A.",
""
],
[
"Ju",
"Chao",
""
]
] |
1708.04983 | Andrey Boytsov | Andrey Boytsov, Francois Fouquet, Thomas Hartmann, and Yves LeTraon | Visualizing and Exploring Dynamic High-Dimensional Datasets with
LION-tSNE | 44 pages, 24 figures, 7 tables, planned for submission | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | T-distributed stochastic neighbor embedding (tSNE) is a popular and
prize-winning approach for dimensionality reduction and visualizing
high-dimensional data. However, tSNE is non-parametric: once visualization is
built, tSNE is not designed to incorporate additional data into existing
representation. It highly limits the applicability of tSNE to the scenarios
where data are added or updated over time (like dashboards or series of data
snapshots).
In this paper we propose, analyze and evaluate LION-tSNE (Local Interpolation
with Outlier coNtrol) - a novel approach for incorporating new data into tSNE
representation. LION-tSNE is based on local interpolation in the vicinity of
training data, outlier detection and a special outlier mapping algorithm. We
show that LION-tSNE method is robust both to outliers and to new samples from
existing clusters. We also discuss multiple possible improvements for special
cases.
We compare LION-tSNE to a comprehensive list of possible benchmark approaches
that include multiple interpolation techniques, gradient descent for new data,
and neural network approximation.
| [
{
"version": "v1",
"created": "Wed, 16 Aug 2017 17:17:56 GMT"
}
] | 1,502,928,000,000 | [
[
"Boytsov",
"Andrey",
""
],
[
"Fouquet",
"Francois",
""
],
[
"Hartmann",
"Thomas",
""
],
[
"LeTraon",
"Yves",
""
]
] |
1708.05263 | Lucas Bechberger | Lucas Bechberger | The Size of a Hyperball in a Conceptual Space | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The cognitive framework of conceptual spaces [3] provides geometric means for
representing knowledge. A conceptual space is a high-dimensional space whose
dimensions are partitioned into so-called domains. Within each domain, the
Euclidean metric is used to compute distances. Distances in the overall space
are computed by applying the Manhattan metric to the intra-domain distances.
Instances are represented as points in this space and concepts are represented
by regions. In this paper, we derive a formula for the size of a hyperball
under the combined metric of a conceptual space. One can think of such a
hyperball as the set of all points having a certain minimal similarity to the
hyperball's center.
| [
{
"version": "v1",
"created": "Tue, 4 Jul 2017 10:13:51 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Sep 2017 09:10:06 GMT"
},
{
"version": "v3",
"created": "Mon, 18 Sep 2017 11:37:45 GMT"
},
{
"version": "v4",
"created": "Fri, 22 Sep 2017 12:44:03 GMT"
}
] | 1,506,297,600,000 | [
[
"Bechberger",
"Lucas",
""
]
] |
1708.05296 | Alex Fukunaga | Alex Fukunaga, Adi Botea, Yuu Jinnai and Akihiro Kishimoto | A Survey of Parallel A* | arXiv admin note: text overlap with arXiv:1201.3204 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A* is a best-first search algorithm for finding optimal-cost paths in graphs.
A* benefits significantly from parallelism because in many applications, A* is
limited by memory usage, so distributed memory implementations of A* that use
all of the aggregate memory on the cluster enable problems that can not be
solved by serial, single-machine implementations to be solved. We survey
approaches to parallel A*, focusing on decentralized approaches to A* which
partition the state space among processors. We also survey approaches to
parallel, limited-memory variants of A* such as parallel IDA*.
| [
{
"version": "v1",
"created": "Wed, 16 Aug 2017 01:45:40 GMT"
}
] | 1,503,014,400,000 | [
[
"Fukunaga",
"Alex",
""
],
[
"Botea",
"Adi",
""
],
[
"Jinnai",
"Yuu",
""
],
[
"Kishimoto",
"Akihiro",
""
]
] |
1708.05346 | Jan Feyereisl | Jan Feyereisl, Matej Nikl, Martin Poliak, Martin Stransky, Michal
Vlasak | General AI Challenge - Round One: Gradual Learning | Presented as keynote talk at IJCAI Workshop on Evaluating
General-Purpose AI (EGPAI 2017) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The General AI Challenge is an initiative to encourage the wider artificial
intelligence community to focus on important problems in building intelligent
machines with more general scope than is currently possible. The challenge
comprises of multiple rounds, with the first round focusing on gradual
learning, i.e. the ability to re-use already learned knowledge for efficiently
learning to solve subsequent problems. In this article, we will present details
of the first round of the challenge, its inspiration and aims. We also outline
a more formal description of the challenge and present a preliminary analysis
of its curriculum, based on ideas from computational mechanics. We believe,
that such formalism will allow for a more principled approach towards
investigating tasks in the challenge, building new curricula and for
potentially improving consequent challenge rounds.
| [
{
"version": "v1",
"created": "Thu, 17 Aug 2017 16:10:58 GMT"
}
] | 1,503,014,400,000 | [
[
"Feyereisl",
"Jan",
""
],
[
"Nikl",
"Matej",
""
],
[
"Poliak",
"Martin",
""
],
[
"Stransky",
"Martin",
""
],
[
"Vlasak",
"Michal",
""
]
] |
1708.05448 | Philip Thomas | Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto, and Emma
Brunskill | On Ensuring that Intelligent Machines Are Well-Behaved | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning algorithms are everywhere, ranging from simple data analysis
and pattern recognition tools used across the sciences to complex systems that
achieve super-human performance on various tasks. Ensuring that they are
well-behaved---that they do not, for example, cause harm to humans or act in a
racist or sexist way---is therefore not a hypothetical problem to be dealt with
in the future, but a pressing one that we address here. We propose a new
framework for designing machine learning algorithms that simplifies the problem
of specifying and regulating undesirable behaviors. To show the viability of
this new framework, we use it to create new machine learning algorithms that
preclude the sexist and harmful behaviors exhibited by standard machine
learning algorithms in our experiments. Our framework for designing machine
learning algorithms simplifies the safe and responsible application of machine
learning.
| [
{
"version": "v1",
"created": "Thu, 17 Aug 2017 21:53:47 GMT"
}
] | 1,503,273,600,000 | [
[
"Thomas",
"Philip S.",
""
],
[
"da Silva",
"Bruno Castro",
""
],
[
"Barto",
"Andrew G.",
""
],
[
"Brunskill",
"Emma",
""
]
] |
1708.05522 | Shufeng Kong | Shufeng Kong, Sanjiang Li, Michael Sioutis | Exploring Directional Path-Consistency for Solving Constraint Networks | null | null | 10.1093/comjnl/bxx122 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Among the local consistency techniques used for solving constraint networks,
path-consistency (PC) has received a great deal of attention. However,
enforcing PC is computationally expensive and sometimes even unnecessary.
Directional path-consistency (DPC) is a weaker notion of PC that considers a
given variable ordering and can thus be enforced more efficiently than PC. This
paper shows that DPC (the DPC enforcing algorithm of Dechter and Pearl) decides
the constraint satisfaction problem (CSP) of a constraint language if it is
complete and has the variable elimination property (VEP). However, we also show
that no complete VEP constraint language can have a domain with more than 2
values. We then present a simple variant of the DPC algorithm, called DPC*, and
show that the CSP of a constraint language can be decided by DPC* if it is
closed under a majority operation. In fact, DPC* is sufficient for guaranteeing
backtrack-free search for such constraint networks. Examples of majority-closed
constraint classes include the classes of connected row-convex (CRC)
constraints and tree-preserving constraints, which have found applications in
various domains, such as scene labeling, temporal reasoning, geometric
reasoning, and logical filtering. Our experimental evaluations show that DPC*
significantly outperforms the state-of-the-art algorithms for solving
majority-closed constraints.
| [
{
"version": "v1",
"created": "Fri, 18 Aug 2017 07:06:23 GMT"
}
] | 1,524,528,000,000 | [
[
"Kong",
"Shufeng",
""
],
[
"Li",
"Sanjiang",
""
],
[
"Sioutis",
"Michael",
""
]
] |
1708.05824 | Yu Zhao | Yu Zhao, Rennong Yang, Guillaume Chevalier, Rajiv Shah, Rob Romijnders | Applying Deep Bidirectional LSTM and Mixture Density Network for
Basketball Trajectory Prediction | null | null | 10.1016/j.ijleo.2017.12.038 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data analytics helps basketball teams to create tactics. However, manual data
collection and analytics are costly and ineffective. Therefore, we applied a
deep bidirectional long short-term memory (BLSTM) and mixture density network
(MDN) approach. This model is not only capable of predicting a basketball
trajectory based on real data, but it also can generate new trajectory samples.
It is an excellent application to help coaches and players decide when and
where to shoot. Its structure is particularly suitable for dealing with time
series problems. BLSTM receives forward and backward information at the same
time, while stacking multiple BLSTMs further increases the learning ability of
the model. Combined with BLSTMs, MDN is used to generate a multi-modal
distribution of outputs. Thus, the proposed model can, in principle, represent
arbitrary conditional probability distributions of output variables. We tested
our model with two experiments on three-pointer datasets from NBA SportVu data.
In the hit-or-miss classification experiment, the proposed model outperformed
other models in terms of the convergence speed and accuracy. In the trajectory
generation experiment, eight model-generated trajectories at a given time
closely matched real trajectories.
| [
{
"version": "v1",
"created": "Sat, 19 Aug 2017 08:36:12 GMT"
}
] | 1,518,566,400,000 | [
[
"Zhao",
"Yu",
""
],
[
"Yang",
"Rennong",
""
],
[
"Chevalier",
"Guillaume",
""
],
[
"Shah",
"Rajiv",
""
],
[
"Romijnders",
"Rob",
""
]
] |
1708.05930 | Longfei Wang | Haoyuan Hu, Xiaodong Zhang, Xiaowei Yan, Longfei Wang, Yinghui Xu | Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning
Method | 7 pages, 1 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a new type of 3D bin packing problem (BPP) is proposed, in
which a number of cuboid-shaped items must be put into a bin one by one
orthogonally. The objective is to find a way to place these items that can
minimize the surface area of the bin. This problem is based on the fact that
there is no fixed-sized bin in many real business scenarios and the cost of a
bin is proportional to its surface area. Our research shows that this problem
is NP-hard. Based on previous research on 3D BPP, the surface area is
determined by the sequence, spatial locations and orientations of items. Among
these factors, the sequence of items plays a key role in minimizing the surface
area. Inspired by recent achievements of deep reinforcement learning (DRL)
techniques, especially Pointer Network, on combinatorial optimization problems
such as TSP, a DRL-based method is applied to optimize the sequence of items to
be packed into the bin. Numerical results show that the method proposed in this
paper achieve about 5% improvement than heuristic method.
| [
{
"version": "v1",
"created": "Sun, 20 Aug 2017 03:53:04 GMT"
}
] | 1,503,360,000,000 | [
[
"Hu",
"Haoyuan",
""
],
[
"Zhang",
"Xiaodong",
""
],
[
"Yan",
"Xiaowei",
""
],
[
"Wang",
"Longfei",
""
],
[
"Xu",
"Yinghui",
""
]
] |
1708.06816 | Bhushan Kotnis | Bhushan Kotnis and Vivi Nastase | Analysis of the Impact of Negative Sampling on Link Prediction in
Knowledge Graphs | 14 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graphs are large, useful, but incomplete knowledge repositories.
They encode knowledge through entities and relations which define each other
through the connective structure of the graph. This has inspired methods for
the joint embedding of entities and relations in continuous low-dimensional
vector spaces, that can be used to induce new edges in the graph, i.e., link
prediction in knowledge graphs. Learning these representations relies on
contrasting positive instances with negative ones. Knowledge graphs include
only positive relation instances, leaving the door open for a variety of
methods for selecting negative examples. In this paper we present an empirical
study on the impact of negative sampling on the learned embeddings, assessed
through the task of link prediction. We use state-of-the-art knowledge graph
embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on
benchmark datasets -- FB15k and WN18. We compare well known methods for
negative sampling and additionally propose embedding based sampling methods. We
note a marked difference in the impact of these sampling methods on the two
datasets, with the "traditional" corrupting positives method leading to best
results on WN18, while embedding based methods benefiting the task on FB15k.
| [
{
"version": "v1",
"created": "Tue, 22 Aug 2017 20:53:29 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Mar 2018 12:27:10 GMT"
}
] | 1,520,208,000,000 | [
[
"Kotnis",
"Bhushan",
""
],
[
"Nastase",
"Vivi",
""
]
] |
1708.07129 | Siamak Yousefi mr | Siamak Yousefi, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, Shahrokh
Valaee | A Survey of Human Activity Recognition Using WiFi CSI | 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we present a survey of recent advances in passive human
behaviour recognition in indoor areas using the channel state information (CSI)
of commercial WiFi systems. Movement of human body causes a change in the
wireless signal reflections, which results in variations in the CSI. By
analyzing the data streams of CSIs for different activities and comparing them
against stored models, human behaviour can be recognized. This is done by
extracting features from CSI data streams and using machine learning techniques
to build models and classifiers. The techniques from the literature that are
presented herein have great performances, however, instead of the machine
learning techniques employed in these works, we propose to use deep learning
techniques such as long-short term memory (LSTM) recurrent neural network
(RNN), and show the improved performance. We also discuss about different
challenges such as environment change, frame rate selection, and multi-user
scenario, and suggest possible directions for future work.
| [
{
"version": "v1",
"created": "Wed, 23 Aug 2017 18:00:05 GMT"
}
] | 1,503,619,200,000 | [
[
"Yousefi",
"Siamak",
""
],
[
"Narui",
"Hirokazu",
""
],
[
"Dayal",
"Sankalp",
""
],
[
"Ermon",
"Stefano",
""
],
[
"Valaee",
"Shahrokh",
""
]
] |
1708.07280 | Edward Groshev | Edward Groshev, Maxwell Goldstein, Aviv Tamar, Siddharth Srivastava,
Pieter Abbeel | Learning Generalized Reactive Policies using Deep Neural Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3.
| [
{
"version": "v1",
"created": "Thu, 24 Aug 2017 05:24:36 GMT"
},
{
"version": "v2",
"created": "Sun, 29 Apr 2018 08:30:18 GMT"
},
{
"version": "v3",
"created": "Wed, 25 Jul 2018 01:54:26 GMT"
}
] | 1,532,563,200,000 | [
[
"Groshev",
"Edward",
""
],
[
"Goldstein",
"Maxwell",
""
],
[
"Tamar",
"Aviv",
""
],
[
"Srivastava",
"Siddharth",
""
],
[
"Abbeel",
"Pieter",
""
]
] |
1708.07775 | Jing Wang | Jing Wang | Subspace Approximation for Approximate Nearest Neighbor Search in NLP | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most natural language processing tasks can be formulated as the approximated
nearest neighbor search problem, such as word analogy, document similarity,
machine translation. Take the question-answering task as an example, given a
question as the query, the goal is to search its nearest neighbor in the
training dataset as the answer. However, existing methods for approximate
nearest neighbor search problem may not perform well owing to the following
practical challenges: 1) there are noise in the data; 2) the large scale
dataset yields a huge retrieval space and high search time complexity.
In order to solve these problems, we propose a novel approximate nearest
neighbor search framework which i) projects the data to a subspace based
spectral analysis which eliminates the influence of noise; ii) partitions the
training dataset to different groups in order to reduce the search space.
Specifically, the retrieval space is reduced from $O(n)$ to $O(\log n)$ (where
$n$ is the number of data points in the training dataset). We prove that the
retrieved nearest neighbor in the projected subspace is the same as the one in
the original feature space. We demonstrate the outstanding performance of our
framework on real-world natural language processing tasks.
| [
{
"version": "v1",
"created": "Fri, 25 Aug 2017 15:26:15 GMT"
}
] | 1,503,878,400,000 | [
[
"Wang",
"Jing",
""
]
] |
1708.07867 | Chen Luo | Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun
Li | Accelerating Dependency Graph Learning from Heterogeneous Categorical
Event Streams via Knowledge Transfer | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dependency graph, as a heterogeneous graph representing the intrinsic
relationships between different pairs of system entities, is essential to many
data analysis applications, such as root cause diagnosis, intrusion detection,
etc. Given a well-trained dependency graph from a source domain and an immature
dependency graph from a target domain, how can we extract the entity and
dependency knowledge from the source to enhance the target? One way is to
directly apply a mature dependency graph learned from a source domain to the
target domain. But due to the domain variety problem, directly using the source
dependency graph often can not achieve good performance. Traditional transfer
learning methods mainly focus on numerical data and are not applicable.
In this paper, we propose ACRET, a knowledge transfer based model for
accelerating dependency graph learning from heterogeneous categorical event
streams. In particular, we first propose an entity estimation model to filter
out irrelevant entities from the source domain based on entity embedding and
manifold learning. Only the entities with statistically high correlations are
transferred to the target domain. On the surviving entities, we propose a
dependency construction model for constructing the unbiased dependency
relationships by solving a two-constraint optimization problem. The
experimental results on synthetic and real-world datasets demonstrate the
effectiveness and efficiency of ACRET. We also apply ACRET to a real enterprise
security system for intrusion detection. Our method is able to achieve superior
detection performance at least 20 days lead lag time in advance with more than
70% accuracy.
| [
{
"version": "v1",
"created": "Fri, 25 Aug 2017 19:24:27 GMT"
}
] | 1,503,964,800,000 | [
[
"Luo",
"Chen",
""
],
[
"Chen",
"Zhengzhang",
""
],
[
"Tang",
"Lu-An",
""
],
[
"Shrivastava",
"Anshumali",
""
],
[
"Li",
"Zhichun",
""
]
] |
1708.07902 | Niels Justesen | Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi | Deep Learning for Video Game Playing | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards.
| [
{
"version": "v1",
"created": "Fri, 25 Aug 2017 22:01:09 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Oct 2017 20:46:01 GMT"
},
{
"version": "v3",
"created": "Mon, 18 Feb 2019 15:43:22 GMT"
}
] | 1,550,534,400,000 | [
[
"Justesen",
"Niels",
""
],
[
"Bontrager",
"Philip",
""
],
[
"Togelius",
"Julian",
""
],
[
"Risi",
"Sebastian",
""
]
] |
1708.07938 | Kui Zhao | Kui Zhao, Xia Hu, Jiajun Bu, Can Wang | Deep Style Match for Complementary Recommendation | Workshops at the Thirty-First AAAI Conference on Artificial
Intelligence | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans develop a common sense of style compatibility between items based on
their attributes. We seek to automatically answer questions like "Does this
shirt go well with that pair of jeans?" In order to answer these kinds of
questions, we attempt to model human sense of style compatibility in this
paper. The basic assumption of our approach is that most of the important
attributes for a product in an online store are included in its title
description. Therefore it is feasible to learn style compatibility from these
descriptions. We design a Siamese Convolutional Neural Network architecture and
feed it with title pairs of items, which are either compatible or incompatible.
Those pairs will be mapped from the original space of symbolic words into some
embedded style space. Our approach takes only words as the input with few
preprocessing and there is no laborious and expensive feature engineering.
| [
{
"version": "v1",
"created": "Sat, 26 Aug 2017 06:09:53 GMT"
}
] | 1,503,964,800,000 | [
[
"Zhao",
"Kui",
""
],
[
"Hu",
"Xia",
""
],
[
"Bu",
"Jiajun",
""
],
[
"Wang",
"Can",
""
]
] |
1708.09032 | Andrew MacFie | Andrew MacFie | Plausibility and probability in deductive reasoning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of rational uncertainty about unproven mathematical
statements, remarked on by G\"odel and others. Using Bayesian-inspired
arguments we build a normative model of fair bets under deductive uncertainty
which draws from both probability and the theory of algorithms. We comment on
connections to Zeilberger's notion of "semi-rigorous proofs", particularly that
inherent subjectivity would be present. We also discuss a financial view with
models of arbitrage where traders have limited computational resources.
| [
{
"version": "v1",
"created": "Tue, 29 Aug 2017 21:29:05 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Sep 2017 17:03:21 GMT"
},
{
"version": "v3",
"created": "Wed, 6 Feb 2019 04:48:57 GMT"
},
{
"version": "v4",
"created": "Mon, 4 Mar 2019 16:09:35 GMT"
},
{
"version": "v5",
"created": "Sun, 24 Mar 2019 23:00:49 GMT"
},
{
"version": "v6",
"created": "Mon, 16 Dec 2019 17:27:47 GMT"
}
] | 1,576,540,800,000 | [
[
"MacFie",
"Andrew",
""
]
] |
1709.00322 | Kenta Cho | Kenta Cho and Bart Jacobs | Disintegration and Bayesian Inversion via String Diagrams | Accepted for publication in Mathematical Structures in Computer
Science | Math. Struct. Comp. Sci. 29 (2019) 938-971 | 10.1017/S0960129518000488 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The notions of disintegration and Bayesian inversion are fundamental in
conditional probability theory. They produce channels, as conditional
probabilities, from a joint state, or from an already given channel (in
opposite direction). These notions exist in the literature, in concrete
situations, but are presented here in abstract graphical formulations. The
resulting abstract descriptions are used for proving basic results in
conditional probability theory. The existence of disintegration and Bayesian
inversion is discussed for discrete probability, and also for measure-theoretic
probability --- via standard Borel spaces and via likelihoods. Finally, the
usefulness of disintegration and Bayesian inversion is illustrated in several
examples.
| [
{
"version": "v1",
"created": "Tue, 29 Aug 2017 13:01:07 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Jun 2018 01:58:30 GMT"
},
{
"version": "v3",
"created": "Fri, 8 Feb 2019 01:45:14 GMT"
}
] | 1,564,531,200,000 | [
[
"Cho",
"Kenta",
""
],
[
"Jacobs",
"Bart",
""
]
] |
1709.00670 | Vinu E V | Vinu E.V, P Sreenivasa Kumar | Difficulty-level Modeling of Ontology-based Factual Questions | This manuscript is currently under review in the Semantic Web Journal
(http://www.semantic-web-journal.net/system/files/swj1712.pdf) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semantics based knowledge representations such as ontologies are found to be
very useful in automatically generating meaningful factual questions.
Determining the difficulty level of these system generated questions is helpful
to effectively utilize them in various educational and professional
applications. The existing approaches for finding the difficulty level of
factual questions are very simple and are limited to a few basic principles. We
propose a new methodology for this problem by considering an educational theory
called Item Response Theory (IRT). In the IRT, knowledge proficiency of end
users (learners) are considered for assigning difficulty levels, because of the
assumptions that a given question is perceived differently by learners of
various proficiencies. We have done a detailed study on the features (factors)
of a question statement which could possibly determine its difficulty level for
three learner categories (experts, intermediates and beginners). We formulate
ontology based metrics for the same. We then train three logistic regression
models to predict the difficulty level corresponding to the three learner
categories.
| [
{
"version": "v1",
"created": "Sun, 3 Sep 2017 06:27:45 GMT"
}
] | 1,504,569,600,000 | [
[
"E.",
"Vinu",
"V"
],
[
"Kumar",
"P Sreenivasa",
""
]
] |
1709.00931 | Azlan Iqbal | Azlan Iqbal | A Computer Composes A Fabled Problem: Four Knights vs. Queen | 12 pages, 5 figures and 2 appendices | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explain how the prototype automatic chess problem composer, Chesthetica,
successfully composed a rare and interesting chess problem using the new
Digital Synaptic Neural Substrate (DSNS) computational creativity approach.
This problem represents a greater challenge from a creative standpoint because
the checkmate is not always clear and the method of winning even less so.
Creating a decisive chess problem of this type without the aid of an omniscient
7-piece endgame tablebase (and one that also abides by several chess
composition conventions) would therefore be a challenge for most human players
and composers working on their own. The fact that a small computer with
relatively low processing power and memory was sufficient to compose such a
problem using the DSNS approach in just 10 days is therefore noteworthy. In
this report we document the event and result in some detail. It lends
additional credence to the DSNS as a viable new approach in the field of
computational creativity. In particular, in areas where human-like creativity
is required for targeted or specific problems with no clear path to the
solution.
| [
{
"version": "v1",
"created": "Mon, 4 Sep 2017 12:56:23 GMT"
}
] | 1,504,569,600,000 | [
[
"Iqbal",
"Azlan",
""
]
] |
1709.01308 | Simyung Chang | Simyung Chang, YoungJoon Yoo, Jaeseok Choi, Nojun Kwak | BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement
Learning | 8 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a novel method to train agents of reinforcement learning (RL) by
sharing knowledge in a way similar to the concept of using a book. The recorded
information in the form of a book is the main means by which humans learn
knowledge. Nevertheless, the conventional deep RL methods have mainly focused
either on experiential learning where the agent learns through interactions
with the environment from the start or on imitation learning that tries to
mimic the teacher. Contrary to these, our proposed book learning shares key
information among different agents in a book-like manner by delving into the
following two characteristic features: (1) By defining the linguistic function,
input states can be clustered semantically into a relatively small number of
core clusters, which are forwarded to other RL agents in a prescribed manner.
(2) By defining state priorities and the contents for recording, core
experiences can be selected and stored in a small container. We call this
container as `BOOK'. Our method learns hundreds to thousand times faster than
the conventional methods by learning only a handful of core cluster
information, which shows that deep RL agents can effectively learn through the
shared knowledge from other agents.
| [
{
"version": "v1",
"created": "Tue, 5 Sep 2017 09:47:41 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Nov 2017 16:57:18 GMT"
},
{
"version": "v3",
"created": "Mon, 12 Feb 2018 08:44:59 GMT"
}
] | 1,518,480,000,000 | [
[
"Chang",
"Simyung",
""
],
[
"Yoo",
"YoungJoon",
""
],
[
"Choi",
"Jaeseok",
""
],
[
"Kwak",
"Nojun",
""
]
] |
1709.01490 | Garrett Andersen | Garrett Andersen, George Konidaris | Active Exploration for Learning Symbolic Representations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce an online active exploration algorithm for data-efficiently
learning an abstract symbolic model of an environment. Our algorithm is divided
into two parts: the first part quickly generates an intermediate Bayesian
symbolic model from the data that the agent has collected so far, which the
agent can then use along with the second part to guide its future exploration
towards regions of the state space that the model is uncertain about. We show
that our algorithm outperforms random and greedy exploration policies on two
different computer game domains. The first domain is an Asteroids-inspired game
with complex dynamics but basic logical structure. The second is the Treasure
Game, with simpler dynamics but more complex logical structure.
| [
{
"version": "v1",
"created": "Tue, 5 Sep 2017 17:09:48 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Nov 2017 15:09:31 GMT"
}
] | 1,509,580,800,000 | [
[
"Andersen",
"Garrett",
""
],
[
"Konidaris",
"George",
""
]
] |
1709.01547 | Ivan Yu. Tyukin | Ivan Y. Tyukin, Alexander N. Gorban, Konstantin Sofeikov, Ilya
Romanenko | Knowledge Transfer Between Artificial Intelligence Systems | null | Front Neurorobot. 2018; 12: 49 | 10.3389/fnbot.2018.00049 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the fundamental question: how a legacy "student" Artificial
Intelligent (AI) system could learn from a legacy "teacher" AI system or a
human expert without complete re-training and, most importantly, without
requiring significant computational resources. Here "learning" is understood as
an ability of one system to mimic responses of the other and vice-versa. We
call such learning an Artificial Intelligence knowledge transfer. We show that
if internal variables of the "student" Artificial Intelligent system have the
structure of an $n$-dimensional topological vector space and $n$ is
sufficiently high then, with probability close to one, the required knowledge
transfer can be implemented by simple cascades of linear functionals. In
particular, for $n$ sufficiently large, with probability close to one, the
"student" system can successfully and non-iteratively learn $k\ll n$ new
examples from the "teacher" (or correct the same number of mistakes) at the
cost of two additional inner products. The concept is illustrated with an
example of knowledge transfer from a pre-trained convolutional neural network
to a simple linear classifier with HOG features.
| [
{
"version": "v1",
"created": "Tue, 5 Sep 2017 18:38:07 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Nov 2017 08:21:13 GMT"
}
] | 1,652,745,600,000 | [
[
"Tyukin",
"Ivan Y.",
""
],
[
"Gorban",
"Alexander N.",
""
],
[
"Sofeikov",
"Konstantin",
""
],
[
"Romanenko",
"Ilya",
""
]
] |
1709.02642 | Dmytro Terletskyi | Dmytro Terletskyi | Object-Oriented Knowledge Extraction using Universal Exploiters | null | Proceedings of the XIIth International Scientific and Technical
Conference Computer Science and Information Technologies, CSIT-2017, 5-8
September, 2017, Lviv, Ukraine, pp. 257-266 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper contains analysis and extension of exploiters-based knowledge
extraction methods, which allow generation of new knowledge, based on the basic
ones. The main achievement of the paper is useful features of some universal
exploiters proof, which allow extending set of basic classes and set of basic
relations by finite set of new classes of objects and relations among them,
which allow creating of complete lattice. Proposed approach gives an
opportunity to compute quantity of new classes, which can be generated using
it, and quantity of different types, which each of obtained classes describes;
constructing of defined hierarchy of classes with determined subsumption
relation; avoidance of some problems of inheritance and more efficient
restoring of basic knowledge within the database.
| [
{
"version": "v1",
"created": "Fri, 8 Sep 2017 10:55:15 GMT"
}
] | 1,505,088,000,000 | [
[
"Terletskyi",
"Dmytro",
""
]
] |
1709.03136 | Vahid Moosavi | Vahid Moosavi | Computational Machines in a Coexistence with Concrete Universals and
Data Streams | null | Buhlmann, V., Hovestadt, L., & Moosavi, V. (Eds.). (2015). Coding
as Literacy: Metalithic IV (Vol 4). Birkhauser | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss that how the majority of traditional modeling approaches are
following the idealism point of view in scientific modeling, which follow the
set theoretical notions of models based on abstract universals. We show that
while successful in many classical modeling domains, there are fundamental
limits to the application of set theoretical models in dealing with complex
systems with many potential aspects or properties depending on the
perspectives. As an alternative to abstract universals, we propose a conceptual
modeling framework based on concrete universals that can be interpreted as a
category theoretical approach to modeling. We call this modeling framework
pre-specific modeling. We further, discuss how a certain group of mathematical
and computational methods, along with ever-growing data streams are able to
operationalize the concept of pre-specific modeling.
| [
{
"version": "v1",
"created": "Sun, 10 Sep 2017 16:57:14 GMT"
}
] | 1,505,174,400,000 | [
[
"Moosavi",
"Vahid",
""
]
] |
1709.03480 | Nicolas A. Barriga | Nicolas A. Barriga, Marius Stanescu and Michael Buro | Combining Strategic Learning and Tactical Search in Real-Time Strategy
Games | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A commonly used technique for managing AI complexity in real-time strategy
(RTS) games is to use action and/or state abstractions. High-level abstractions
can often lead to good strategic decision making, but tactical decision quality
may suffer due to lost details. A competing method is to sample the search
space which often leads to good tactical performance in simple scenarios, but
poor high-level planning.
We propose to use a deep convolutional neural network (CNN) to select among a
limited set of abstract action choices, and to utilize the remaining
computation time for game tree search to improve low level tactics. The CNN is
trained by supervised learning on game states labelled by Puppet Search, a
strategic search algorithm that uses action abstractions. The network is then
used to select a script --- an abstract action --- to produce low level actions
for all units. Subsequently, the game tree search algorithm improves the
tactical actions of a subset of units using a limited view of the game state
only considering units close to opponent units.
Experiments in the microRTS game show that the combined algorithm results in
higher win-rates than either of its two independent components and other
state-of-the-art microRTS agents.
To the best of our knowledge, this is the first successful application of a
convolutional network to play a full RTS game on standard game maps, as
previous work has focused on sub-problems, such as combat, or on very small
maps.
| [
{
"version": "v1",
"created": "Mon, 11 Sep 2017 17:17:51 GMT"
}
] | 1,505,174,400,000 | [
[
"Barriga",
"Nicolas A.",
""
],
[
"Stanescu",
"Marius",
""
],
[
"Buro",
"Michael",
""
]
] |
1709.03879 | Eray Ozkural | Eray \"Ozkural | Ultimate Intelligence Part III: Measures of Intelligence, Perception and
Intelligent Agents | Third installation of the Ultimate Intelligence series. Submitted to
AGI-2017. arXiv admin note: text overlap with arXiv:1504.03303 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose that operator induction serves as an adequate model of perception.
We explain how to reduce universal agent models to operator induction. We
propose a universal measure of operator induction fitness, and show how it can
be used in a reinforcement learning model and a homeostasis (self-preserving)
agent based on the free energy principle. We show that the action of the
homeostasis agent can be explained by the operator induction model.
| [
{
"version": "v1",
"created": "Fri, 8 Sep 2017 17:45:30 GMT"
}
] | 1,505,260,800,000 | [
[
"Özkural",
"Eray",
""
]
] |
1709.03915 | Wilhelmiina H\"am\"al\"ainen | Wilhelmiina H\"am\"al\"ainen and Geoffrey I. Webb | Specious rules: an efficient and effective unifying method for removing
misleading and uninformative patterns in association rule mining | Note: This is a corrected version of the paper published in SDM'17.
In the equation on page 4, the range of the sum has been corrected | Proceedings of SIAM International Conference on Data Mining, pp.
309-317, SIAM 2017 | 10.1137/1.9781611974973.35 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present theoretical analysis and a suite of tests and procedures for
addressing a broad class of redundant and misleading association rules we call
\emph{specious rules}. Specious dependencies, also known as \emph{spurious},
\emph{apparent}, or \emph{illusory associations}, refer to a well-known
phenomenon where marginal dependencies are merely products of interactions with
other variables and disappear when conditioned on those variables.
The most extreme example is Yule-Simpson's paradox where two variables
present positive dependence in the marginal contingency table but negative in
all partial tables defined by different levels of a confounding factor. It is
accepted wisdom that in data of any nontrivial dimensionality it is infeasible
to control for all of the exponentially many possible confounds of this nature.
In this paper, we consider the problem of specious dependencies in the context
of statistical association rule mining. We define specious rules and show they
offer a unifying framework which covers many types of previously proposed
redundant or misleading association rules. After theoretical analysis, we
introduce practical algorithms for detecting and pruning out specious
association rules efficiently under many key goodness measures, including
mutual information and exact hypergeometric probabilities. We demonstrate that
the procedure greatly reduces the number of associations discovered, providing
an elegant and effective solution to the problem of association mining
discovering large numbers of misleading and redundant rules.
| [
{
"version": "v1",
"created": "Tue, 12 Sep 2017 15:39:47 GMT"
}
] | 1,505,260,800,000 | [
[
"Hämäläinen",
"Wilhelmiina",
""
],
[
"Webb",
"Geoffrey I.",
""
]
] |
1709.03969 | Zhiyu Lin | Zhiyu Lin, Brent Harrison, Aaron Keech, and Mark O. Riedl | Explore, Exploit or Listen: Combining Human Feedback and Policy Model to
Speed up Deep Reinforcement Learning in 3D Worlds | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a method to use discrete human feedback to enhance the
performance of deep learning agents in virtual three-dimensional environments
by extending deep-reinforcement learning to model the confidence and
consistency of human feedback. This enables deep reinforcement learning
algorithms to determine the most appropriate time to listen to the human
feedback, exploit the current policy model, or explore the agent's environment.
Managing the trade-off between these three strategies allows DRL agents to be
robust to inconsistent or intermittent human feedback. Through experimentation
using a synthetic oracle, we show that our technique improves the training
speed and overall performance of deep reinforcement learning in navigating
three-dimensional environments using Minecraft. We further show that our
technique is robust to highly innacurate human feedback and can also operate
when no human feedback is given.
| [
{
"version": "v1",
"created": "Tue, 12 Sep 2017 17:42:21 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Jun 2021 20:27:31 GMT"
}
] | 1,624,492,800,000 | [
[
"Lin",
"Zhiyu",
""
],
[
"Harrison",
"Brent",
""
],
[
"Keech",
"Aaron",
""
],
[
"Riedl",
"Mark O.",
""
]
] |
1709.04029 | Subhash Kak | Subhash Kak | Probability Reversal and the Disjunction Effect in Reasoning Systems | 11 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data based judgments go into artificial intelligence applications but they
undergo paradoxical reversal when seemingly unnecessary additional data is
provided. Examples of this are Simpson's reversal and the disjunction effect
where the beliefs about the data change once it is presented or aggregated
differently. Sometimes the significance of the difference can be evaluated
using statistical tests such as Pearson's chi-squared or Fisher's exact test,
but this may not be helpful in threshold-based decision systems that operate
with incomplete information. To mitigate risks in the use of algorithms in
decision-making, we consider the question of modeling of beliefs. We argue that
evidence supports that beliefs are not classical statistical variables and they
should, in the general case, be considered as superposition states of disjoint
or polar outcomes. We analyze the disjunction effect from the perspective of
the belief as a quantum vector.
| [
{
"version": "v1",
"created": "Tue, 12 Sep 2017 19:18:22 GMT"
}
] | 1,505,347,200,000 | [
[
"Kak",
"Subhash",
""
]
] |
1709.04182 | Arnaud Martin | Arnaud Martin (DRUID) | Conflict management in information fusion with belief functions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Information fusion, the conflict is an important concept. Indeed,
combining several imperfect experts or sources allows conflict. In the theory
of belief functions, this notion has been discussed a lot. The mass appearing
on the empty set during the conjunctive combination rule is generally
considered as conflict, but that is not really a conflict. Some measures of
conflict have been proposed and some approaches have been proposed in order to
manage this conflict or to decide with conflicting mass functions. We recall in
this chapter some of them and we propose a discussion to consider the conflict
in information fusion with the theory of belief functions.
| [
{
"version": "v1",
"created": "Wed, 13 Sep 2017 08:35:48 GMT"
}
] | 1,505,347,200,000 | [
[
"Martin",
"Arnaud",
"",
"DRUID"
]
] |
1709.04240 | Joseph Ramsey | Joseph D. Ramsey and Bryan Andrews | A Comparison of Public Causal Search Packages on Linear, Gaussian Data
with No Latent Variables | 7 figures, 1 table | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We compare Tetrad (Java) algorithms to the other public software packages BNT
(Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \vanilla" task of
recovering DAG structure to the extent possible from data generated recursively
from linear, Gaussian structure equation models (SEMs) with no latent
variables, for random graphs, with no additional knowledge of variable order or
adjacency structure, and without additional specification of intervention
information. Each one of the above packages offers at least one implementation
suitable to this purpose. We compare them on adjacency and orientation accuracy
as well as time performance, for fixed datasets. We vary the number of
variables, the number of samples, and the density of graph, for a total of 27
combinations, averaging all statistics over 10 runs, for a total of 270
datasets. All runs are carried out on the same machine and on their native
platforms. An interactive visualization tool is provided for the reader who
wishes to know more than can be documented explicitly in this report.
| [
{
"version": "v1",
"created": "Wed, 13 Sep 2017 10:41:19 GMT"
},
{
"version": "v2",
"created": "Sat, 16 Sep 2017 16:09:06 GMT"
}
] | 1,505,779,200,000 | [
[
"Ramsey",
"Joseph D.",
""
],
[
"Andrews",
"Bryan",
""
]
] |
1709.04328 | Maxime Lenormand | Maxime Lenormand | Generating OWA weights using truncated distributions | 7 pages, 7 figures | International Journal of Intelligent Systems 33, 791-801 (2018) | 10.1002/int.21963 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ordered weighted averaging (OWA) operators have been widely used in decision
making these past few years. An important issue facing the OWA operators' users
is the determination of the OWA weights. This paper introduces an OWA
determination method based on truncated distributions that enables intuitive
generation of OWA weights according to a certain level of risk and trade-off.
These two dimensions are represented by the two first moments of the truncated
distribution. We illustrate our approach with the well-know normal distribution
and the definition of a continuous parabolic decision-strategy space. We
finally study the impact of the number of criteria on the results.
| [
{
"version": "v1",
"created": "Wed, 13 Sep 2017 13:43:43 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Feb 2018 23:42:13 GMT"
}
] | 1,525,651,200,000 | [
[
"Lenormand",
"Maxime",
""
]
] |
1709.04524 | Kartik Talamadupula | Kartik Talamadupula and Biplav Srivastava and Jeffrey O. Kephart | Workflow Complexity for Collaborative Interactions: Where are the
Metrics? -- A Challenge | 4 pages, 1 figure, 1 table Appeared in the ICAPS 2017 UISP Workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce the problem of denoting and deriving the
complexity of workflows (plans, schedules) in collaborative, planner-assisted
settings where humans and agents are trying to jointly solve a task. The
interactions -- and hence the workflows that connect the human and the agents
-- may differ according to the domain and the kind of agents. We adapt insights
from prior work in human-agent teaming and workflow analysis to suggest metrics
for workflow complexity. The main motivation behind this work is to highlight
metrics for human comprehensibility of plans and schedules. The planning
community has seen its fair share of work on the synthesis of plans that take
diversity into account -- what value do such plans hold if their generation is
not guided at least in part by metrics that reflect the ease of engaging with
and using those plans?
| [
{
"version": "v1",
"created": "Wed, 13 Sep 2017 20:06:43 GMT"
}
] | 1,505,433,600,000 | [
[
"Talamadupula",
"Kartik",
""
],
[
"Srivastava",
"Biplav",
""
],
[
"Kephart",
"Jeffrey O.",
""
]
] |
1709.04571 | Pierre-Luc Bacon | Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup | When Waiting is not an Option : Learning Options with a Deliberation
Cost | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work has shown that temporally extended actions (options) can be
learned fully end-to-end as opposed to being specified in advance. While the
problem of "how" to learn options is increasingly well understood, the question
of "what" good options should be has remained elusive. We formulate our answer
to what "good" options should be in the bounded rationality framework (Simon,
1957) through the notion of deliberation cost. We then derive practical
gradient-based learning algorithms to implement this objective. Our results in
the Arcade Learning Environment (ALE) show increased performance and
interpretability.
| [
{
"version": "v1",
"created": "Thu, 14 Sep 2017 00:18:44 GMT"
}
] | 1,505,433,600,000 | [
[
"Harb",
"Jean",
""
],
[
"Bacon",
"Pierre-Luc",
""
],
[
"Klissarov",
"Martin",
""
],
[
"Precup",
"Doina",
""
]
] |
1709.04579 | Behzad Ghazanfari | Behzad Ghazanfari and Matthew E. Taylor | Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement
Learning and Multi-task Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning (RL), while often powerful, can suffer from slow
learning speeds, particularly in high dimensional spaces. The autonomous
decomposition of tasks and use of hierarchical methods hold the potential to
significantly speed up learning in such domains. This paper proposes a novel
practical method that can autonomously decompose tasks, by leveraging
association rule mining, which discovers hidden relationship among entities in
data mining. We introduce a novel method called ARM-HSTRL (Association Rule
Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning).
It extracts temporal and structural relationships of sub-goals in RL, and
multi-task RL. In particular,it finds sub-goals and relationship among them. It
is shown the significant efficiency and performance of the proposed method in
two main topics of RL.
| [
{
"version": "v1",
"created": "Thu, 14 Sep 2017 01:43:13 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Sep 2017 16:21:03 GMT"
}
] | 1,505,692,800,000 | [
[
"Ghazanfari",
"Behzad",
""
],
[
"Taylor",
"Matthew E.",
""
]
] |
1709.04636 | Marius Lindauer | Marius Lindauer and Frank Hutter | Warmstarting of Model-based Algorithm Configuration | Preprint of AAAI'18 paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The performance of many hard combinatorial problem solvers depends strongly
on their parameter settings, and since manual parameter tuning is both tedious
and suboptimal the AI community has recently developed several algorithm
configuration (AC) methods to automatically address this problem. While all
existing AC methods start the configuration process of an algorithm A from
scratch for each new type of benchmark instances, here we propose to exploit
information about A's performance on previous benchmarks in order to warmstart
its configuration on new types of benchmarks. We introduce two complementary
ways in which we can exploit this information to warmstart AC methods based on
a predictive model. Experiments for optimizing a very flexible modern SAT
solver on twelve different instance sets show that our methods often yield
substantial speedups over existing AC methods (up to 165-fold) and can also
find substantially better configurations given the same compute budget.
| [
{
"version": "v1",
"created": "Thu, 14 Sep 2017 07:09:54 GMT"
},
{
"version": "v2",
"created": "Tue, 24 Oct 2017 07:14:01 GMT"
},
{
"version": "v3",
"created": "Tue, 28 Nov 2017 10:07:41 GMT"
}
] | 1,511,913,600,000 | [
[
"Lindauer",
"Marius",
""
],
[
"Hutter",
"Frank",
""
]
] |
1709.04676 | Alberto Garcia-Duran | Alberto Garcia-Duran and Mathias Niepert | KBLRN : End-to-End Learning of Knowledge Base Representations with
Latent, Relational, and Numerical Features | UAI 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present KBLRN, a framework for end-to-end learning of knowledge base
representations from latent, relational, and numerical features. KBLRN
integrates feature types with a novel combination of neural representation
learning and probabilistic product of experts models. To the best of our
knowledge, KBLRN is the first approach that learns representations of knowledge
bases by integrating latent, relational, and numerical features. We show that
instances of KBLRN outperform existing methods on a range of knowledge base
completion tasks. We contribute a novel data sets enriching commonly used
knowledge base completion benchmarks with numerical features. The data sets are
available under a permissive BSD-3 license. We also investigate the impact
numerical features have on the KB completion performance of KBLRN.
| [
{
"version": "v1",
"created": "Thu, 14 Sep 2017 09:13:46 GMT"
},
{
"version": "v2",
"created": "Mon, 12 Mar 2018 14:40:43 GMT"
},
{
"version": "v3",
"created": "Mon, 11 Jun 2018 11:55:27 GMT"
}
] | 1,528,761,600,000 | [
[
"Garcia-Duran",
"Alberto",
""
],
[
"Niepert",
"Mathias",
""
]
] |
1709.04734 | Neelanshi Varia | Mahipal Jadeja and Neelanshi Varia | Perspectives for Evaluating Conversational AI | SCAI'17 - Search-Oriented Conversational AI (@ICTIR'17) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conversational AI systems are becoming famous in day to day lives. In this
paper, we are trying to address the following key question: To identify whether
design, as well as development efforts for search oriented conversational AI
are successful or not.It is tricky to define 'success' in the case of
conversational AI and equally tricky part is to use appropriate metrics for the
evaluation of conversational AI. We propose four different perspectives namely
user experience, information retrieval, linguistic and artificial intelligence
for the evaluation of conversational AI systems. Additionally, background
details of conversational AI systems are provided including desirable
characteristics of personal assistants, differences between chatbot and an AI
based personal assistant. An importance of personalization and how it can be
achieved is explained in detail. Current challenges in the development of an
ideal conversational AI (personal assistant) are also highlighted along with
guidelines for achieving personalized experience for users.
| [
{
"version": "v1",
"created": "Thu, 14 Sep 2017 12:37:08 GMT"
}
] | 1,505,433,600,000 | [
[
"Jadeja",
"Mahipal",
""
],
[
"Varia",
"Neelanshi",
""
]
] |
1709.04763 | Yuanduo He | Yuanduo He, Xu Chu, Juguang Peng, Jingyue Gao, Yasha Wang | Motif-based Rule Discovery for Predicting Real-valued Time Series | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Time series prediction is of great significance in many applications and has
attracted extensive attention from the data mining community. Existing work
suggests that for many problems, the shape in the current time series may
correlate an upcoming shape in the same or another series. Therefore, it is a
promising strategy to associate two recurring patterns as a rule's antecedent
and consequent: the occurrence of the antecedent can foretell the occurrence of
the consequent, and the learned shape of consequent will give accurate
predictions. Earlier work employs symbolization methods, but the symbolized
representation maintains too little information of the original series to mine
valid rules. The state-of-the-art work, though directly manipulating the
series, fails to segment the series precisely for seeking
antecedents/consequents, resulting in inaccurate rules in common scenarios. In
this paper, we propose a novel motif-based rule discovery method, which
utilizes motif discovery to accurately extract frequently occurring consecutive
subsequences, i.e. motifs, as antecedents/consequents. It then investigates the
underlying relationships between motifs by matching motifs as rule candidates
and ranking them based on the similarities. Experimental results on real open
datasets show that the proposed approach outperforms the baseline method by
23.9%. Furthermore, it extends the applicability from single time series to
multiple ones.
| [
{
"version": "v1",
"created": "Thu, 14 Sep 2017 13:13:01 GMT"
},
{
"version": "v2",
"created": "Mon, 16 Oct 2017 14:30:52 GMT"
},
{
"version": "v3",
"created": "Sat, 18 Nov 2017 08:19:44 GMT"
},
{
"version": "v4",
"created": "Sat, 2 Dec 2017 03:30:23 GMT"
}
] | 1,512,432,000,000 | [
[
"He",
"Yuanduo",
""
],
[
"Chu",
"Xu",
""
],
[
"Peng",
"Juguang",
""
],
[
"Gao",
"Jingyue",
""
],
[
"Wang",
"Yasha",
""
]
] |
1709.04825 | Francisco J. Arjonilla | Francisco J. Arjonilla, Tetsuya Ogata | General problem solving with category theory | Laboratory for Intelligent Dynamics and Representation. Waseda
University | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a formal cognitive framework for problem solving based on
category theory. We introduce cognitive categories, which are categories with
exactly one morphism between any two objects. Objects in these categories are
interpreted as states and morphisms as transformations between states.
Moreover, cognitive problems are reduced to the specification of two objects in
a cognitive category: an outset (i.e. the current state of the system) and a
goal (i.e. the desired state). Cognitive systems transform the target system by
means of generators and evaluators. Generators realize cognitive operations
over a system by grouping morphisms, whilst evaluators group objects as a way
to generalize outsets and goals to partially defined states. Meta-cognition
emerges when the whole cognitive system is self-referenced as sub-states in the
cognitive category, whilst learning must always be considered as a
meta-cognitive process to maintain consistency. Several examples grounded in
basic AI methods are provided as well.
| [
{
"version": "v1",
"created": "Thu, 14 Sep 2017 14:56:49 GMT"
}
] | 1,505,433,600,000 | [
[
"Arjonilla",
"Francisco J.",
""
],
[
"Ogata",
"Tetsuya",
""
]
] |
1709.05067 | Neelanshi Varia | Mahipal Jadeja, Neelanshi Varia and Agam Shah | Deep Reinforcement Learning for Conversational AI | SCAI'17-Search-Oriented Conversational AI (@ICTIR) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep reinforcement learning is revolutionizing the artificial intelligence
field. Currently, it serves as a good starting point for constructing
intelligent autonomous systems which offer a better knowledge of the visual
world. It is possible to scale deep reinforcement learning with the use of deep
learning and do amazing tasks such as use of pixels in playing video games. In
this paper, key concepts of deep reinforcement learning including reward
function, differences between reinforcement learning and supervised learning
and models for implementation of reinforcement are discussed. Key challenges
related to the implementation of reinforcement learning in conversational AI
domain are identified as well as discussed in detail. Various conversational
models which are based on deep reinforcement learning (as well as deep
learning) are also discussed. In summary, this paper discusses key aspects of
deep reinforcement learning which are crucial for designing an efficient
conversational AI.
| [
{
"version": "v1",
"created": "Fri, 15 Sep 2017 06:18:33 GMT"
}
] | 1,505,692,800,000 | [
[
"Jadeja",
"Mahipal",
""
],
[
"Varia",
"Neelanshi",
""
],
[
"Shah",
"Agam",
""
]
] |
1709.05638 | Milan Aggarwal | Milan Aggarwal, Aarushi Arora, Shagun Sodhani, Balaji Krishnamurthy | Improving Search through A3C Reinforcement Learning based Conversational
Agent | 17 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states.
| [
{
"version": "v1",
"created": "Sun, 17 Sep 2017 10:56:41 GMT"
},
{
"version": "v2",
"created": "Sun, 19 Aug 2018 08:00:34 GMT"
}
] | 1,534,809,600,000 | [
[
"Aggarwal",
"Milan",
""
],
[
"Arora",
"Aarushi",
""
],
[
"Sodhani",
"Shagun",
""
],
[
"Krishnamurthy",
"Balaji",
""
]
] |
1709.05706 | Arbaaz Khan | Arbaaz Khan, Clark Zhang, Nikolay Atanasov, Konstantinos Karydis,
Vijay Kumar, Daniel D. Lee | Memory Augmented Control Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planning problems in partially observable environments cannot be solved
directly with convolutional networks and require some form of memory. But, even
memory networks with sophisticated addressing schemes are unable to learn
intelligent reasoning satisfactorily due to the complexity of simultaneously
learning to access memory and plan. To mitigate these challenges we introduce
the Memory Augmented Control Network (MACN). The proposed network architecture
consists of three main parts. The first part uses convolutions to extract
features and the second part uses a neural network-based planning module to
pre-plan in the environment. The third part uses a network controller that
learns to store those specific instances of past information that are necessary
for planning. The performance of the network is evaluated in discrete grid
world environments for path planning in the presence of simple and complex
obstacles. We show that our network learns to plan and can generalize to new
environments.
| [
{
"version": "v1",
"created": "Sun, 17 Sep 2017 19:06:13 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Sep 2017 05:11:23 GMT"
},
{
"version": "v3",
"created": "Fri, 3 Nov 2017 03:34:58 GMT"
},
{
"version": "v4",
"created": "Wed, 27 Dec 2017 00:24:40 GMT"
},
{
"version": "v5",
"created": "Mon, 12 Feb 2018 01:25:55 GMT"
},
{
"version": "v6",
"created": "Wed, 14 Feb 2018 05:34:03 GMT"
}
] | 1,518,652,800,000 | [
[
"Khan",
"Arbaaz",
""
],
[
"Zhang",
"Clark",
""
],
[
"Atanasov",
"Nikolay",
""
],
[
"Karydis",
"Konstantinos",
""
],
[
"Kumar",
"Vijay",
""
],
[
"Lee",
"Daniel D.",
""
]
] |
1709.05825 | Ondrej Kuzelka | Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert | Relational Marginal Problems: Theory and Estimation | Long version of a paper that appeared in AAAI 2018; added a paragraph
to Related Work | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the propositional setting, the marginal problem is to find a
(maximum-entropy) distribution that has some given marginals. We study this
problem in a relational setting and make the following contributions. First, we
compare two different notions of relational marginals. Second, we show a
duality between the resulting relational marginal problems and the maximum
likelihood estimation of the parameters of relational models, which generalizes
a well-known duality from the propositional setting. Third, by exploiting the
relational marginal formulation, we present a statistically sound method to
learn the parameters of relational models that will be applied in settings
where the number of constants differs between the training and test data.
Furthermore, based on a relational generalization of marginal polytopes, we
characterize cases where the standard estimators based on feature's number of
true groundings needs to be adjusted and we quantitatively characterize the
consequences of these adjustments. Fourth, we prove bounds on expected errors
of the estimated parameters, which allows us to lower-bound, among other
things, the effective sample size of relational training data.
| [
{
"version": "v1",
"created": "Mon, 18 Sep 2017 09:10:27 GMT"
},
{
"version": "v2",
"created": "Sun, 19 Nov 2017 12:46:20 GMT"
},
{
"version": "v3",
"created": "Tue, 17 Apr 2018 13:25:40 GMT"
},
{
"version": "v4",
"created": "Wed, 25 Apr 2018 09:57:42 GMT"
}
] | 1,524,700,800,000 | [
[
"Kuzelka",
"Ondrej",
""
],
[
"Wang",
"Yuyi",
""
],
[
"Davis",
"Jesse",
""
],
[
"Schockaert",
"Steven",
""
]
] |
1709.05958 | Matthew Peveler | Matthew Peveler, Naveen Sundar Govindarajulu, Selmer Bringsjord,
Biplav Srivastava, Kartik Talamadupula, Hui Su | Toward Cognitive and Immersive Systems: Experiments in a Cognitive
Microworld | Submitted to Advances of Cognitive Systems 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As computational power has continued to increase, and sensors have become
more accurate, the corresponding advent of systems that are at once cognitive
and immersive has arrived. These \textit{cognitive and immersive systems}
(CAISs) fall squarely into the intersection of AI with HCI/HRI: such systems
interact with and assist the human agents that enter them, in no small part
because such systems are infused with AI able to understand and reason about
these humans and their knowledge, beliefs, goals, communications, plans, etc.
We herein explain our approach to engineering CAISs. We emphasize the capacity
of a CAIS to develop and reason over a `theory of the mind' of its human
partners. This capacity entails that the AI in question has a sophisticated
model of the beliefs, knowledge, goals, desires, emotions, etc.\ of these
humans. To accomplish this engineering, a formal framework of very high
expressivity is needed. In our case, this framework is a \textit{cognitive
event calculus}, a particular kind of quantified multi-operator modal logic,
and a matching high-expressivity automated reasoner and planner. To explain,
advance, and to a degree validate our approach, we show that a calculus of this
type satisfies a set of formal requirements, and can enable a CAIS to
understand a psychologically tricky scenario couched in what we call the
\textit{cognitive polysolid framework} (CPF). We also formally show that a room
that satisfies these requirements can have a useful property we term
\emph{expectation of usefulness}. CPF, a sub-class of \textit{cognitive
microworlds}, includes machinery able to represent and plan over not merely
blocks and actions (such as seen in the primitive `blocks worlds' of old), but
also over agents and their mental attitudes about both other agents and
inanimate objects.
| [
{
"version": "v1",
"created": "Thu, 14 Sep 2017 21:52:54 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Dec 2018 17:26:47 GMT"
}
] | 1,545,177,600,000 | [
[
"Peveler",
"Matthew",
""
],
[
"Govindarajulu",
"Naveen Sundar",
""
],
[
"Bringsjord",
"Selmer",
""
],
[
"Srivastava",
"Biplav",
""
],
[
"Talamadupula",
"Kartik",
""
],
[
"Su",
"Hui",
""
]
] |
1709.06275 | Ryan Carey | Ryan Carey | Incorrigibility in the CIRL Framework | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A value learning system has incentives to follow shutdown instructions,
assuming the shutdown instruction provides information (in the technical sense)
about which actions lead to valuable outcomes. However, this assumption is not
robust to model mis-specification (e.g., in the case of programmer errors). We
demonstrate this by presenting some Supervised POMDP scenarios in which errors
in the parameterized reward function remove the incentive to follow shutdown
commands. These difficulties parallel those discussed by Soares et al. (2015)
in their paper on corrigibility. We argue that it is important to consider
systems that follow shutdown commands under some weaker set of assumptions
(e.g., that one small verified module is correctly implemented; as opposed to
an entire prior probability distribution and/or parameterized reward function).
We discuss some difficulties with simple ways to attempt to attain these sorts
of guarantees in a value learning framework.
| [
{
"version": "v1",
"created": "Tue, 19 Sep 2017 07:23:18 GMT"
},
{
"version": "v2",
"created": "Sun, 3 Jun 2018 17:43:18 GMT"
}
] | 1,528,156,800,000 | [
[
"Carey",
"Ryan",
""
]
] |
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