id
stringlengths 9
10
| submitter
stringlengths 5
47
⌀ | authors
stringlengths 5
1.72k
| title
stringlengths 11
234
| comments
stringlengths 1
491
⌀ | journal-ref
stringlengths 4
396
⌀ | doi
stringlengths 13
97
⌀ | report-no
stringlengths 4
138
⌀ | categories
stringclasses 1
value | license
stringclasses 9
values | abstract
stringlengths 29
3.66k
| versions
listlengths 1
21
| update_date
int64 1,180B
1,718B
| authors_parsed
sequencelengths 1
98
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1810.04444 | Zheng Tian Mr | Zheng Tian, Shihao Zou, Ian Davies, Tim Warr, Lisheng Wu, Haitham Bou
Ammar, Jun Wang | Learning to Communicate Implicitly By Actions | AAAI 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In situations where explicit communication is limited, human collaborators
act by learning to: (i) infer meaning behind their partner's actions, and (ii)
convey private information about the state to their partner implicitly through
actions. The first component of this learning process has been well-studied in
multi-agent systems, whereas the second --- which is equally crucial for
successful collaboration --- has not. To mimic both components mentioned above,
thereby completing the learning process, we introduce a novel algorithm: Policy
Belief Learning (PBL). PBL uses a belief module to model the other agent's
private information and a policy module to form a distribution over actions
informed by the belief module. Furthermore, to encourage communication by
actions, we propose a novel auxiliary reward which incentivizes one agent to
help its partner to make correct inferences about its private information. The
auxiliary reward for communication is integrated into the learning of the
policy module. We evaluate our approach on a set of environments including a
matrix game, particle environment and the non-competitive bidding problem from
contract bridge. We show empirically that this auxiliary reward is effective
and easy to generalize. These results demonstrate that our PBL algorithm can
produce strong pairs of agents in collaborative games where explicit
communication is disabled.
| [
{
"version": "v1",
"created": "Wed, 10 Oct 2018 10:16:55 GMT"
},
{
"version": "v2",
"created": "Sun, 17 Feb 2019 14:05:21 GMT"
},
{
"version": "v3",
"created": "Sun, 17 Nov 2019 18:43:20 GMT"
},
{
"version": "v4",
"created": "Wed, 20 Nov 2019 20:05:08 GMT"
}
] | 1,574,380,800,000 | [
[
"Tian",
"Zheng",
""
],
[
"Zou",
"Shihao",
""
],
[
"Davies",
"Ian",
""
],
[
"Warr",
"Tim",
""
],
[
"Wu",
"Lisheng",
""
],
[
"Ammar",
"Haitham Bou",
""
],
[
"Wang",
"Jun",
""
]
] |
1810.04465 | Congqing He | Congqing He, Li Peng, Yuquan Le, Jiawei He and Xiangyu Zhu | SECaps: A Sequence Enhanced Capsule Model for Charge Prediction | 13 pages, 3figures, 5 tables | Artificial Neural Networks and Machine Learning - ICANN 2019: Text
and Time Series. ICANN 2019. Lecture Notes in Computer Science, vol 11730.
Springer, Cham | 10.1007/978-3-030-30490-4_19 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic charge prediction aims to predict appropriate final charges
according to the fact descriptions for a given criminal case. Automatic charge
prediction plays a critical role in assisting judges and lawyers to improve the
efficiency of legal decisions, and thus has received much attention.
Nevertheless, most existing works on automatic charge prediction perform
adequately on high-frequency charges but are not yet capable of predicting
few-shot charges with limited cases. In this paper, we propose a Sequence
Enhanced Capsule model, dubbed as SECaps model, to relieve this problem.
Specifically, following the work of capsule networks, we propose the seq-caps
layer, which considers sequence information and spatial information of legal
texts simultaneously. Then we design a attention residual unit, which provides
auxiliary information for charge prediction. In addition, our SECaps model
introduces focal loss, which relieves the problem of imbalanced charges.
Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4%
absolutely considerable improvements under Macro F1 in Criminal-S and
Criminal-L respectively. The experimental results consistently demonstrate the
superiorities and competitiveness of our proposed model.
| [
{
"version": "v1",
"created": "Wed, 10 Oct 2018 11:42:59 GMT"
},
{
"version": "v2",
"created": "Sat, 25 May 2019 09:16:54 GMT"
}
] | 1,568,246,400,000 | [
[
"He",
"Congqing",
""
],
[
"Peng",
"Li",
""
],
[
"Le",
"Yuquan",
""
],
[
"He",
"Jiawei",
""
],
[
"Zhu",
"Xiangyu",
""
]
] |
1810.04554 | J. G. Wolff | J Gerard Wolff | Interpreting Winograd Schemas Via the SP Theory of Intelligence and Its
Realisation in the SP Computer Model | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In 'Winograd Schema' (WS) sentences like "The city councilmen refused the
demonstrators a permit because they feared violence" and "The city councilmen
refused the demonstrators a permit because they advocated revolution", it is
easy for adults to understand what "they" refers to but can be difficult for AI
systems. This paper describes how the SP System -- outlined in an appendix --
may solve this kind of problem of interpretation. The central idea is that a
knowledge of discontinuous associations amongst linguistic features, and an
ability to recognise such patterns of associations, provides a robust means of
determining what a pronoun like "they" refers to. For any AI system to solve
this kind of problem, it needs appropriate knowledge of relevant syntax and
semantics which, ideally, it should learn for itself. Although the SP System
has some strengths in unsupervised learning, its capabilities in this area are
not yet good enough to learn the kind of knowledge needed to interpret WS
examples, so it must be supplied with such knowledge at the outset. However,
its existing strengths in unsupervised learning suggest that it has potential
to learn the kind of knowledge needed for the interpretation of WS examples. In
particular, it has potential to learn the kind of discontinuous association of
linguistic features mentioned earlier.
| [
{
"version": "v1",
"created": "Tue, 9 Oct 2018 08:30:44 GMT"
}
] | 1,539,216,000,000 | [
[
"Wolff",
"J Gerard",
""
]
] |
1810.04789 | Michael Slawinski | Michael A. Slawinski, Andy Wortman | Applications of Graph Integration to Function Comparison and Malware
Classification | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We classify .NET files as either benign or malicious by examining directed
graphs derived from the set of functions comprising the given file. Each graph
is viewed probabilistically as a Markov chain where each node represents a code
block of the corresponding function, and by computing the PageRank vector
(Perron vector with transport), a probability measure can be defined over the
nodes of the given graph. Each graph is vectorized by computing Lebesgue
antiderivatives of hand-engineered functions defined on the vertex set of the
given graph against the PageRank measure. Files are subsequently vectorized by
aggregating the set of vectors corresponding to the set of graphs resulting
from decompiling the given file. The result is a fast, intuitive, and
easy-to-compute glass-box vectorization scheme, which can be leveraged for
training a standalone classifier or to augment an existing feature space. We
refer to this vectorization technique as PageRank Measure Integration
Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla
random forest on 2.5 million samples of decompiled .NET, evenly split between
benign and malicious, from our in-house corpus and compare this model to a
baseline model which leverages a text-only feature space. The median time
needed for decompilation and scoring was 24ms.
| [
{
"version": "v1",
"created": "Thu, 11 Oct 2018 00:14:46 GMT"
},
{
"version": "v2",
"created": "Fri, 12 Oct 2018 17:33:27 GMT"
},
{
"version": "v3",
"created": "Sun, 28 Oct 2018 00:24:16 GMT"
},
{
"version": "v4",
"created": "Wed, 10 Jul 2019 01:22:29 GMT"
},
{
"version": "v5",
"created": "Mon, 19 Aug 2019 23:02:26 GMT"
},
{
"version": "v6",
"created": "Wed, 13 Nov 2019 22:38:05 GMT"
}
] | 1,573,776,000,000 | [
[
"Slawinski",
"Michael A.",
""
],
[
"Wortman",
"Andy",
""
]
] |
1810.04886 | Federico Cerutti | Pietro Baroni, Federico Cerutti, Massimiliano Giacomin, Giovanni Guida | AFRA: Argumentation framework with recursive attacks | null | null | 10.1016/j.ijar.2010.05.004 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The issue of representing attacks to attacks in argumentation is receiving an
increasing attention as a useful conceptual modelling tool in several contexts.
In this paper we present AFRA, a formalism encompassing unlimited recursive
attacks within argumentation frameworks. AFRA satisfies the basic requirements
of definition simplicity and rigorous compatibility with Dung's theory of
argumentation. This paper provides a complete development of the AFRA formalism
complemented by illustrative examples and a detailed comparison with other
recursive attack formalizations.
| [
{
"version": "v1",
"created": "Thu, 11 Oct 2018 08:16:48 GMT"
}
] | 1,539,302,400,000 | [
[
"Baroni",
"Pietro",
""
],
[
"Cerutti",
"Federico",
""
],
[
"Giacomin",
"Massimiliano",
""
],
[
"Guida",
"Giovanni",
""
]
] |
1810.04892 | Federico Cerutti | Pietro Baroni, Federico Cerutti, Paul E. Dunne, Massimiliano Giacomin | Automata for Infinite Argumentation Structures | null | null | 10.1016/j.artint.2013.05.002 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theory of abstract argumentation frameworks (afs) has, in the main,
focused on finite structures, though there are many significant contexts where
argumentation can be regarded as a process involving infinite objects. To
address this limitation, in this paper we propose a novel approach for
describing infinite afs using tools from formal language theory. In particular,
the possibly infinite set of arguments is specified through the language
recognized by a deterministic finite automaton while a suitable formalism,
called attack expression, is introduced to describe the relation of attack
between arguments. The proposed approach is shown to satisfy some desirable
properties which can not be achieved through other "naive" uses of formal
languages. In particular, the approach is shown to be expressive enough to
capture (besides any arbitrary finite structure) a large variety of infinite
afs including two major examples from previous literature and two sample cases
from the domains of multi-agent negotiation and ambient intelligence. On the
computational side, we show that several decision and construction problems
which are known to be polynomial time solvable in finite afs are decidable in
the context of the proposed formalism and we provide the relevant algorithms.
Moreover we obtain additional results concerning the case of finitary afs.
| [
{
"version": "v1",
"created": "Thu, 11 Oct 2018 08:26:59 GMT"
}
] | 1,539,302,400,000 | [
[
"Baroni",
"Pietro",
""
],
[
"Cerutti",
"Federico",
""
],
[
"Dunne",
"Paul E.",
""
],
[
"Giacomin",
"Massimiliano",
""
]
] |
1810.05110 | Resmiye Nasiboglu | Resmiye Nasiboglu, Rahila Abdullayeva | Analytical Formulations for the Level Based Weighted Average Value of
Discrete Trapezoidal Fuzzy Numbers | 15 pages, 3 figures | International Journal on Soft Computing (IJSC) Vol.9, No.2/3,
August 2018, pp.1-15 | 10.5121/ijsc.2018.9301 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In fuzzy decision-making processes based on linguistic information,
operations on discrete fuzzy numbers are commonly performed. Aggregation and
defuzzification operations are some of these often used operations. Many
aggregation and defuzzification operators produce results independent to the
decision makers strategy. On the other hand, the Weighted Average Based on
Levels (WABL) approach can take into account the level weights and the decision
makers optimism strategy. This gives flexibility to the WABL operator and,
through machine learning, can be trained in the direction of the decision
makers strategy, producing more satisfactory results for the decision maker.
However, in order to determine the WABL value, it is necessary to calculate
some integrals. In this study, the concept of WABL for discrete trapezoidal
fuzzy numbers is investigated, and analytical formulas have been proven to
facilitate the calculation of WABL value for these fuzzy numbers. Trapezoidal
and their special form, triangular fuzzy numbers, are the most commonly used
fuzzy number types in fuzzy modeling, so in this study, such numbers have been
studied. Computational examples explaining the theoretical results have been
performed.
| [
{
"version": "v1",
"created": "Thu, 13 Sep 2018 13:54:50 GMT"
}
] | 1,539,302,400,000 | [
[
"Nasiboglu",
"Resmiye",
""
],
[
"Abdullayeva",
"Rahila",
""
]
] |
1810.05315 | Brian Groenke | Brian Groenke | Learning to Reason | 13 pages, 3 figures Not an official publication. Project report only.
Available as reference to interested researchers | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated theorem proving has long been a key task of artificial
intelligence. Proofs form the bedrock of rigorous scientific inquiry. Many
tools for both partially and fully automating their derivations have been
developed over the last half a century. Some examples of state-of-the-art
provers are E (Schulz, 2013), VAMPIRE (Kov\'acs & Voronkov, 2013), and Prover9
(McCune, 2005-2010). Newer theorem provers, such as E, use superposition
calculus in place of more traditional resolution and tableau based methods.
There have also been a number of past attempts to apply machine learning
methods to guiding proof search. Suttner & Ertel proposed a
multilayer-perceptron based method using hand-engineered features as far back
as 1990; Urban et al (2011) apply machine learning to tableau calculus; and
Loos et al (2017) recently proposed a method for guiding the E theorem prover
using deep nerual networks. All of this prior work, however, has one common
limitation: they all rely on the axioms of classical first-order logic. Very
little attention has been paid to automated theorem proving for non-classical
logics. One of the only recent examples is McLaughlin & Pfenning (2008) who
applied the polarized inverse method to intuitionistic propositional logic. The
literature is otherwise mostly silent. This is truly unfortunate, as there are
many reasons to desire non-classical proofs over classical.
Constructive/intuitionistic proofs should be of particular interest to computer
scientists thanks to the well-known Curry-Howard correspondence (Howard, 1980)
which tells us that all terminating programs correspond to a proof in
intuitionistic logic and vice versa. This work explores using Q-learning
(Watkins, 1989) to inform proof search for a specific system called
non-classical logic called Core Logic (Tennant, 2017).
| [
{
"version": "v1",
"created": "Fri, 12 Oct 2018 01:50:24 GMT"
}
] | 1,539,561,600,000 | [
[
"Groenke",
"Brian",
""
]
] |
1810.05514 | Ruslan Krenzler | Ruslan Krenzler and Lin Xie and Hanyi Li | Deterministic Pod Repositioning Problem in Robotic Mobile Fulfillment
Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a robotic mobile fulfillment system, robots bring shelves, called pods,
with storage items from the storage area to pick stations. At every pick
station there is a person -- the picker -- who takes parts from the pod and
packs them into boxes according to orders. Usually there are multiple shelves
at the pick station. In this case, they build a queue with the picker at its
head. When the picker does not need the pod any more, a robot transports the
pod back to the storage area. At that time, we need to answer a question:
"Where is the optimal place in the inventory to put this pod back?". It is a
tough question, because there are many uncertainties to consider before
answering it. Moreover, each decision made to answer the question influences
the subsequent ones. The goal of this paper is to answer the question properly.
We call this problem the Pod Repositioning Problem and formulate a
deterministic model. This model is tested with different algorithms, including
binary integer programming, cheapest place, fixed place, random place, genetic
algorithms, and a novel algorithm called tetris.
| [
{
"version": "v1",
"created": "Tue, 9 Oct 2018 18:02:47 GMT"
}
] | 1,539,561,600,000 | [
[
"Krenzler",
"Ruslan",
""
],
[
"Xie",
"Lin",
""
],
[
"Li",
"Hanyi",
""
]
] |
1810.05550 | Gabriel Michau Dr. | Gabriel Michau, Yang Hu, Thomas Palm\'e and Olga Fink | Feature Learning for Fault Detection in High-Dimensional
Condition-Monitoring Signals | null | 2019, Proceedings of the Institution of Mechanical Engineers, Part
O: Journal of Risk and Reliability | 10.1177/1748006X19868335 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Complex industrial systems are continuously monitored by a large number of
heterogeneous sensors. The diversity of their operating conditions and the
possible fault types make it impossible to collect enough data for learning all
the possible fault patterns. The paper proposes an integrated automatic
unsupervised feature learning and one-class classification for fault detection
that uses data on healthy conditions only for its training. The approach is
based on stacked Extreme Learning Machines (namely Hierarchical, or HELM) and
comprises an autoencoder, performing unsupervised feature learning, stacked
with a one-class classifier monitoring the distance of the test data to the
training healthy class, thereby assessing the health of the system.
This study provides a comprehensive evaluation of HELM fault detection
capability compared to other machine learning approaches, such as stand-alone
one-class classifiers (ELM and SVM), these same one-class classifiers combined
with traditional dimensionality reduction methods (PCA) and a Deep Belief
Network. The performance is first evaluated on a synthetic dataset that
encompasses typical characteristics of condition monitoring data. Subsequently,
the approach is evaluated on a real case study of a power plant fault. The
proposed algorithm for fault detection, combining feature learning with the
one-class classifier, demonstrates a better performance, particularly in cases
where condition monitoring data contain several non-informative signals.
| [
{
"version": "v1",
"created": "Fri, 12 Oct 2018 14:38:18 GMT"
},
{
"version": "v2",
"created": "Mon, 15 Jul 2019 09:52:44 GMT"
}
] | 1,566,864,000,000 | [
[
"Michau",
"Gabriel",
""
],
[
"Hu",
"Yang",
""
],
[
"Palmé",
"Thomas",
""
],
[
"Fink",
"Olga",
""
]
] |
1810.05564 | Yosuke Fukuchi | Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, Tatsuji Takahashi,
Michita Imai | Bayesian Inference of Self-intention Attributed by Observer | null | 6th International Conference on Human-Agent Interaction (HAI '18),
2018 | 10.1145/3284432.3284438 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most of agents that learn policy for tasks with reinforcement learning (RL)
lack the ability to communicate with people, which makes human-agent
collaboration challenging. We believe that, in order for RL agents to
comprehend utterances from human colleagues, RL agents must infer the mental
states that people attribute to them because people sometimes infer an
interlocutor's mental states and communicate on the basis of this mental
inference. This paper proposes PublicSelf model, which is a model of a person
who infers how the person's own behavior appears to their colleagues. We
implemented the PublicSelf model for an RL agent in a simulated environment and
examined the inference of the model by comparing it with people's judgment. The
results showed that the agent's intention that people attributed to the agent's
movement was correctly inferred by the model in scenes where people could find
certain intentionality from the agent's behavior.
| [
{
"version": "v1",
"created": "Fri, 12 Oct 2018 15:04:14 GMT"
}
] | 1,539,561,600,000 | [
[
"Fukuchi",
"Yosuke",
""
],
[
"Osawa",
"Masahiko",
""
],
[
"Yamakawa",
"Hiroshi",
""
],
[
"Takahashi",
"Tatsuji",
""
],
[
"Imai",
"Michita",
""
]
] |
1810.05764 | Juyang Weng | Juyang Weng | A Model for Auto-Programming for General Purposes | 22 pages, 2 figures, Much of the work appeared as Juyang Weng, A
Theory of the GENISAMA Turing Machines that Automatically Program for General
Purposes, Technical Report MSU-CSE-15-13, Oct. 19, 2015, 22 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Universal Turing Machine (TM) is a model for VonNeumann computers ---
general-purpose computers. A human brain can inside-skull-automatically learn a
universal TM so that he acts as a general-purpose computer and writes a
computer program for any practical purposes. It is unknown whether a machine
can accomplish the same. This theoretical work shows how the Developmental
Network (DN) can accomplish this. Unlike a traditional TM, the TM learned by DN
is a super TM --- Grounded, Emergent, Natural, Incremental, Skulled, Attentive,
Motivated, and Abstractive (GENISAMA). A DN is free of any central controller
(e.g., Master Map, convolution, or error back-propagation). Its learning from a
teacher TM is one transition observation at a time, immediate, and error-free
until all its neurons have been initialized by early observed teacher
transitions. From that point on, the DN is no longer error-free but is always
optimal at every time instance in the sense of maximal likelihood, conditioned
on its limited computational resources and the learning experience. This letter
also extends the Church-Turing thesis to automatic programming for general
purposes and sketchily proved it.
| [
{
"version": "v1",
"created": "Fri, 12 Oct 2018 23:58:42 GMT"
}
] | 1,553,731,200,000 | [
[
"Weng",
"Juyang",
""
]
] |
1810.05814 | Bart Jacobs | Bart Jacobs | Categorical Aspects of Parameter Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parameter learning is the technique for obtaining the probabilistic
parameters in conditional probability tables in Bayesian networks from tables
with (observed) data --- where it is assumed that the underlying graphical
structure is known. There are basically two ways of doing so, referred to as
maximal likelihood estimation (MLE) and as Bayesian learning. This paper
provides a categorical analysis of these two techniques and describes them in
terms of basic properties of the multiset monad M, the distribution monad D and
the Giry monad G. In essence, learning is about the reltionships between
multisets (used for counting) on the one hand and probability distributions on
the other. These relationsips will be described as suitable natural
transformations.
| [
{
"version": "v1",
"created": "Sat, 13 Oct 2018 07:50:04 GMT"
}
] | 1,539,648,000,000 | [
[
"Jacobs",
"Bart",
""
]
] |
1810.05851 | Cunchao Tu | Haoxi Zhong, Chaojun Xiao, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu,
Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang, Jianfeng Xu | Overview of CAIL2018: Legal Judgment Prediction Competition | 6 pages, 1 table | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we give an overview of the Legal Judgment Prediction (LJP)
competition at Chinese AI and Law challenge (CAIL2018). This competition
focuses on LJP which aims to predict the judgment results according to the
given facts. Specifically, in CAIL2018 , we proposed three subtasks of LJP for
the contestants, i.e., predicting relevant law articles, charges and prison
terms given the fact descriptions. CAIL2018 has attracted several hundreds
participants (601 teams, 1, 144 contestants from 269 organizations). In this
paper, we provide a detailed overview of the task definition, related works,
outstanding methods and competition results in CAIL2018.
| [
{
"version": "v1",
"created": "Sat, 13 Oct 2018 12:18:57 GMT"
}
] | 1,539,648,000,000 | [
[
"Zhong",
"Haoxi",
""
],
[
"Xiao",
"Chaojun",
""
],
[
"Guo",
"Zhipeng",
""
],
[
"Tu",
"Cunchao",
""
],
[
"Liu",
"Zhiyuan",
""
],
[
"Sun",
"Maosong",
""
],
[
"Feng",
"Yansong",
""
],
[
"Han",
"Xianpei",
""
],
[
"Hu",
"Zhen",
""
],
[
"Wang",
"Heng",
""
],
[
"Xu",
"Jianfeng",
""
]
] |
1810.06018 | Alun Preece | Frank Stein, Alun Preece, Mihai Boicu | AAAI FSS-18: Artificial Intelligence in Government and Public Sector
Proceedings | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proceedings of the AAAI Fall Symposium on Artificial Intelligence in
Government and Public Sector, Arlington, Virginia, USA, October 18-20, 2018
| [
{
"version": "v1",
"created": "Sun, 14 Oct 2018 11:40:30 GMT"
}
] | 1,539,648,000,000 | [
[
"Stein",
"Frank",
""
],
[
"Preece",
"Alun",
""
],
[
"Boicu",
"Mihai",
""
]
] |
1810.06078 | Hui Wang | Hui Wang, Michael Emmerich, Aske Plaat | Assessing the Potential of Classical Q-learning in General Game Playing | arXiv admin note: substantial text overlap with arXiv:1802.05944 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | After the recent groundbreaking results of AlphaGo and AlphaZero, we have
seen strong interests in deep reinforcement learning and artificial general
intelligence (AGI) in game playing. However, deep learning is
resource-intensive and the theory is not yet well developed. For small games,
simple classical table-based Q-learning might still be the algorithm of choice.
General Game Playing (GGP) provides a good testbed for reinforcement learning
to research AGI. Q-learning is one of the canonical reinforcement learning
methods, and has been used by (Banerjee $\&$ Stone, IJCAI 2007) in GGP. In this
paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe,
Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to
allow comparison to Banerjee et al.. We find that Q-learning converges to a
high win rate in GGP. For the $\epsilon$-greedy strategy, we propose a first
enhancement, the dynamic $\epsilon$ algorithm. In addition, inspired by (Gelly
$\&$ Silver, ICML 2007) we combine online search (Monte Carlo Search) to
enhance offline learning, and propose QM-learning for GGP. Both enhancements
improve the performance of classical Q-learning. In this work, GGP allows us to
show, if augmented by appropriate enhancements, that classical table-based
Q-learning can perform well in small games.
| [
{
"version": "v1",
"created": "Sun, 14 Oct 2018 18:49:33 GMT"
}
] | 1,539,734,400,000 | [
[
"Wang",
"Hui",
""
],
[
"Emmerich",
"Michael",
""
],
[
"Plaat",
"Aske",
""
]
] |
1810.06284 | C\'edric Colas | C\'edric Colas, Pierre Fournier, Olivier Sigaud, Mohamed Chetouani,
Pierre-Yves Oudeyer | CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement
Learning | Accepted at ICML 2019 | Proceedings of the 36th International Conference on Machine
Learning 2019 | null | PMLR 97:1331-1340 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In open-ended environments, autonomous learning agents must set their own
goals and build their own curriculum through an intrinsically motivated
exploration. They may consider a large diversity of goals, aiming to discover
what is controllable in their environments, and what is not. Because some goals
might prove easy and some impossible, agents must actively select which goal to
practice at any moment, to maximize their overall mastery on the set of
learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a
modular Universal Value Function Approximator with hindsight learning to
achieve a diversity of goals of different kinds within a unique policy and 2)
an automated curriculum learning mechanism that biases the attention of the
agent towards goals maximizing the absolute learning progress. Agents focus
sequentially on goals of increasing complexity, and focus back on goals that
are being forgotten. Experiments conducted in a new modular-goal robotic
environment show the resulting developmental self-organization of a learning
curriculum, and demonstrate properties of robustness to distracting goals,
forgetting and changes in body properties.
| [
{
"version": "v1",
"created": "Mon, 15 Oct 2018 11:40:28 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Oct 2018 09:10:44 GMT"
},
{
"version": "v3",
"created": "Sun, 24 Feb 2019 16:41:17 GMT"
},
{
"version": "v4",
"created": "Wed, 29 May 2019 11:52:20 GMT"
}
] | 1,559,174,400,000 | [
[
"Colas",
"Cédric",
""
],
[
"Fournier",
"Pierre",
""
],
[
"Sigaud",
"Olivier",
""
],
[
"Chetouani",
"Mohamed",
""
],
[
"Oudeyer",
"Pierre-Yves",
""
]
] |
1810.06338 | Michael Cashmore | Rita Borgo, Michael Cashmore, Daniele Magazzeni | Towards Providing Explanations for AI Planner Decisions | Presented at the IJCAI/ECAI 2018 Workshop on Explainable Artificial
Intelligence (XAI)
(http://home.earthlink.net/~dwaha/research/meetings/faim18-xai). Stockholm,
July 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to engender trust in AI, humans must understand what an AI system is
trying to achieve, and why. To overcome this problem, the underlying AI process
must produce justifications and explanations that are both transparent and
comprehensible to the user. AI Planning is well placed to be able to address
this challenge. In this paper we present a methodology to provide initial
explanations for the decisions made by the planner. Explanations are created by
allowing the user to suggest alternative actions in plans and then compare the
resulting plans with the one found by the planner. The methodology is
implemented in the new XAI-Plan framework.
| [
{
"version": "v1",
"created": "Mon, 15 Oct 2018 13:13:48 GMT"
}
] | 1,539,648,000,000 | [
[
"Borgo",
"Rita",
""
],
[
"Cashmore",
"Michael",
""
],
[
"Magazzeni",
"Daniele",
""
]
] |
1810.06374 | Andrea Marrella | Andrea Marrella | SmartPM: Automatic Adaptation of Dynamic Processes at Run-Time | Postprint of PhD Thesis of Andrea Marrella, published on October 2013 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The research activity outlined in this PhD thesis is devoted to define a
general approach, a concrete architecture and a prototype Process Management
System (PMS) for the automated adaptation of dynamic processes at run-time, on
the basis of a declarative specification of process tasks and relying on
well-established reasoning about actions and planning techniques. The purpose
is to demonstrate that the combination of procedural and imperative models with
declarative elements, along with the exploitation of techniques from the field
of artificial intelligence (AI), such as Situation Calculus, IndiGolog and
automated planning, can increase the ability of existing PMSs of supporting
dynamic processes. To this end, a prototype PMS named SmartPM, which is
specifically tailored for supporting collaborative work of process participants
during pervasive scenarios, has been developed. The adaptation mechanism
deployed on SmartPM is based on execution monitoring for detecting failures at
run-time, which does not require the definition of the adaptation strategy in
the process itself (as most of the current approaches do), and on automatic
planning techniques for the synthesis of the recovery procedure.
| [
{
"version": "v1",
"created": "Fri, 12 Oct 2018 13:17:35 GMT"
}
] | 1,539,648,000,000 | [
[
"Marrella",
"Andrea",
""
]
] |
1810.06617 | Razieh Mehri | Razieh Mehri, Volker Haarslev, Hamidreza Chinaei | Optimizing Heuristics for Tableau-based OWL Reasoners | 9 pages, 0 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimization techniques play a significant role in improving description
logic reasoners covering the Web Ontology Language (OWL). These techniques are
essential to speed up these reasoners. Many of the optimization techniques are
based on heuristic choices. Optimal heuristic selection makes these techniques
more effective. The FaCT++ OWL reasoner and its Java version JFact implement an
optimization technique called ToDo list which is a substitute for a traditional
top-down approach in tableau-based reasoners. The ToDo list mechanism allows
one to arrange the order of applying different rules by giving each a priority.
Compared to a top-down approach, the ToDo list technique has a better control
over the application of expansion rules. Learning the proper heuristic order
for applying rules in ToDo lis} will have a great impact on reasoning speed. We
use a binary SVM technique to build our learning model. The model can help to
choose ontology-specific order sets to speed up OWL reasoning. On average, our
learning approach tested with 40 selected ontologies achieves a speedup of two
orders of magnitude when compared to the worst rule ordering choice.
| [
{
"version": "v1",
"created": "Mon, 15 Oct 2018 19:06:14 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Oct 2018 21:36:43 GMT"
}
] | 1,540,512,000,000 | [
[
"Mehri",
"Razieh",
""
],
[
"Haarslev",
"Volker",
""
],
[
"Chinaei",
"Hamidreza",
""
]
] |
1810.07007 | Naveen Sundar Govindarajulu | Selmer Bringsjord, Naveen Sundar Govindarajulu, Atriya Sen, Matthew
Peveler, Biplav Srivastava, Kartik Talamadupula | Tentacular Artificial Intelligence, and the Architecture Thereof,
Introduced | FAIM Workshop on Architectures And Evaluation For Generality,
Autonomy & Progress in AI July 15, 2018, Stockholm, Sweden, 1st International
Workshop Held In Conjunction With IJCAI-ECAI 2018, Aamas 2018 and ICML 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We briefly introduce herein a new form of distributed, multi-agent artificial
intelligence, which we refer to as "tentacular." Tentacular AI is distinguished
by six attributes, which among other things entail a capacity for reasoning and
planning based in highly expressive calculi (logics), and which enlists
subsidiary agents across distances circumscribed only by the reach of one or
more given networks.
| [
{
"version": "v1",
"created": "Sun, 14 Oct 2018 02:37:46 GMT"
}
] | 1,539,734,400,000 | [
[
"Bringsjord",
"Selmer",
""
],
[
"Govindarajulu",
"Naveen Sundar",
""
],
[
"Sen",
"Atriya",
""
],
[
"Peveler",
"Matthew",
""
],
[
"Srivastava",
"Biplav",
""
],
[
"Talamadupula",
"Kartik",
""
]
] |
1810.07096 | Luciano Serafini | Luciano Serafini and Paolo Traverso | Incremental learning abstract discrete planning domains and mappings to
continuous perceptions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most of the works on planning and learning, e.g., planning by (model based)
reinforcement learning, are based on two main assumptions: (i) the set of
states of the planning domain is fixed; (ii) the mapping between the
observations from the real word and the states is implicitly assumed or learned
offline, and it is not part of the planning domain. Consequently, the focus is
on learning the transitions between states. In this paper, we drop such
assumptions. We provide a formal framework in which (i) the agent can learn
dynamically new states of the planning domain; (ii) the mapping between
abstract states and the perception from the real world, represented by
continuous variables, is part of the planning domain; (iii) such mapping is
learned and updated along the "life" of the agent. We define an algorithm that
interleaves planning, acting, and learning, and allows the agent to update the
planning domain depending on how much it trusts the model w.r.t. the new
experiences learned by executing actions. We define a measure of coherence
between the planning domain and the real world as perceived by the agent. We
test our approach showing that the agent learns increasingly coherent models,
and that the system can scale to deal with models with an order of $10^6$
states.
| [
{
"version": "v1",
"created": "Tue, 16 Oct 2018 15:53:22 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Nov 2018 10:55:58 GMT"
}
] | 1,543,276,800,000 | [
[
"Serafini",
"Luciano",
""
],
[
"Traverso",
"Paolo",
""
]
] |
1810.07232 | Robert Kent | Robert E. Kent and Christian Neuss | Conceptual Analysis of Hypertext | 19 pages, 3 figures, 5 tables | In Charles Nicholas and James Mayfield, editors, Intelligent
Hypertext: Advanced Techniques for the World Wide Web, volume 1326 of Lecture
Note Computer Science, pages 70-89. Springer, 1997. Invited chapter | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this chapter tools and techniques from the mathematical theory of formal
concept analysis are applied to hypertext systems in general, and the World
Wide Web in particular. Various processes for the conceptual structuring of
hypertext are discussed: summarization, conceptual scaling, and the creation of
conceptual links. Well-known interchange formats for summarizing networked
information resources as resource meta-information are reviewed, and two new
interchange formats originating from formal concept analysis are advocated.
Also reviewed is conceptual scaling, which provides a principled approach to
the faceted analysis techniques in library science classification. The
important notion of conceptual linkage is introduced as a generalization of a
hyperlink. The automatic hyperization of the content of legacy data is
described, and the composite conceptual structuring with hypertext linkage is
defined. For the conceptual empowerment of the Web user, a new technique called
conceptual browsing is advocated. Conceptual browsing, which browses over
conceptual links, is dual mode (extensional versus intensional) and dual scope
(global versus local).
| [
{
"version": "v1",
"created": "Tue, 16 Oct 2018 18:56:33 GMT"
}
] | 1,539,820,800,000 | [
[
"Kent",
"Robert E.",
""
],
[
"Neuss",
"Christian",
""
]
] |
1810.07311 | Yuu Jinnai | Yuu Jinnai, David Abel, D Ellis Hershkowitz, Michael Littman, George
Konidaris | Finding Options that Minimize Planning Time | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We formalize the problem of selecting the optimal set of options for planning
as that of computing the smallest set of options so that planning converges in
less than a given maximum of value-iteration passes. We first show that the
problem is NP-hard, even if the task is constrained to be deterministic---the
first such complexity result for option discovery. We then present the first
polynomial-time boundedly suboptimal approximation algorithm for this setting,
and empirically evaluate it against both the optimal options and a
representative collection of heuristic approaches in simple grid-based domains
including the classic four-rooms problem.
| [
{
"version": "v1",
"created": "Tue, 16 Oct 2018 23:24:18 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Dec 2018 19:04:14 GMT"
},
{
"version": "v3",
"created": "Sat, 16 Mar 2019 20:08:18 GMT"
}
] | 1,552,953,600,000 | [
[
"Jinnai",
"Yuu",
""
],
[
"Abel",
"David",
""
],
[
"Hershkowitz",
"D Ellis",
""
],
[
"Littman",
"Michael",
""
],
[
"Konidaris",
"George",
""
]
] |
1810.07528 | David Gunning | David Gunning | Machine Common Sense Concept Paper | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper summarizes some of the technical background, research ideas, and
possible development strategies for achieving machine common sense. Machine
common sense has long been a critical-but-missing component of Artificial
Intelligence (AI). Recent advances in machine learning have resulted in new AI
capabilities, but in all of these applications, machine reasoning is narrow and
highly specialized. Developers must carefully train or program systems for
every situation. General commonsense reasoning remains elusive. The absence of
common sense prevents intelligent systems from understanding their world,
behaving reasonably in unforeseen situations, communicating naturally with
people, and learning from new experiences. Its absence is perhaps the most
significant barrier between the narrowly focused AI applications we have today
and the more general, human-like AI systems we would like to build in the
future. Machine common sense remains a broad, potentially unbounded problem in
AI. There are a wide range of strategies that could be employed to make
progress on this difficult challenge. This paper discusses two diverse
strategies for focusing development on two different machine commonsense
services: (1) a service that learns from experience, like a child, to construct
computational models that mimic the core domains of child cognition for objects
(intuitive physics), agents (intentional actors), and places (spatial
navigation); and (2) service that learns from reading the Web, like a research
librarian, to construct a commonsense knowledge repository capable of answering
natural language and image-based questions about commonsense phenomena.
| [
{
"version": "v1",
"created": "Wed, 17 Oct 2018 13:31:41 GMT"
}
] | 1,539,820,800,000 | [
[
"Gunning",
"David",
""
]
] |
1810.08059 | Alok Chauhan | Nikolas Klug, Alok Chauhan, Ramesh Ragala, V Vijayakumar | k-RNN: Extending NN-heuristics for the TSP | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present an extension of existing Nearest-Neighbor heuristics
to an algorithm called k-Repetitive-Nearest-Neighbor. The idea is to start with
a tour of k nodes and then perform a Nearest-Neighbor search from there on.
After doing this for all permutations of k nodes the result gets selected as
the shortest tour found. Experimental results show that for 2-RNN the solutions
quality remains relatively stable between about 10% to 40% above the optimum.
| [
{
"version": "v1",
"created": "Wed, 17 Oct 2018 11:32:15 GMT"
}
] | 1,539,907,200,000 | [
[
"Klug",
"Nikolas",
""
],
[
"Chauhan",
"Alok",
""
],
[
"Ragala",
"Ramesh",
""
],
[
"Vijayakumar",
"V",
""
]
] |
1810.08159 | Sandhya Saisubramanian | Sandhya Saisubramanian, Kyle Hollins Wray, Luis Pineda, Shlomo
Zilberstein | Planning in Stochastic Environments with Goal Uncertainty | 6 pages, IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS) 2019 | 2019 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS), Macau, China, 2019, pp. 1649-1654 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a
general framework to model path planning and decision making in stochastic
environments with goal uncertainty. The framework extends the stochastic
shortest path (SSP) model to dynamic environments in which it is impossible to
determine the exact goal states ahead of plan execution. GUSSPs introduce
flexibility in goal specification by allowing a belief over possible goal
configurations. The unique observations at potential goals helps the agent
identify the true goal during plan execution. The partial observability is
restricted to goals, facilitating the reduction to an SSP with a modified state
space. We formally define a GUSSP and discuss its theoretical properties. We
then propose an admissible heuristic that reduces the planning time using
FLARES -- a start-of-the-art probabilistic planner. We also propose a
determinization approach for solving this class of problems. Finally, we
present empirical results on a search and rescue mobile robot and three other
problem domains in simulation.
| [
{
"version": "v1",
"created": "Thu, 18 Oct 2018 16:56:09 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Apr 2020 23:18:14 GMT"
}
] | 1,586,217,600,000 | [
[
"Saisubramanian",
"Sandhya",
""
],
[
"Wray",
"Kyle Hollins",
""
],
[
"Pineda",
"Luis",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1810.08431 | Damien Pellier | Damien Pellier and Humbert Fiorino | Assumption-Based Planning | null | Proceedings of the International Conference on Advances in
Intelligence Systems Theory and Applications (AISTA), 2004, November, pages
367-376, Luxembourg-Kirchberg, Luxembourg | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The purpose of the paper is to introduce a new approach of planning called
Assumption-Based Planning. This approach is a very interesting way to devise a
planner based on a multi-agent system in which the production of a global
shared plan is obtained by conjecture/refutation cycles. Contrary to classical
approaches, our contribution relies on the agents reasoning that leads to the
production of a plan from planning domains. To take into account complex
environments and the partial agents knowledge, we propose to consider the
planning problem as a defeasible reasoning where the agents exchange proposals
and counter-proposals and are able to reason about uncertainty. The
argumentation dialogue between agents must not be viewed as a negotiation
process but as an investigation process in order to build a plan. In this
paper, we focus on the mechanisms that allow an agent to produce `reasonable'
proposals according to its knowledge.
| [
{
"version": "v1",
"created": "Fri, 19 Oct 2018 10:26:27 GMT"
}
] | 1,540,166,400,000 | [
[
"Pellier",
"Damien",
""
],
[
"Fiorino",
"Humbert",
""
]
] |
1810.08460 | Damien Pellier | Damien Pellier and lias. Belaidi | Planification par fusions incr\'ementales de graphes | in French | Journ\'ees Francophones de Planification, D\'ecision,
Apprentissage pour la conduite de syst\`emes (JFPDA). 2008, Metz, France | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce a generic and fresh model for distributed
planning called "Distributed Planning Through Graph Merging" ({\sf DPGM}). This
model unifies the different steps of the distributed planning process into a
single step. Our approach is based on a planning graph structure for the agent
reasoning and a CSP mechanism for the individual plan extraction and the
coordination. We assume that no agent can reach the global goal alone.
Therefore the agents must cooperate, {\it i.e.,} take in into account potential
positive interactions between their activities to reach their common shared
goal. The originality of our model consists in considering as soon as possible,
{\it i.e.,} in the individual planning process, the positive and the negative
interactions between agents activities in order to reduce the search cost of a
global coordinated solution plan.
| [
{
"version": "v1",
"created": "Fri, 19 Oct 2018 12:21:47 GMT"
}
] | 1,540,166,400,000 | [
[
"Pellier",
"Damien",
""
],
[
"Belaidi",
"lias.",
""
]
] |
1810.08811 | Yosuke Fukuchi | Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, Michita Imai | Autonomous Self-Explanation of Behavior for Interactive Reinforcement
Learning Agents | null | Proceedings of the 5th International Conference on Human Agent
Interaction Pages 97-101 2017 | 10.1145/3125739.3125746 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In cooperation, the workers must know how co-workers behave. However, an
agent's policy, which is embedded in a statistical machine learning model, is
hard to understand, and requires much time and knowledge to comprehend.
Therefore, it is difficult for people to predict the behavior of machine
learning robots, which makes Human Robot Cooperation challenging. In this
paper, we propose Instruction-based Behavior Explanation (IBE), a method to
explain an autonomous agent's future behavior. In IBE, an agent can
autonomously acquire the expressions to explain its own behavior by reusing the
instructions given by a human expert to accelerate the learning of the agent's
policy. IBE also enables a developmental agent, whose policy may change during
the cooperation, to explain its own behavior with sufficient time granularity.
| [
{
"version": "v1",
"created": "Sat, 20 Oct 2018 14:25:53 GMT"
}
] | 1,540,252,800,000 | [
[
"Fukuchi",
"Yosuke",
""
],
[
"Osawa",
"Masahiko",
""
],
[
"Yamakawa",
"Hiroshi",
""
],
[
"Imai",
"Michita",
""
]
] |
1810.08914 | Salvador Garc\'ia | Jos\'e-Ram\'on Cano and Juli\'an Luengo and Salvador Garc\'ia | Label Noise Filtering Techniques to Improve Monotonic Classification | This paper is already accepted for publication in Neurocomputing | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The monotonic ordinal classification has increased the interest of
researchers and practitioners within machine learning community in the last
years. In real applications, the problems with monotonicity constraints are
very frequent. To construct predictive monotone models from those problems,
many classifiers require as input a data set satisfying the monotonicity
relationships among all samples. Changing the class labels of the data set
(relabelling) is useful for this. Relabelling is assumed to be an important
building block for the construction of monotone classifiers and it is proved
that it can improve the predictive performance.
In this paper, we will address the construction of monotone datasets
considering as noise the cases that do not meet the monotonicity restrictions.
For the first time in the specialized literature, we propose the use of noise
filtering algorithms in a preprocessing stage with a double goal: to increase
both the monotonicity index of the models and the accuracy of the predictions
for different monotonic classifiers. The experiments are performed over 12
datasets coming from classification and regression problems and show that our
scheme improves the prediction capabilities of the monotonic classifiers
instead of being applied to original and relabeled datasets. In addition, we
have included the analysis of noise filtering process in the particular case of
wine quality classification to understand its effect in the predictive models
generated.
| [
{
"version": "v1",
"created": "Sun, 21 Oct 2018 08:36:37 GMT"
}
] | 1,540,252,800,000 | [
[
"Cano",
"José-Ramón",
""
],
[
"Luengo",
"Julián",
""
],
[
"García",
"Salvador",
""
]
] |
1810.09030 | Siwei Fu | Siwei Fu, Anbang Xu, Xiaotong Liu, Huimin Zhou, Rama Akkiraju | Challenge AI Mind: A Crowd System for Proactive AI Testing | a 10-page full paper | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial Intelligence (AI) has burrowed into our lives in various aspects;
however, without appropriate testing, deployed AI systems are often being
criticized to fail in critical and embarrassing cases. Existing testing
approaches mainly depend on fixed and pre-defined datasets, providing a limited
testing coverage. In this paper, we propose the concept of proactive testing to
dynamically generate testing data and evaluate the performance of AI systems.
We further introduce Challenge.AI, a new crowd system that features the
integration of crowdsourcing and machine learning techniques in the process of
error generation, error validation, error categorization, and error analysis.
We present experiences and insights into a participatory design with AI
developers. The evaluation shows that the crowd workflow is more effective with
the help of machine learning techniques. AI developers found that our system
can help them discover unknown errors made by the AI models, and engage in the
process of proactive testing.
| [
{
"version": "v1",
"created": "Sun, 21 Oct 2018 21:13:48 GMT"
}
] | 1,540,252,800,000 | [
[
"Fu",
"Siwei",
""
],
[
"Xu",
"Anbang",
""
],
[
"Liu",
"Xiaotong",
""
],
[
"Zhou",
"Huimin",
""
],
[
"Akkiraju",
"Rama",
""
]
] |
1810.09036 | Robert Kent | Robert E. Kent | Soft Concept Analysis | 16 pages, 5 figures, 6 tables | Rough-Fuzzy Hybridization: New Trend in Decision-Making, pages
215-232. Sankar K. Pal and Andrzej Skowron, editors, Springer-Verlag,
Singapore, June 1999 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this chapter we discuss soft concept analysis, a study which identifies an
enriched notion of "conceptual scale" as developed in formal concept analysis
with an enriched notion of "linguistic variable" as discussed in fuzzy logic.
The identification "enriched conceptual scale" = "enriched linguistic variable"
was made in a previous paper (Enriched interpretation, Robert E. Kent). In this
chapter we offer further arguments for the importance of this identification by
discussing the philosophy, spirit, and practical application of conceptual
scaling to the discovery, conceptual analysis, interpretation, and
categorization of networked information resources. We argue that a linguistic
variable, which has been defined at just the right generalization of valuated
categories, provides a natural definition for the process of soft conceptual
scaling. This enrichment using valuated categories models the relation of
indiscernability, a notion of central importance in rough set theory. At a more
fundamental level for soft concept analysis, it also models the derivation of
formal concepts, a process of central importance in formal concept analysis.
Soft concept analysis is synonymous with enriched concept analysis. From one
viewpoint, the study of soft concept analysis that is initiated here extends
formal concept analysis to soft computational structures. From another
viewpoint, soft concept analysis provides a natural foundation for soft
computation by unifying and explaining notions from soft computation in terms
of suitably generalized notions from formal concept analysis, rough set theory
and fuzzy set theory.
| [
{
"version": "v1",
"created": "Sun, 21 Oct 2018 22:30:17 GMT"
}
] | 1,540,252,800,000 | [
[
"Kent",
"Robert E.",
""
]
] |
1810.09145 | Damien Pellier | Sandra Castellanos-Paez and Damien Pellier and Humbert Fiorino and
Sylvie Pesty | Mining useful Macro-actions in Planning | International Conference on Artificial Intelligence and Pattern
Recognition, 2016 | International Conference on Artificial Intelligence and Pattern
Recognition, 2016 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planning has achieved significant progress in recent years. Among the various
approaches to scale up plan synthesis, the use of macro-actions has been widely
explored. As a first stage towards the development of a solution to learn
on-line macro-actions, we propose an algorithm to identify useful macro-actions
based on data mining techniques. The integration in the planning search of
these learned macro-actions shows significant improvements over six classical
planning benchmarks.
| [
{
"version": "v1",
"created": "Mon, 22 Oct 2018 09:05:57 GMT"
}
] | 1,541,116,800,000 | [
[
"Castellanos-Paez",
"Sandra",
""
],
[
"Pellier",
"Damien",
""
],
[
"Fiorino",
"Humbert",
""
],
[
"Pesty",
"Sylvie",
""
]
] |
1810.09150 | Damien Pellier | Damien Pellier and Bruno Bouzy and Marc M\'etivier | Mean-based Heuristic Search for Real-Time Planning | Journ\'ees Francophones de Planification, D\'ecision, Apprentissage
pour la conduite de syst\`emes, 2010 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce a new heuristic search algorithm based on mean
values for real-time planning, called MHSP. It consists in associating the
principles of UCT, a bandit-based algorithm which gave very good results in
computer games, and especially in Computer Go, with heuristic search in order
to obtain a real-time planner in the context of classical planning. MHSP is
evaluated on different planning problems and compared to existing algorithms
performing on-line search and learning. Besides, our results highlight the
capacity of MHSP to return plans in a real-time manner which tend to an optimal
plan over the time which is faster and of better quality compared to existing
algorithms in the literature.
| [
{
"version": "v1",
"created": "Mon, 22 Oct 2018 09:32:55 GMT"
}
] | 1,540,252,800,000 | [
[
"Pellier",
"Damien",
""
],
[
"Bouzy",
"Bruno",
""
],
[
"Métivier",
"Marc",
""
]
] |
1810.09171 | Fabian Neuhaus | Fabian Neuhaus | What is an Ontology? | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the knowledge engineering community "ontology" is usually defined in the
tradition of Gruber as an "explicit specification of a conceptualization".
Several variations of this definition exist. In the paper we argue that (with
one notable exception) these definitions are of no explanatory value, because
they violate one of the basic rules for good definitions: The defining
statement (the definiens) should be clearer than the term that is defined (the
definiendum). In the paper we propose a different definition of "ontology" and
discuss how it helps to explain various phenomena: the ability of ontologies to
change, the role of the choice of vocabulary, the significance of annotations,
the possibility of collaborative ontology development, and the relationship
between ontological conceptualism and ontological realism.
| [
{
"version": "v1",
"created": "Mon, 22 Oct 2018 10:47:03 GMT"
}
] | 1,540,252,800,000 | [
[
"Neuhaus",
"Fabian",
""
]
] |
1810.09245 | Damien Pellier | Ankuj Arora and Humbert Fiorino and Damien Pellier and Sylvie Pesty | A Review on Learning Planning Action Models for Socio-Communicative HRI | Workshop on Affect, Artifcial Compagnon and Interaction, 2016 | Workshop on Affect, Artifcial Compagnon and Interaction, 2016 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For social robots to be brought more into widespread use in the fields of
companionship, care taking and domestic help, they must be capable of
demonstrating social intelligence. In order to be acceptable, they must exhibit
socio-communicative skills. Classic approaches to program HRI from observed
human-human interactions fails to capture the subtlety of multimodal
interactions as well as the key structural differences between robots and
humans. The former arises due to a difficulty in quantifying and coding
multimodal behaviours, while the latter due to a difference of the degrees of
liberty between a robot and a human. However, the notion of reverse engineering
from multimodal HRI traces to learn the underlying behavioral blueprint of the
robot given multimodal traces seems an option worth exploring. With this
spirit, the entire HRI can be seen as a sequence of exchanges of speech acts
between the robot and human, each act treated as an action, bearing in mind
that the entire sequence is goal-driven. Thus, this entire interaction can be
treated as a sequence of actions propelling the interaction from its initial to
goal state, also known as a plan in the domain of AI planning. In the same
domain, this action sequence that stems from plan execution can be represented
as a trace. AI techniques, such as machine learning, can be used to learn
behavioral models (also known as symbolic action models in AI), intended to be
reusable for AI planning, from the aforementioned multimodal traces. This
article reviews recent machine learning techniques for learning planning action
models which can be applied to the field of HRI with the intent of rendering
robots as socio-communicative.
| [
{
"version": "v1",
"created": "Mon, 22 Oct 2018 13:17:28 GMT"
}
] | 1,541,116,800,000 | [
[
"Arora",
"Ankuj",
""
],
[
"Fiorino",
"Humbert",
""
],
[
"Pellier",
"Damien",
""
],
[
"Pesty",
"Sylvie",
""
]
] |
1810.09598 | Tae Wan Kim | Tae Wan Kim | Explainable artificial intelligence (XAI), the goodness criteria and the
grasp-ability test | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces the "grasp-ability test" as a "goodness" criteria by
which to compare which explanation is more or less meaningful than others for
users to understand the automated algorithmic data processing.
| [
{
"version": "v1",
"created": "Mon, 22 Oct 2018 23:40:20 GMT"
}
] | 1,540,425,600,000 | [
[
"Kim",
"Tae Wan",
""
]
] |
1810.09648 | Shi Feng | Shi Feng, Jordan Boyd-Graber | What can AI do for me: Evaluating Machine Learning Interpretations in
Cooperative Play | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning is an important tool for decision making, but its ethical
and responsible application requires rigorous vetting of its interpretability
and utility: an understudied problem, particularly for natural language
processing models. We propose an evaluation of interpretation on a real task
with real human users, where the effectiveness of interpretation is measured by
how much it improves human performance. We design a grounded, realistic
human-computer cooperative setting using a question answering task, Quizbowl.
We recruit both trivia experts and novices to play this game with computer as
their teammate, who communicates its prediction via three different
interpretations. We also provide design guidance for natural language
processing human-in-the-loop settings.
| [
{
"version": "v1",
"created": "Tue, 23 Oct 2018 03:59:22 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Oct 2018 14:34:06 GMT"
},
{
"version": "v3",
"created": "Mon, 10 Jun 2019 02:39:28 GMT"
}
] | 1,560,211,200,000 | [
[
"Feng",
"Shi",
""
],
[
"Boyd-Graber",
"Jordan",
""
]
] |
1810.09993 | Tomer Libal | Tomer Libal and Matteo Pascucci | Automated Reasoning in Normative Detachment Structures with Ideal
Conditions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Systems of deontic logic suffer either from being too expressive and
therefore hard to mechanize, or from being too simple to capture relevant
aspects of normative reasoning. In this article we look for a suitable way in
between: the automation of a simple logic of normative ideality and
sub-ideality that is not affected by many deontic paradoxes and that is
expressive enough to capture contrary-to-duty reason- ing. We show that this
logic is very useful to reason on normative scenarios from which one can
extract a certain kind of argumentative structure, called a Normative
Detachment Structure with Ideal Conditions. The theoretical analysis of the
logic is accompanied by examples of automated reasoning on a concrete legal
text.
| [
{
"version": "v1",
"created": "Tue, 23 Oct 2018 17:52:36 GMT"
}
] | 1,540,339,200,000 | [
[
"Libal",
"Tomer",
""
],
[
"Pascucci",
"Matteo",
""
]
] |
1810.10102 | Saleh Mousa | Saleh Mousa, Sherif Ishak | Comparative Evaluation of Tree-Based Ensemble Algorithms for Short-Term
Travel Time Prediction | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Disseminating accurate travel time information to road users helps achieve
traffic equilibrium and reduce traffic congestion. The deployment of Connected
Vehicles technology will provide unique opportunities for the implementation of
travel time prediction models. The aim of this study is twofold: (1) estimate
travel times in the freeway network at five-minute intervals using Basic Safety
Messages (BSM); (2) develop an eXtreme Gradient Boosting (XGB) model for
short-term travel time prediction on freeways. The XGB tree-based ensemble
prediction model is evaluated against common tree-based ensemble algorithms and
the evaluations are performed at five-minute intervals over a 30-minute
horizon. BSMs generated by the Safety Pilot Model Deployment conducted in Ann
Arbor, Michigan, were used. Nearly two billion messages were processed for
providing travel time estimates for the entire freeway network. A Combination
of grid search and five-fold cross-validation techniques using the travel time
estimates were used for developing the prediction models and tuning their
parameters. About 9.6 km freeway stretch was used for evaluating the XGB
together with the most common tree-based ensemble algorithms. The results show
that XGB is superior to all other algorithms, followed by the Gradient
Boosting. XGB travel time predictions were accurate and consistent with
variations during peak periods, with mean absolute percentage error in
prediction about 5.9% and 7.8% for 5-minute and 30-minute horizons,
respectively. Additionally, through applying the developed models to another
4.7 km stretch along the eastbound segment of M-14, the XGB demonstrated its
considerable advantages in travel time prediction during congested and
uncongested conditions.
| [
{
"version": "v1",
"created": "Tue, 23 Oct 2018 21:41:41 GMT"
}
] | 1,540,425,600,000 | [
[
"Mousa",
"Saleh",
""
],
[
"Ishak",
"Sherif",
""
]
] |
1810.10175 | Ye Liu | Ye Liu, Jiawei Zhang, Chenwei Zhang, Philip S. Yu | Data-driven Blockbuster Planning on Online Movie Knowledge Library | IEEE BigData | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the era of big data, logistic planning can be made data-driven to take
advantage of accumulated knowledge in the past. While in the movie industry,
movie planning can also exploit the existing online movie knowledge library to
achieve better results. However, it is ineffective to solely rely on
conventional heuristics for movie planning, due to a large number of existing
movies and various real-world factors that contribute to the success of each
movie, such as the movie genre, available budget, production team (involving
actor, actress, director, and writer), etc. In this paper, we study a
"Blockbuster Planning" (BP) problem to learn from previous movies and plan for
low budget yet high return new movies in a totally data-driven fashion. After a
thorough investigation of an online movie knowledge library, a novel movie
planning framework "Blockbuster Planning with Maximized Movie Configuration
Acquaintance" (BigMovie) is introduced in this paper. From the investment
perspective, BigMovie maximizes the estimated gross of the planned movies with
a given budget. It is able to accurately estimate the movie gross with a 0.26
mean absolute percentage error (and 0.16 for budget). Meanwhile, from the
production team's perspective, BigMovie is able to formulate an optimized team
with people/movie genres that team members are acquainted with. Historical
collaboration records are utilized to estimate acquaintance scores of movie
configuration factors via an acquaintance tensor. We formulate the BP problem
as a non-linear binary programming problem and prove its NP-hardness. To solve
it in polynomial time, BigMovie relaxes the hard binary constraints and
addresses the BP problem as a cubic programming problem. Extensive experiments
conducted on IMDB movie database demonstrate the capability of BigMovie for an
effective data-driven blockbuster planning.
| [
{
"version": "v1",
"created": "Wed, 24 Oct 2018 03:56:07 GMT"
}
] | 1,540,425,600,000 | [
[
"Liu",
"Ye",
""
],
[
"Zhang",
"Jiawei",
""
],
[
"Zhang",
"Chenwei",
""
],
[
"Yu",
"Philip S.",
""
]
] |
1810.10907 | Damien Pellier | Damien Pellier and Bruno Bouzy and Marc M\'etivier | Planification en temps r\'eel avec agenda de buts et sauts | in French, Journ\'ees Francophones de Planification, D\'ecision,
Apprentissage pour la conduite de syst\`emes, 2011 | Journ\'ees Francophones de Planification, D\'ecision,
Apprentissage pour la conduite de syst\`emes, 2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the context of real-time planning, this paper investigates the
contributions of two enhancements for selecting actions. First, the
agenda-driven planning enhancement ranks relevant atomic goals and solves them
incrementally in a best-first manner. Second, the committed jump enhancement
commits a sequence of actions to be executed at the following time steps. To
assess these two enhancements, we developed a real-time planning algorithm in
which action selection can be driven by a goal-agenda, and committed jumps can
be done. Experimental results, performed on classical planning problems, show
that agenda-planning and committed jumps are clear advantages in the real-time
context. Used simultaneously, they enable the planner to be several orders of
magnitude faster and solution plans to be shorter.
| [
{
"version": "v1",
"created": "Mon, 22 Oct 2018 10:25:01 GMT"
}
] | 1,541,116,800,000 | [
[
"Pellier",
"Damien",
""
],
[
"Bouzy",
"Bruno",
""
],
[
"Métivier",
"Marc",
""
]
] |
1810.10908 | Damien Pellier | Damien Pellier and Micka\"el Vanneufville and Humbert Fiorino and Marc
M\'etivier and Bruno Bouzy | MGP: Un algorithme de planification temps r\'eel prenant en compte
l'\'evolution dynamique du but | in French | Journ\'ees Francophones de Planification, D\'ecision,
Apprentissage pour la conduite de Syst\`emes, 2012 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Devising intelligent robots or agents that interact with humans is a major
challenge for artificial intelligence. In such contexts, agents must constantly
adapt their decisions according to human activities and modify their goals. In
this paper, we tackle this problem by introducing a novel planning approach,
called Moving Goal Planning (MGP), to adapt plans to goal evolutions. This
planning algorithm draws inspiration from Moving Target Search (MTS)
algorithms. In order to limit the number of search iterations and to improve
its efficiency, MGP delays as much as possible triggering new searches when the
goal changes over time. To this purpose, MGP uses two strategies: Open Check
(OC) that checks if the new goal is still in the current search tree and Plan
Follow (PF) that estimates whether executing actions of the current plan brings
MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to
update incrementally the search tree at each new search that reduces the number
of calls to the heuristic function and speeds up the search. Finally, we show
evaluation results that demonstrate the effectiveness of our approach.
| [
{
"version": "v1",
"created": "Mon, 22 Oct 2018 12:02:56 GMT"
}
] | 1,540,512,000,000 | [
[
"Pellier",
"Damien",
""
],
[
"Vanneufville",
"Mickaël",
""
],
[
"Fiorino",
"Humbert",
""
],
[
"Métivier",
"Marc",
""
],
[
"Bouzy",
"Bruno",
""
]
] |
1810.10910 | Damien Pellier | Abdeldjalil Ramoul and Damien Pellier and Humbert Fiorino and Sylvie
Pesty | Une approche totalement instanci\'ee pour la planification HTN | in French, Journ\'ees Francophones de Planification de D\'ecision et
d'Apprentissage, 2016 | Journ\'ees Francophones de Planification de D\'ecision et
d'Apprentissage, 2016 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many planning techniques have been developed to allow autonomous systems to
act and make decisions based on their perceptions of the environment. Among
these techniques, HTN ({\it Hierarchical Task Network}) planning is one of the
most used in practice. Unlike classical approaches of planning. HTN operates by
decomposing task into sub-tasks until each of these sub-tasks can be achieved
an action. This hierarchical representation provide a richer representation of
planning problems and allows to better guide the plan search and provides more
knowledge to the underlying algorithms. In this paper, we propose a new
approach of HTN planning in which, as in conventional planning, we instantiate
all planning operators before starting the search process. This approach has
proven its effectiveness in classical planning and is necessary for the
development of effective heuristics and encoding planning problems in other
formalism such as CSP or SAT. The instantiation is actually used by most modern
planners but has never been applied in an HTN based planning framework. We
present in this article a generic instantiation algorithm which implements many
simplification techniques to reduce the process complexity inspired from those
used in classical planning. Finally we present some results obtained from an
experimentation on a range of problems used in the international planning
competitions with a modified version of SHOP planner using fully instantiated
problems.
| [
{
"version": "v1",
"created": "Mon, 22 Oct 2018 14:06:48 GMT"
}
] | 1,541,116,800,000 | [
[
"Ramoul",
"Abdeldjalil",
""
],
[
"Pellier",
"Damien",
""
],
[
"Fiorino",
"Humbert",
""
],
[
"Pesty",
"Sylvie",
""
]
] |
1810.11078 | Jaroslaw Jankowski | Jaros{\l}aw W\k{a}tr\'obski, Jaros{\l}aw Jankowski, Pawe{\l} Ziemba,
Artur Karczmarczyk, Magdalena Zio{\l}o | Generalised framework for multi-criteria method selection | null | J.W\k{a}tr\'obski, J.Jankowski, P.Ziemba, A.Karczmarczyk and
M.Zio{\l}o, Generalised framework for multi-criteria method selection, Omega
(2018), doi.org/10.1016/j.omega.2018.07.004 | 10.1016/j.omega.2018.07.004 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-Criteria Decision Analysis (MCDA) methods are widely used in various
fields and disciplines. While most of the research has been focused on the
development and improvement of new MCDA methods, relatively limited attention
has been paid to their appropriate selection for the given decision problem.
Their improper application decreases the quality of recommendations, as
different MCDA methods deliver inconsistent results. The current paper presents
a methodological and practical framework for selecting suitable MCDA methods
for a particular decision situation. A set of 56 available MCDA methods was
analyzed and, based on that, a hierarchical set of methods characteristics and
the rule base were obtained. This analysis, rules and modelling of the
uncertainty in the decision problem description allowed to build a framework
supporting the selection of a MCDA method for a given decision-making
situation. The practical studies indicate consistency between the methods
recommended with the proposed approach and those used by the experts in
reference cases. The results of the research also showed that the proposed
approach can be used as a general framework for selecting an appropriate MCDA
method for a given area of decision support, even in cases of data gaps in the
decision-making problem description. The proposed framework was implemented
within a web platform available for public use at www.mcda.it.
| [
{
"version": "v1",
"created": "Thu, 25 Oct 2018 19:29:46 GMT"
}
] | 1,540,771,200,000 | [
[
"Wątróbski",
"Jarosław",
""
],
[
"Jankowski",
"Jarosław",
""
],
[
"Ziemba",
"Paweł",
""
],
[
"Karczmarczyk",
"Artur",
""
],
[
"Zioło",
"Magdalena",
""
]
] |
1810.11116 | Tae Wan Kim | Tae Wan Kim, Thomas Donaldson, and John Hooker | Mimetic vs Anchored Value Alignment in Artificial Intelligence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | "Value alignment" (VA) is considered as one of the top priorities in AI
research. Much of the existing research focuses on the "A" part and not the "V"
part of "value alignment." This paper corrects that neglect by emphasizing the
"value" side of VA and analyzes VA from the vantage point of requirements in
value theory, in particular, of avoiding the "naturalistic fallacy"--a major
epistemic caveat. The paper begins by isolating two distinct forms of VA:
"mimetic" and "anchored." Then it discusses which VA approach better avoids the
naturalistic fallacy. The discussion reveals stumbling blocks for VA approaches
that neglect implications of the naturalistic fallacy. Such problems are more
serious in mimetic VA since the mimetic process imitates human behavior that
may or may not rise to the level of correct ethical behavior. Anchored VA,
including hybrid VA, in contrast, holds more promise for future VA since it
anchors alignment by normative concepts of intrinsic value.
| [
{
"version": "v1",
"created": "Thu, 25 Oct 2018 21:34:21 GMT"
}
] | 1,540,771,200,000 | [
[
"Kim",
"Tae Wan",
""
],
[
"Donaldson",
"Thomas",
""
],
[
"Hooker",
"John",
""
]
] |
1810.11370 | Abigail Chown | Abigail H. Chown, Christopher J. Cook, Nigel B. Wilding | A simulated annealing approach to the student-project allocation problem | 22 pages, 6 figures | American Journal of Physics, 2018, Volume 86, Issue 9, Page 701 | 10.1119/1.5045331 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a solution to the student-project allocation problem using
simulated annealing. The problem involves assigning students to projects, where
each student has ranked a fixed number of projects in order of preference. Each
project is offered by a specific supervisor (or supervisors), and the goal is
to find an optimal matching of students to projects taking into account the
students' preferences, the constraint that only one student can be assigned to
a given project, and the constraint that supervisors have a maximum workload.
We show that when applied to a real dataset from a university physics
department, simulated annealing allows the rapid determination of high quality
solutions to this allocation problem. The quality of the solution is quantified
by a satisfaction metric derived from empirical student survey data. Our
approach provides high quality allocations in a matter of minutes that are as
good as those found previously by the course organizer using a laborious
trial-and-error approach. We investigate how the quality of the allocation is
affected by the ratio of the number of projects offered to the number of
students and the number of projects ranked by each student. We briefly discuss
how our approach can be generalized to include other types of constraints and
discuss its potential applicability to wider allocation problems.
| [
{
"version": "v1",
"created": "Mon, 22 Oct 2018 14:23:13 GMT"
}
] | 1,540,771,200,000 | [
[
"Chown",
"Abigail H.",
""
],
[
"Cook",
"Christopher J.",
""
],
[
"Wilding",
"Nigel B.",
""
]
] |
1810.11488 | Sankalp Garg | Aniket Bajpai, Sankalp Garg, Mausam | Transfer of Deep Reactive Policies for MDP Planning | To appear at NIPS 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain-independent probabilistic planners input an MDP description in a
factored representation language such as PPDDL or RDDL, and exploit the
specifics of the representation for faster planning. Traditional algorithms
operate on each problem instance independently, and good methods for
transferring experience from policies of other instances of a domain to a new
instance do not exist. Recently, researchers have begun exploring the use of
deep reactive policies, trained via deep reinforcement learning (RL), for MDP
planning domains. One advantage of deep reactive policies is that they are more
amenable to transfer learning.
In this paper, we present the first domain-independent transfer algorithm for
MDP planning domains expressed in an RDDL representation. Our architecture
exploits the symbolic state configuration and transition function of the domain
(available via RDDL) to learn a shared embedding space for states and
state-action pairs for all problem instances of a domain. We then learn an RL
agent in the embedding space, making a near zero-shot transfer possible, i.e.,
without much training on the new instance, and without using the domain
simulator at all. Experiments on three different benchmark domains underscore
the value of our transfer algorithm. Compared against planning from scratch,
and a state-of-the-art RL transfer algorithm, our transfer solution has
significantly superior learning curves.
| [
{
"version": "v1",
"created": "Fri, 26 Oct 2018 18:28:42 GMT"
}
] | 1,540,857,600,000 | [
[
"Bajpai",
"Aniket",
""
],
[
"Garg",
"Sankalp",
""
],
[
"Mausam",
"",
""
]
] |
1810.11937 | Satvik Jain | Satvik Jain, Arun Balaji Buduru, Anshuman Chhabra | An approach to predictively securing critical cloud infrastructures
through probabilistic modeling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cloud infrastructures are being increasingly utilized in critical
infrastructures such as banking/finance, transportation and utility management.
Sophistication and resources used in recent security breaches including those
on critical infrastructures show that attackers are no longer limited by
monetary/computational constraints. In fact, they may be aided by entities with
large financial and human resources. Hence there is urgent need to develop
predictive approaches for cyber defense to strengthen cloud infrastructures
specifically utilized by critical infrastructures. Extensive research has been
done in the past on applying techniques such as Game Theory, Machine Learning
and Bayesian Networks among others for the predictive defense of critical
infrastructures. However a major drawback of these approaches is that they do
not incorporate probabilistic human behavior which limits their predictive
ability. In this paper, a stochastic approach is proposed to predict less
secure states in critical cloud systems which might lead to potential security
breaches. These less-secure states are deemed as `risky' states in our
approach. Markov Decision Process (MDP) is used to accurately incorporate user
behavior(s) as well as operational behavior of the cloud infrastructure through
a set of features. The developed reward/cost mechanism is then used to select
appropriate `actions' to identify risky states at future time steps by learning
an optimal policy. Experimental results show that the proposed framework
performs well in identifying future `risky' states. Through this work we
demonstrate the effectiveness of using probabilistic modeling (MDP) to
predictively secure critical cloud infrastructures.
| [
{
"version": "v1",
"created": "Mon, 29 Oct 2018 03:34:56 GMT"
}
] | 1,540,857,600,000 | [
[
"Jain",
"Satvik",
""
],
[
"Buduru",
"Arun Balaji",
""
],
[
"Chhabra",
"Anshuman",
""
]
] |
1810.13314 | Naman Goel | Naman Goel and Boi Faltings | Crowdsourcing with Fairness, Diversity and Budget Constraints | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies have shown that the labels collected from crowdworkers can be
discriminatory with respect to sensitive attributes such as gender and race.
This raises questions about the suitability of using crowdsourced data for
further use, such as for training machine learning algorithms. In this work, we
address the problem of fair and diverse data collection from a crowd under
budget constraints. We propose a novel algorithm which maximizes the expected
accuracy of the collected data, while ensuring that the errors satisfy desired
notions of fairness. We provide guarantees on the performance of our algorithm
and show that the algorithm performs well in practice through experiments on a
real dataset.
| [
{
"version": "v1",
"created": "Wed, 31 Oct 2018 14:46:17 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Mar 2019 13:29:53 GMT"
}
] | 1,551,657,600,000 | [
[
"Goel",
"Naman",
""
],
[
"Faltings",
"Boi",
""
]
] |
1810.13354 | Ronen Brafman | Amos Beimel and Ronen I. Brafman | Privacy Preserving Multi-Agent Planning with Provable Guarantees | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In privacy-preserving multi-agent planning, a group of agents attempt to
cooperatively solve a multi-agent planning problem while maintaining private
their data and actions. Although much work was carried out in this area in past
years, its theoretical foundations have not been fully worked out.
Specifically, although algorithms with precise privacy guarantees exist, even
their most efficient implementations are not fast enough on realistic
instances, whereas for practical algorithms no meaningful privacy guarantees
exist. Secure-MAFS, a variant of the multi-agent forward search algorithm
(MAFS) is the only practical algorithm to attempt to offer more precise
guarantees, but only in very limited settings and with proof sketches only. In
this paper we formulate a precise notion of secure computation for search-based
algorithms and prove that Secure MAFS has this property in all domains.
| [
{
"version": "v1",
"created": "Wed, 31 Oct 2018 15:47:12 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Nov 2018 10:24:06 GMT"
}
] | 1,541,116,800,000 | [
[
"Beimel",
"Amos",
""
],
[
"Brafman",
"Ronen I.",
""
]
] |
1811.00090 | Fangkai Yang | Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson | SDRL: Interpretable and Data-efficient Deep Reinforcement Learning
Leveraging Symbolic Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep reinforcement learning (DRL) has gained great success by learning
directly from high-dimensional sensory inputs, yet is notorious for the lack of
interpretability. Interpretability of the subtasks is critical in hierarchical
decision-making as it increases the transparency of black-box-style DRL
approach and helps the RL practitioners to understand the high-level behavior
of the system better. In this paper, we introduce symbolic planning into DRL
and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can
handle both high-dimensional sensory inputs and symbolic planning. The
task-level interpretability is enabled by relating symbolic actions to
options.This framework features a planner -- controller -- meta-controller
architecture, which takes charge of subtask scheduling, data-driven subtask
learning, and subtask evaluation, respectively. The three components
cross-fertilize each other and eventually converge to an optimal symbolic plan
along with the learned subtasks, bringing together the advantages of long-term
planning capability with symbolic knowledge and end-to-end reinforcement
learning directly from a high-dimensional sensory input. Experimental results
validate the interpretability of subtasks, along with improved data efficiency
compared with state-of-the-art approaches.
| [
{
"version": "v1",
"created": "Wed, 31 Oct 2018 19:56:06 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Nov 2018 22:24:09 GMT"
},
{
"version": "v3",
"created": "Fri, 30 Nov 2018 23:01:23 GMT"
},
{
"version": "v4",
"created": "Thu, 28 Feb 2019 18:24:19 GMT"
}
] | 1,551,398,400,000 | [
[
"Lyu",
"Daoming",
""
],
[
"Yang",
"Fangkai",
""
],
[
"Liu",
"Bo",
""
],
[
"Gustafson",
"Steven",
""
]
] |
1811.00265 | Rui Wang | Rui Wang and Deyu Zhou and Yulan He | ATM:Adversarial-neural Topic Model | Published at the journal Information Processing & Management | Information Processing & Management, Volume 56, Issue 6, November
2019, 102098 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Topic models are widely used for thematic structure discovery in text. But
traditional topic models often require dedicated inference procedures for
specific tasks at hand. Also, they are not designed to generate word-level
semantic representations. To address these limitations, we propose a topic
modeling approach based on Generative Adversarial Nets (GANs), called
Adversarial-neural Topic Model (ATM). The proposed ATM models topics with
Dirichlet prior and employs a generator network to capture the semantic
patterns among latent topics. Meanwhile, the generator could also produce
word-level semantic representations. To illustrate the feasibility of porting
ATM to tasks other than topic modeling, we apply ATM for open domain event
extraction. Our experimental results on the two public corpora show that ATM
generates more coherence topics, outperforming a number of competitive
baselines. Moreover, ATM is able to extract meaningful events from news
articles.
| [
{
"version": "v1",
"created": "Thu, 1 Nov 2018 07:18:31 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Aug 2019 09:34:04 GMT"
}
] | 1,566,432,000,000 | [
[
"Wang",
"Rui",
""
],
[
"Zhou",
"Deyu",
""
],
[
"He",
"Yulan",
""
]
] |
1811.00797 | Natalia Soboleva | Anton Andreychuk (1), Natalia Soboleva (2), Konstantin Yakovlev (2, 3,
4) ((1) Peoples' Friendship University of Russia, (2) National Research
University Higher School of Economics, (3) Federal Research Center ''Computer
Science and Control'' of Russian Academy of Sciences, (4) Moscow Institute of
Physics and Technology ) | eLIAN: Enhanced Algorithm for Angle-constrained Path Finding | null | Kuznetsov S., Osipov G., Stefanuk V. (eds) Artificial
Intelligence. RCAI 2018. Communications in Computer and Information Science,
vol 934. Springer, Cham | 10.1007/978-3-030-00617-4_19 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Problem of finding 2D paths of special shape, e.g. paths comprised of line
segments having the property that the angle between any two consecutive
segments does not exceed the predefined threshold, is considered in the paper.
This problem is harder to solve than the one when shortest paths of any shape
are sought, since the planer's search space is substantially bigger as multiple
search nodes corresponding to the same location need to be considered. One way
to reduce the search effort is to fix the length of the path's segment and to
prune the nodes that violate the imposed constraint. This leads to
incompleteness and to the sensitivity of the 's performance to chosen parameter
value. In this work we introduce a novel technique that reduces this
sensitivity by automatically adjusting the length of the path's segment
on-the-fly, e.g. during the search. Embedding this technique into the known
grid-based angle-constrained path finding algorithm - LIAN, leads to notable
increase of the planner's effectiveness, e.g. success rate, while keeping
efficiency, e.g. runtime, overhead at reasonable level. Experimental evaluation
shows that LIAN with the suggested enhancements, dubbed eLIAN, solves up to
20\% of tasks more compared to the predecessor. Meanwhile, the solution quality
of eLIAN is nearly the same as the one of LIAN.
| [
{
"version": "v1",
"created": "Fri, 2 Nov 2018 09:54:35 GMT"
}
] | 1,541,376,000,000 | [
[
"Andreychuk",
"Anton",
""
],
[
"Soboleva",
"Natalia",
""
],
[
"Yakovlev",
"Konstantin",
""
]
] |
1811.01147 | Sharon Levy | Sharon Levy, Wenhan Xiong, Elizabeth Belding, William Yang Wang | SafeRoute: Learning to Navigate Streets Safely in an Urban Environment | 8 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies show that 85% of women have changed their traveled route to
avoid harassment and assault. Despite this, current mapping tools do not
empower users with information to take charge of their personal safety. We
propose SafeRoute, a novel solution to the problem of navigating cities and
avoiding street harassment and crime. Unlike other street navigation
applications, SafeRoute introduces a new type of path generation via deep
reinforcement learning. This enables us to successfully optimize for
multi-criteria path-finding and incorporate representation learning within our
framework. Our agent learns to pick favorable streets to create a safe and
short path with a reward function that incorporates safety and efficiency.
Given access to recent crime reports in many urban cities, we train our model
for experiments in Boston, New York, and San Francisco. We test our model on
areas of these cities, specifically the populated downtown regions where
tourists and those unfamiliar with the streets walk. We evaluate SafeRoute and
successfully improve over state-of-the-art methods by up to 17% in local
average distance from crimes while decreasing path length by up to 7%.
| [
{
"version": "v1",
"created": "Sat, 3 Nov 2018 03:16:11 GMT"
}
] | 1,541,462,400,000 | [
[
"Levy",
"Sharon",
""
],
[
"Xiong",
"Wenhan",
""
],
[
"Belding",
"Elizabeth",
""
],
[
"Wang",
"William Yang",
""
]
] |
1811.01439 | Brent Mittelstadt | Brent Mittelstadt, Chris Russell, Sandra Wachter | Explaining Explanations in AI | FAT* 2019 Proceedings | null | 10.1145/3287560.3287574 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work on interpretability in machine learning and AI has focused on the
building of simplified models that approximate the true criteria used to make
decisions. These models are a useful pedagogical device for teaching trained
professionals how to predict what decisions will be made by the complex system,
and most importantly how the system might break. However, when considering any
such model it's important to remember Box's maxim that "All models are wrong
but some are useful." We focus on the distinction between these models and
explanations in philosophy and sociology. These models can be understood as a
"do it yourself kit" for explanations, allowing a practitioner to directly
answer "what if questions" or generate contrastive explanations without
external assistance. Although a valuable ability, giving these models as
explanations appears more difficult than necessary, and other forms of
explanation may not have the same trade-offs. We contrast the different schools
of thought on what makes an explanation, and suggest that machine learning
might benefit from viewing the problem more broadly.
| [
{
"version": "v1",
"created": "Sun, 4 Nov 2018 21:35:16 GMT"
}
] | 1,541,462,400,000 | [
[
"Mittelstadt",
"Brent",
""
],
[
"Russell",
"Chris",
""
],
[
"Wachter",
"Sandra",
""
]
] |
1811.01741 | Minne Li | Lisheng Wu, Minne Li, Jun Wang | Learning Shared Dynamics with Meta-World Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans have consciousness as the ability to perceive events and objects: a
mental model of the world developed from the most impoverished of visual
stimuli, enabling humans to make rapid decisions and take actions. Although
spatial and temporal aspects of different scenes are generally diverse, the
underlying physics among environments still work the same way, thus learning an
abstract description of shared physical dynamics helps human to understand the
world. In this paper, we explore building this mental world with neural network
models through multi-task learning, namely the meta-world model. We show
through extensive experiments that our proposed meta-world models successfully
capture the common dynamics over the compact representations of visually
different environments from Atari Games. We also demonstrate that agents
equipped with our meta-world model possess the ability of visual
self-recognition, i.e., recognize themselves from the reflected mirrored
environment derived from the classic mirror self-recognition test (MSR).
| [
{
"version": "v1",
"created": "Mon, 5 Nov 2018 14:38:45 GMT"
}
] | 1,541,462,400,000 | [
[
"Wu",
"Lisheng",
""
],
[
"Li",
"Minne",
""
],
[
"Wang",
"Jun",
""
]
] |
1811.02178 | Feifan Xu | Feifan Xu, Fei He, Enze Xie, Liang Li | Fast OBDD Reordering using Neural Message Passing on Hypergraph | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ordered binary decision diagrams (OBDDs) are an efficient data structure for
representing and manipulating Boolean formulas. With respect to different
variable orders, the OBDDs' sizes may vary from linear to exponential in the
number of the Boolean variables. Finding the optimal variable order has been
proved a NP-complete problem. Many heuristics have been proposed to find a
near-optimal solution of this problem. In this paper, we propose a neural
network-based method to predict near-optimal variable orders for unknown
formulas. Viewing these formulas as hypergraphs, and lifting the message
passing neural network into 3-hypergraph (MPNN3), we are able to learn the
patterns of Boolean formula. Compared to the traditional methods, our method
can find a near-the-best solution with an extremely shorter time, even for some
hard examples.To the best of our knowledge, this is the first work on applying
neural network to OBDD reordering.
| [
{
"version": "v1",
"created": "Tue, 6 Nov 2018 06:07:09 GMT"
}
] | 1,541,548,800,000 | [
[
"Xu",
"Feifan",
""
],
[
"He",
"Fei",
""
],
[
"Xie",
"Enze",
""
],
[
"Li",
"Liang",
""
]
] |
1811.02188 | Ritchie Lee | Ritchie Lee, Ole J. Mengshoel, Anshu Saksena, Ryan Gardner, Daniel
Genin, Joshua Silbermann, Michael Owen, Mykel J. Kochenderfer | Adaptive Stress Testing: Finding Likely Failure Events with
Reinforcement Learning | 36 pages, 17 figures, 5 tables | Journal of Artificial Intelligence Research (JAIR) 69 (2020)
1165-1201 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Finding the most likely path to a set of failure states is important to the
analysis of safety-critical systems that operate over a sequence of time steps,
such as aircraft collision avoidance systems and autonomous cars. In many
applications such as autonomous driving, failures cannot be completely
eliminated due to the complex stochastic environment in which the system
operates. As a result, safety validation is not only concerned about whether a
failure can occur, but also discovering which failures are most likely to
occur. This article presents adaptive stress testing (AST), a framework for
finding the most likely path to a failure event in simulation. We consider a
general black box setting for partially observable and continuous-valued
systems operating in an environment with stochastic disturbances. We formulate
the problem as a Markov decision process and use reinforcement learning to
optimize it. The approach is simulation-based and does not require internal
knowledge of the system, making it suitable for black-box testing of large
systems. We present formulations for fully observable and partially observable
systems. In the latter case, we present a modified Monte Carlo tree search
algorithm that only requires access to the pseudorandom number generator of the
simulator to overcome partial observability. We also present an extension of
the framework, called differential adaptive stress testing (DAST), that can
find failures that occur in one system but not in another. This type of
differential analysis is useful in applications such as regression testing,
where we are concerned with finding areas of relative weakness compared to a
baseline. We demonstrate the effectiveness of the approach on an aircraft
collision avoidance application, where a prototype aircraft collision avoidance
system is stress tested to find the most likely scenarios of near mid-air
collision.
| [
{
"version": "v1",
"created": "Tue, 6 Nov 2018 06:49:47 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Jun 2020 21:21:48 GMT"
},
{
"version": "v3",
"created": "Fri, 4 Dec 2020 18:56:44 GMT"
}
] | 1,607,299,200,000 | [
[
"Lee",
"Ritchie",
""
],
[
"Mengshoel",
"Ole J.",
""
],
[
"Saksena",
"Anshu",
""
],
[
"Gardner",
"Ryan",
""
],
[
"Genin",
"Daniel",
""
],
[
"Silbermann",
"Joshua",
""
],
[
"Owen",
"Michael",
""
],
[
"Kochenderfer",
"Mykel J.",
""
]
] |
1811.02216 | Dmitry Maximov | Dmitry Maximov | An Optimal Itinerary Generation in a Configuration Space of Large
Intellectual Agent Groups with Linear Logic | "Management of Large-Scale Systems Development" (MLSD-2018) a full
version of the conference paper | Advances in Systems Science and Applications. 2019. Vol. 19, No 4.
P. 79-86 https://ijassa.ipu.ru/index.php/ijassa/article/view/829/513 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A group of intelligent agents which fulfill a set of tasks in parallel is
represented first by the tensor multiplication of corresponding processes in a
linear logic game category. An optimal itinerary in the configuration space of
the group states is defined as a play with maximal total reward in the
category. New moments also are: the reward is represented as a degree of
certainty (visibility) of an agent goal, and the system goals are chosen by the
greatest value corresponding to these processes in the system goal lattice.
| [
{
"version": "v1",
"created": "Tue, 6 Nov 2018 08:24:58 GMT"
}
] | 1,638,403,200,000 | [
[
"Maximov",
"Dmitry",
""
]
] |
1811.02366 | Antonio Lieto | Antonio Lieto and Gian Luca Pozzato | A Description Logic Framework for Commonsense Conceptual Combination
Integrating Typicality, Probabilities and Cognitive Heuristics | 39 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a nonmonotonic Description Logic of typicality able to account for
the phenomenon of concept combination of prototypical concepts. The proposed
logic relies on the logic of typicality ALC TR, whose semantics is based on the
notion of rational closure, as well as on the distributed semantics of
probabilistic Description Logics, and is equipped with a cognitive heuristic
used by humans for concept composition. We first extend the logic of typicality
ALC TR by typicality inclusions whose intuitive meaning is that "there is
probability p about the fact that typical Cs are Ds". As in the distributed
semantics, we define different scenarios containing only some typicality
inclusions, each one having a suitable probability. We then focus on those
scenarios whose probabilities belong to a given and fixed range, and we exploit
such scenarios in order to ascribe typical properties to a concept C obtained
as the combination of two prototypical concepts. We also show that reasoning in
the proposed Description Logic is EXPTIME-complete as for the underlying ALC.
| [
{
"version": "v1",
"created": "Tue, 6 Nov 2018 14:28:43 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Nov 2018 14:45:20 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Feb 2019 22:05:36 GMT"
},
{
"version": "v4",
"created": "Mon, 12 Aug 2019 08:31:42 GMT"
}
] | 1,565,654,400,000 | [
[
"Lieto",
"Antonio",
""
],
[
"Pozzato",
"Gian Luca",
""
]
] |
1811.02546 | Paul Yaworsky | Paul Yaworsky | A Model for General Intelligence | 7 pages; distribution statement added | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The overarching problem in artificial intelligence (AI) is that we do not
understand the intelligence process well enough to enable the development of
adequate computational models. Much work has been done in AI over the years at
lower levels, but a big part of what has been missing involves the high level,
abstract, general nature of intelligence. We address this gap by developing a
model for general intelligence. To accomplish this, we focus on three basic
aspects of intelligence. First, we must realize the general order and nature of
intelligence at a high level. Second, we must come to know what these
realizations mean with respect to the overall intelligence process. Third, we
must describe these realizations as clearly as possible. We propose a
hierarchical model to help capture and exploit the order within intelligence.
The underlying order involves patterns of signals that become organized, stored
and activated in space and time. These patterns can be described using a
simple, general hierarchy, with physical signals at the lowest level,
information in the middle, and abstract signal representations at the top. This
high level perspective provides a big picture that literally helps us see the
intelligence process, thereby enabling fundamental realizations, a better
understanding and clear descriptions of the intelligence process. The resulting
model can be used to support all kinds of information processing across
multiple levels of abstraction. As computer technology improves, and as
cooperation increases between humans and computers, people will become more
efficient and more productive in performing their information processing tasks.
| [
{
"version": "v1",
"created": "Tue, 6 Nov 2018 18:37:04 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Nov 2018 20:21:25 GMT"
}
] | 1,542,326,400,000 | [
[
"Yaworsky",
"Paul",
""
]
] |
1811.02872 | Mikul\'a\v{s} Zelinka | Mikul\'a\v{s} Zelinka | Baselines for Reinforcement Learning in Text Games | 8 pages, published at ICTAI 2018 | null | 10.1109/ICTAI2018.2018.00125 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to learn optimal control policies in systems where action space
is defined by sentences in natural language would allow many interesting
real-world applications such as automatic optimisation of dialogue systems.
Text-based games with multiple endings and rewards are a promising platform for
this task, since their feedback allows us to employ reinforcement learning
techniques to jointly learn text representations and control policies. We argue
that the key property of AI agents, especially in the text-games context, is
their ability to generalise to previously unseen games. We present a
minimalistic text-game playing agent, testing its generalisation and transfer
learning performance and showing its ability to play multiple games at once. We
also present pyfiction, an open-source library for universal access to
different text games that could, together with our agent that implements its
interface, serve as a baseline for future research.
| [
{
"version": "v1",
"created": "Wed, 7 Nov 2018 13:30:40 GMT"
},
{
"version": "v2",
"created": "Mon, 12 Nov 2018 19:23:15 GMT"
}
] | 1,542,153,600,000 | [
[
"Zelinka",
"Mikuláš",
""
]
] |
1811.03035 | Can Eren Sezener | Can Eren Sezener | Computing the Value of Computation for Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An intelligent agent performs actions in order to achieve its goals. Such
actions can either be externally directed, such as opening a door, or
internally directed, such as writing data to a memory location or strengthening
a synaptic connection. Some internal actions, to which we refer as
computations, potentially help the agent choose better actions. Considering
that (external) actions and computations might draw upon the same resources,
such as time and energy, deciding when to act or compute, as well as what to
compute, are detrimental to the performance of an agent.
In an environment that provides rewards depending on an agent's behavior, an
action's value is typically defined as the sum of expected long-term rewards
succeeding the action (itself a complex quantity that depends on what the agent
goes on to do after the action in question). However, defining the value of a
computation is not as straightforward, as computations are only valuable in a
higher order way, through the alteration of actions.
This thesis offers a principled way of computing the value of a computation
in a planning setting formalized as a Markov decision process. We present two
different definitions of computation values: static and dynamic. They address
two extreme cases of the computation budget: affording calculation of zero or
infinitely many steps in the future. We show that these values have desirable
properties, such as temporal consistency and asymptotic convergence.
Furthermore, we propose methods for efficiently computing and approximating
the static and dynamic computation values. We describe a sense in which the
policies that greedily maximize these values can be optimal. We utilize these
principles to construct Monte Carlo tree search algorithms that outperform most
of the state-of-the-art in terms of finding higher quality actions given the
same simulation resources.
| [
{
"version": "v1",
"created": "Wed, 7 Nov 2018 17:39:24 GMT"
}
] | 1,541,635,200,000 | [
[
"Sezener",
"Can Eren",
""
]
] |
1811.03119 | Ryan Gardner | Jared Markowitz, Ryan W. Gardner, and Ashley J. Llorens | On the Complexity of Reconnaissance Blind Chess | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper provides a complexity analysis for the game of reconnaissance
blind chess (RBC), a recently-introduced variant of chess where each player
does not know the positions of the opponent's pieces a priori but may reveal a
subset of them through chosen, private sensing actions. In contrast to many
commonly studied imperfect information games like poker, an RBC player does not
know what the opponent knows or has chosen to learn, exponentially expanding
the size of the game's information sets (i.e., the number of possible game
states that are consistent with what a player has observed). Effective RBC
sensing and moving strategies must account for the uncertainty of both players,
an essential element of many real-world decision-making problems. Here we
evaluate RBC from a game theoretic perspective, tracking the proliferation of
information sets from the perspective of selected canonical bot players in
tournament play. We show that, even for effective sensing strategies, the game
sizes of RBC compare to those of Go while the average size of a player's
information set throughout an RBC game is much greater than that of a player in
Heads-up Limit Hold 'Em. We compare these measures of complexity among
different playing algorithms and provide cursory assessments of the various
sensing and moving strategies.
| [
{
"version": "v1",
"created": "Wed, 7 Nov 2018 19:20:21 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Mar 2019 16:09:19 GMT"
}
] | 1,551,657,600,000 | [
[
"Markowitz",
"Jared",
""
],
[
"Gardner",
"Ryan W.",
""
],
[
"Llorens",
"Ashley J.",
""
]
] |
1811.03163 | Tim Miller | Tim Miller | Contrastive Explanation: A Structural-Model Approach | null | The Knowledge Engineering Review 36 (2021) e14 | 10.1017/S0269888921000102 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a model of contrastive explanation using structural
casual models. The topic of causal explanation in artificial intelligence has
gathered interest in recent years as researchers and practitioners aim to
increase trust and understanding of intelligent decision-making. While
different sub-fields of artificial intelligence have looked into this problem
with a sub-field-specific view, there are few models that aim to capture
explanation more generally. One general model is based on structural causal
models. It defines an explanation as a fact that, if found to be true, would
constitute an actual cause of a specific event. However, research in philosophy
and social sciences shows that explanations are contrastive: that is, when
people ask for an explanation of an event -- the fact -- they (sometimes
implicitly) are asking for an explanation relative to some contrast case; that
is, "Why P rather than Q?". In this paper, we extend the structural causal
model approach to define two complementary notions of contrastive explanation,
and demonstrate them on two classical problems in artificial intelligence:
classification and planning. We believe that this model can help researchers in
subfields of artificial intelligence to better understand contrastive
explanation.
| [
{
"version": "v1",
"created": "Wed, 7 Nov 2018 22:05:45 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Dec 2020 03:06:06 GMT"
}
] | 1,687,392,000,000 | [
[
"Miller",
"Tim",
""
]
] |
1811.03355 | Sanjay Modgil | Davide Grossi and Sanjay Modgil | On the Graded Acceptability of Arguments in Abstract and Instantiated
Argumentation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper develops a formal theory of the degree of justification of
arguments, which relies solely on the structure of an argumentation framework,
and which can be successfully interfaced with approaches to instantiated
argumentation. The theory is developed in three steps. First, the paper
introduces a graded generalization of the two key notions underpinning Dung's
semantics: self-defense and conflict-freeness. This leads to a natural
generalization of Dung's semantics, whereby standard extensions are weakened or
strengthened depending on the level of self-defense and conflict-freeness they
meet. The paper investigates the fixpoint theory of these semantics,
establishing existence results for them. Second, the paper shows how graded
semantics readily provide an approach to argument rankings, offering a novel
contribution to the recently growing research programme on ranking-based
semantics. Third, this novel approach to argument ranking is applied and
studied in the context of instantiated argumentation frameworks, and in so
doing is shown to account for a simple form of accrual of arguments within the
Dung paradigm. Finally, the theory is compared in detail with existing
approaches.
| [
{
"version": "v1",
"created": "Thu, 8 Nov 2018 11:08:01 GMT"
}
] | 1,541,721,600,000 | [
[
"Grossi",
"Davide",
""
],
[
"Modgil",
"Sanjay",
""
]
] |
1811.03376 | Xi Chen | Xi Chen, Ali Ghadirzadeh, M{\aa}rten Bj\"orkman and Patric Jensfelt | Meta-Learning for Multi-objective Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-objective reinforcement learning (MORL) is the generalization of
standard reinforcement learning (RL) approaches to solve sequential decision
making problems that consist of several, possibly conflicting, objectives.
Generally, in such formulations, there is no single optimal policy which
optimizes all the objectives simultaneously, and instead, a number of policies
has to be found each optimizing a preference of the objectives. In other words,
the MORL is framed as a meta-learning problem, with the task distribution given
by a distribution over the preferences. We demonstrate that such a formulation
results in a better approximation of the Pareto optimal solutions in terms of
both the optimality and the computational efficiency. We evaluated our method
on obtaining Pareto optimal policies using a number of continuous control
problems with high degrees of freedom.
| [
{
"version": "v1",
"created": "Thu, 8 Nov 2018 12:26:42 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Oct 2019 10:35:03 GMT"
}
] | 1,570,492,800,000 | [
[
"Chen",
"Xi",
""
],
[
"Ghadirzadeh",
"Ali",
""
],
[
"Björkman",
"Mårten",
""
],
[
"Jensfelt",
"Patric",
""
]
] |
1811.03496 | Helge Spieker | Helge Spieker, Arnaud Gotlieb, Morten Mossige | Multi-Cycle Assignment Problems with Rotational Diversity | Extended journal version | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-cycle assignment problems address scenarios where a series of general
assignment problems has to be solved sequentially. Subsequent cycles can differ
from previous ones due to changing availability or creation of tasks and
agents, which makes an upfront static schedule infeasible and introduces
uncertainty in the task-agent assignment process. We consider the setting
where, besides profit maximization, it is also desired to maintain diverse
assignments for tasks and agents, such that all tasks have been assigned to all
agents over subsequent cycles. This problem of multi-cycle assignment with
rotational diversity is approached in two sub-problems: The outer problem which
augments the original profit maximization objective with additional information
about the state of rotational diversity while the inner problem solves the
adjusted general assignment problem in a single execution of the model. We
discuss strategies to augment the profit values and evaluate them
experimentally. The method's efficacy is shown in three case studies:
multi-cycle variants of the multiple knapsack and the multiple subset sum
problems, and a real-world case study on the test case selection and assignment
problem from the software engineering domain.
| [
{
"version": "v1",
"created": "Thu, 8 Nov 2018 15:39:35 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Dec 2019 16:11:28 GMT"
}
] | 1,576,800,000,000 | [
[
"Spieker",
"Helge",
""
],
[
"Gotlieb",
"Arnaud",
""
],
[
"Mossige",
"Morten",
""
]
] |
1811.03532 | Duncan McElfresh | Duncan C McElfresh, Hoda Bidkhori, John P Dickerson | Scalable Robust Kidney Exchange | Presented at AAAI19 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In barter exchanges, participants directly trade their endowed goods in a
constrained economic setting without money. Transactions in barter exchanges
are often facilitated via a central clearinghouse that must match participants
even in the face of uncertainty---over participants, existence and quality of
potential trades, and so on. Leveraging robust combinatorial optimization
techniques, we address uncertainty in kidney exchange, a real-world barter
market where patients swap (in)compatible paired donors. We provide two
scalable robust methods to handle two distinct types of uncertainty in kidney
exchange---over the quality and the existence of a potential match. The latter
case directly addresses a weakness in all stochastic-optimization-based methods
to the kidney exchange clearing problem, which all necessarily require explicit
estimates of the probability of a transaction existing---a still-unsolved
problem in this nascent market. We also propose a novel, scalable kidney
exchange formulation that eliminates the need for an exponential-time
constraint generation process in competing formulations, maintains provable
optimality, and serves as a subsolver for our robust approach. For each type of
uncertainty we demonstrate the benefits of robustness on real data from a
large, fielded kidney exchange in the United States. We conclude by drawing
parallels between robustness and notions of fairness in the kidney exchange
setting.
| [
{
"version": "v1",
"created": "Thu, 8 Nov 2018 16:25:27 GMT"
}
] | 1,541,721,600,000 | [
[
"McElfresh",
"Duncan C",
""
],
[
"Bidkhori",
"Hoda",
""
],
[
"Dickerson",
"John P",
""
]
] |
1811.03555 | Haoran Tang | Dennis Lee, Haoran Tang, Jeffrey O Zhang, Huazhe Xu, Trevor Darrell,
Pieter Abbeel | Modular Architecture for StarCraft II with Deep Reinforcement Learning | Accepted to The 14th AAAI Conference on Artificial Intelligence and
Interactive Digital Entertainment (AIIDE'18) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel modular architecture for StarCraft II AI. The architecture
splits responsibilities between multiple modules that each control one aspect
of the game, such as build-order selection or tactics. A centralized scheduler
reviews macros suggested by all modules and decides their order of execution.
An updater keeps track of environment changes and instantiates macros into
series of executable actions. Modules in this framework can be optimized
independently or jointly via human design, planning, or reinforcement learning.
We apply deep reinforcement learning techniques to training two out of six
modules of a modular agent with self-play, achieving 94% or 87% win rates
against the "Harder" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches,
with or without fog-of-war.
| [
{
"version": "v1",
"created": "Thu, 8 Nov 2018 17:13:50 GMT"
}
] | 1,541,721,600,000 | [
[
"Lee",
"Dennis",
""
],
[
"Tang",
"Haoran",
""
],
[
"Zhang",
"Jeffrey O",
""
],
[
"Xu",
"Huazhe",
""
],
[
"Darrell",
"Trevor",
""
],
[
"Abbeel",
"Pieter",
""
]
] |
1811.03653 | Jason Pittman | Jason M. Pittman, Jesus P. Espinoza, Courtney Crosby | Stovepiping and Malicious Software: A Critical Review of AGI Containment | Updated author name | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Awareness of the possible impacts associated with artificial intelligence has
risen in proportion to progress in the field. While there are tremendous
benefits to society, many argue that there are just as many, if not more,
concerns related to advanced forms of artificial intelligence. Accordingly,
research into methods to develop artificial intelligence safely is increasingly
important. In this paper, we provide an overview of one such safety paradigm:
containment with a critical lens aimed toward generative adversarial networks
and potentially malicious artificial intelligence. Additionally, we illuminate
the potential for a developmental blindspot in the stovepiping of containment
mechanisms.
| [
{
"version": "v1",
"created": "Thu, 8 Nov 2018 19:19:53 GMT"
},
{
"version": "v2",
"created": "Sun, 1 Aug 2021 09:46:27 GMT"
}
] | 1,627,948,800,000 | [
[
"Pittman",
"Jason M.",
""
],
[
"Espinoza",
"Jesus P.",
""
],
[
"Crosby",
"Courtney",
""
]
] |
1811.03742 | Xingyu Li | Xingyu Li, Bogdan I. Epureanu | Analysis of Fleet Modularity in an Artificial Intelligence-Based
Attacker-Defender Game | 30 pages, 15 figures, manuscript to be submitted to European Journal
of Operational Research | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Because combat environments change over time and technology upgrades are
widespread for ground vehicles, a large number of vehicles and equipment become
quickly obsolete. A possible solution for the U.S. Army is to develop fleets of
modular military vehicles, which are built by interchangeable substantial
components also known as modules. One of the typical characteristics of module
is their ease of assembly and disassembly through simple means such as
plug-in/pull-out actions, which allows for real-time fleet reconfiguration to
meet dynamic demands. Moreover, military demands are time-varying and highly
stochastic because commanders keep reacting to enemy's actions. To capture
these characteristics, we formulated an intelligent agent-based model to
imitate decision making process during fleet operation, which combines
real-time optimization with artificial intelligence. The agents are capable of
inferring enemy's future move based on historical data and optimize
dispatch/operation decisions accordingly. We implement our model to simulate an
attacker-defender game between two adversarial and intelligent players,
representing the commanders from modularized fleet and conventional fleet
respectively. Given the same level of combat resources and intelligence, we
highlight the tactical advantages of fleet modularity in terms of win rate,
unpredictability and suffered damage.
| [
{
"version": "v1",
"created": "Fri, 9 Nov 2018 02:24:19 GMT"
},
{
"version": "v2",
"created": "Thu, 31 Jan 2019 20:26:35 GMT"
}
] | 1,549,238,400,000 | [
[
"Li",
"Xingyu",
""
],
[
"Epureanu",
"Bogdan I.",
""
]
] |
1811.03822 | Bin Liu | Bin Liu | A Very Brief and Critical Discussion on AutoML | 5 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This contribution presents a very brief and critical discussion on automated
machine learning (AutoML), which is categorized here into two classes, referred
to as narrow AutoML and generalized AutoML, respectively. The conclusions
yielded from this discussion can be summarized as follows: (1) most existent
research on AutoML belongs to the class of narrow AutoML; (2) advances in
narrow AutoML are mainly motivated by commercial needs, while any possible
benefit obtained is definitely at a cost of increase in computing burdens;
(3)the concept of generalized AutoML has a strong tie in spirit with artificial
general intelligence (AGI), also called "strong AI", for which obstacles abound
for obtaining pivotal progresses.
| [
{
"version": "v1",
"created": "Fri, 9 Nov 2018 09:07:52 GMT"
}
] | 1,541,980,800,000 | [
[
"Liu",
"Bin",
""
]
] |
1811.03868 | Eduardo C\'esar Garrido Merch\'an | Eduardo C. Garrido-Merch\'an and Alejandro Albarca-Molina | Suggesting Cooking Recipes Through Simulation and Bayesian Optimization | null | null | 10.1007/978-3-030-03493-1_30 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cooking typically involves a plethora of decisions about ingredients and
tools that need to be chosen in order to write a good cooking recipe. Cooking
can be modelled in an optimization framework, as it involves a search space of
ingredients, kitchen tools, cooking times or temperatures. If we model as an
objective function the quality of the recipe, several problems arise. No
analytical expression can model all the recipes, so no gradients are available.
The objective function is subjective, in other words, it contains noise.
Moreover, evaluations are expensive both in time and human resources. Bayesian
Optimization (BO) emerges as an ideal methodology to tackle problems with these
characteristics. In this paper, we propose a methodology to suggest recipe
recommendations based on a Machine Learning (ML) model that fits real and
simulated data and BO. We provide empirical evidence with two experiments that
support the adequacy of the methodology.
| [
{
"version": "v1",
"created": "Fri, 9 Nov 2018 11:48:44 GMT"
}
] | 1,580,774,400,000 | [
[
"Garrido-Merchán",
"Eduardo C.",
""
],
[
"Albarca-Molina",
"Alejandro",
""
]
] |
1811.03906 | Helge Spieker | Arnaud Gotlieb, Dusica Marijan, Helge Spieker | ITE: A Lightweight Implementation of Stratified Reasoning for
Constructive Logical Operators | Extended journal version | International Journal on Artificial Intelligence Tools, Vol. 29,
No. 03n04, 2060006 (2020) | 10.1142/S0218213020600064 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Constraint Programming (CP) is a powerful declarative programming paradigm
where inference and search are interleaved to find feasible and optimal
solutions to various type of constraint systems. However, handling logical
connectors with constructive information in CP is notoriously difficult. This
paper presents If Then Else (ITE), a lightweight implementation of stratified
constructive reasoning for logical connectives. Stratification is introduced to
cope with the risk of combinatorial explosion of constructing information from
nested and combined logical operators. ITE is an open-source library built on
top of SICStus Prolog clp(fd), which proposes various operators, including
constructive disjunction and negation, constructive implication and
conditional. These operators can be used to express global constraints and to
benefit from constructive reasoning for more domain pruning during constraint
filtering. Even though ITE is not competitive with specialized filtering
algorithms available in some global constraints implementations, its
expressiveness allows users to easily define well-tuned constraints with
powerful deduction capabilities. Our extended experimental results show that
ITE is more efficient than available generic approaches that handle logical
constraint systems over finite domains.
| [
{
"version": "v1",
"created": "Fri, 9 Nov 2018 14:07:47 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Dec 2019 14:26:14 GMT"
},
{
"version": "v3",
"created": "Mon, 22 Jun 2020 06:23:43 GMT"
}
] | 1,592,870,400,000 | [
[
"Gotlieb",
"Arnaud",
""
],
[
"Marijan",
"Dusica",
""
],
[
"Spieker",
"Helge",
""
]
] |
1811.04173 | Son-Il Kwak | Chung-Jin Kwak, Son-Il Kwak, Dae-Song Kang, Song-Il Choe, Jin-Ung Kim,
Hyok-Gi Chea | New Movement and Transformation Principle of Fuzzy Reasoning and Its
Application to Fuzzy Neural Network | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a new fuzzy reasoning principle, so called Movement
and Transformation Principle(MTP). This Principle is to obtain a new fuzzy
reasoning result by Movement and Transformation the consequent fuzzy set in
response to the Movement, Transformation, and Movement-Transformation
operations between the antecedent fuzzy set and fuzzificated observation
information. And then we presented fuzzy modus ponens and fuzzy modus tollens
based on MTP. We compare proposed method with Mamdani fuzzy system, Sugeno
fuzzy system, Wang distance type fuzzy reasoning method and Hellendoorn
functional type method. And then we applied to the learning experiments of the
fuzzy neural network based on MTP and compared it with the Sugeno method.
Through prediction experiments of fuzzy neural network on the precipitation
data and security situation data, learning accuracy and time performance are
clearly improved. Consequently we show that our method based on MTP is
computationally simple and does not involve nonlinear operations, so it is easy
to handle mathematically.
| [
{
"version": "v1",
"created": "Sat, 10 Nov 2018 01:46:28 GMT"
}
] | 1,542,067,200,000 | [
[
"Kwak",
"Chung-Jin",
""
],
[
"Kwak",
"Son-Il",
""
],
[
"Kang",
"Dae-Song",
""
],
[
"Choe",
"Song-Il",
""
],
[
"Kim",
"Jin-Ung",
""
],
[
"Chea",
"Hyok-Gi",
""
]
] |
1811.04458 | Mark Levin | Mark Sh. Levin | Time-interval balancing in multi-processor scheduling of composite
modular jobs (preliminary description) | 37 pages, 16 figures, 56 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The article describes a special time-interval balancing in multi-processor
scheduling of composite modular jobs. This scheduling problem is close to
just-in-time planning approach. First, brief literature surveys are presented
on just-in-time scheduling and due-data/due-window scheduling problems.
Further, the problem and its formulation are proposed for the time-interval
balanced scheduling of composite modular jobs. The illustrative real world
planning example for modular home-building is described. Here, the main
objective function consists in a balance between production of the typical
building modules (details) and the assembly processes of the building(s) (by
several teams). The assembly plan has to be modified to satisfy the balance
requirements. The solving framework is based on the following: (i) clustering
of initial set of modular detail types to obtain about ten basic detail types
that correspond to main manufacturing conveyors; (ii) designing a preliminary
plan of assembly for buildings; (iii) detection of unbalanced time periods,
(iv) modification of the planning solution to improve the schedule balance. The
framework implements a metaheuristic based on local optimization approach. Two
other applications (supply chain management, information transmission systems)
are briefly described.
| [
{
"version": "v1",
"created": "Sun, 11 Nov 2018 19:13:20 GMT"
}
] | 1,542,067,200,000 | [
[
"Levin",
"Mark Sh.",
""
]
] |
1811.04651 | Pablo Samuel Castro | Pablo Samuel Castro, Maria Attarian | Combining Learned Lyrical Structures and Vocabulary for Improved Lyric
Generation | Extended abstract (2 pages) for the NIPS 2018 Second Workshop on
Machine Learning for Creativity and Design | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of language models for generating lyrics and poetry has received an
increased interest in the last few years. They pose a unique challenge relative
to standard natural language problems, as their ultimate purpose is reative,
notions of accuracy and reproducibility are secondary to notions of lyricism,
structure, and diversity. In this creative setting, traditional quantitative
measures for natural language problems, such as BLEU scores, prove inadequate:
a high-scoring model may either fail to produce output respecting the desired
structure (e.g. song verses), be a terribly boring creative companion, or both.
In this work we propose a mechanism for combining two separately trained
language models into a framework that is able to produce output respecting the
desired song structure, while providing a richness and diversity of vocabulary
that renders it more creatively appealing.
| [
{
"version": "v1",
"created": "Mon, 12 Nov 2018 10:48:43 GMT"
}
] | 1,542,067,200,000 | [
[
"Castro",
"Pablo Samuel",
""
],
[
"Attarian",
"Maria",
""
]
] |
1811.04747 | Hongyi Huang | Hongyi Huang | Reimplementation and Reinterpretation of the Copycat Project | 14 pages, 4 figures, 2 tables. Work in progress preprint. Research
project spans 2 years both within my time in Northeastern University
internships and class time in Viewpoint School, thus dual affiliation is
accepted by both parties. More experimental comparison results will be added
when conclusive experiments are completed | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the reinterpreted and reimplemented Copycat project, an
architecture solving letter analogy domain problems. To support a flexible
implementation change and rigor testing process, we propose a implementation
method in DrRacket by using functional abstraction, naming system,
initialization, and structural reference. Finally, benefits and limitations are
analyzed for cognitive architectures along the lines of Copycat.
| [
{
"version": "v1",
"created": "Fri, 26 Oct 2018 00:55:45 GMT"
}
] | 1,542,067,200,000 | [
[
"Huang",
"Hongyi",
""
]
] |
1811.04787 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw K{\l}opotek | Mathematical Theory of Evidence Versus Evidence | arXiv admin note: substantial text overlap with arXiv:1704.04000,
arXiv:1707.03881 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is concerned with the apparent greatest weakness of the
Mathematical Theory of Evidence (MTE) of Shafer \cite{Shafer:76}, which has
been strongly criticized by Wasserman \cite{Wasserman:92ijar}.
Weaknesses of Shafer's proposal \cite{Shafer:90b} of probabilistic
interpretation of MTE belief functions is demonstrated. Thereafter a new
probabilistic interpretation of MTE conforming both to definition of belief
function and to Dempster's rule of combination of independent evidence. It is
shown that shaferian conditioning of belief functions on observations
\cite{Shafer:90b} may be treated as selection combined with modification of
data, that is data is not viewed as it is but it is casted into one's beliefs
in what it should be like.
| [
{
"version": "v1",
"created": "Fri, 9 Nov 2018 12:01:26 GMT"
}
] | 1,542,067,200,000 | [
[
"Kłopotek",
"Mieczysław",
""
]
] |
1811.04790 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw K{\l}opotek | Reasoning From Data in the Mathematical Theory of Evidence | presented as poster M.A. K{\l}opotek: Reasoning from Data in the
Mathematical Theory of Evidence. [in:] Proc. Eighth International Symposium
On Methodologies For Intelligent Systems (ISMIS'94), Charlotte, North
Carolina, USA, October 16-19, 1994. arXiv admin note: text overlap with
arXiv:1707.03881 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mathematical Theory of Evidence (MTE) is known as a foundation for reasoning
when knowledge is expressed at various levels of detail. Though much research
effort has been committed to this theory since its foundation, many questions
remain open. One of the most important open questions seems to be the
relationship between frequencies and the Mathematical Theory of Evidence. The
theory is blamed to leave frequencies outside (or aside of) its framework. The
seriousness of this accusation is obvious: no experiment may be run to compare
the performance of MTE-based models of real world processes against real world
data.
In this paper we develop a frequentist model of the MTE bringing to fall the
above argument against MTE. We describe, how to interpret data in terms of MTE
belief functions, how to reason from data about conditional belief functions,
how to generate a random sample out of a MTE model, how to derive MTE model
from data and how to compare results of reasoning in MTE model and reasoning
from data.
It is claimed in this paper that MTE is suitable to model some types of
destructive processes
| [
{
"version": "v1",
"created": "Fri, 9 Nov 2018 11:41:10 GMT"
}
] | 1,542,067,200,000 | [
[
"Kłopotek",
"Mieczysław",
""
]
] |
1811.04854 | Flavio Correa Da Silva | Flavio S Correa da Silva and Frederico P Costa and Antonio F Iemma | On the practice of classification learning for clinical diagnosis and
therapy advice in oncology | Submitted to Artificial Intelligence in Medicine | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence and medicine have a longstanding and proficuous
relationship. In the present work we develop a brief assessment of this
relationship with specific focus on machine learning, in which we highlight
some critical points which may hinder the use of machine learning techniques
for clinical diagnosis and therapy advice in practice. We then suggest a
conceptual framework to build successful systems to aid clinical diagnosis and
therapy advice, grounded on a novel concept we have coined drifting domains. We
focus on oncology to build our arguments, as this area of medicine furnishes
strong evidence for the critical points we take into account here.
| [
{
"version": "v1",
"created": "Mon, 12 Nov 2018 16:59:39 GMT"
}
] | 1,542,067,200,000 | [
[
"da Silva",
"Flavio S Correa",
""
],
[
"Costa",
"Frederico P",
""
],
[
"Iemma",
"Antonio F",
""
]
] |
1811.04896 | Michael Hind | Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit
Dhurandhar, Aleksandra Mojsilovi\'c, Karthikeyan Natesan Ramamurthy, Kush R.
Varshney | TED: Teaching AI to Explain its Decisions | This article leverages some content from arXiv:1805.11648; presented
at ACM/AAAI AIES'19 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence systems are being increasingly deployed due to their
potential to increase the efficiency, scale, consistency, fairness, and
accuracy of decisions. However, as many of these systems are opaque in their
operation, there is a growing demand for such systems to provide explanations
for their decisions. Conventional approaches to this problem attempt to expose
or discover the inner workings of a machine learning model with the hope that
the resulting explanations will be meaningful to the consumer. In contrast,
this paper suggests a new approach to this problem. It introduces a simple,
practical framework, called Teaching Explanations for Decisions (TED), that
provides meaningful explanations that match the mental model of the consumer.
We illustrate the generality and effectiveness of this approach with two
different examples, resulting in highly accurate explanations with no loss of
prediction accuracy for these two examples.
| [
{
"version": "v1",
"created": "Mon, 12 Nov 2018 18:29:12 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Jun 2019 21:00:14 GMT"
}
] | 1,560,816,000,000 | [
[
"Hind",
"Michael",
""
],
[
"Wei",
"Dennis",
""
],
[
"Campbell",
"Murray",
""
],
[
"Codella",
"Noel C. F.",
""
],
[
"Dhurandhar",
"Amit",
""
],
[
"Mojsilović",
"Aleksandra",
""
],
[
"Ramamurthy",
"Karthikeyan Natesan",
""
],
[
"Varshney",
"Kush R.",
""
]
] |
1811.05245 | Luca Costabello | Rory Mc Grath, Luca Costabello, Chan Le Van, Paul Sweeney, Farbod
Kamiab, Zhao Shen, Freddy Lecue | Interpretable Credit Application Predictions With Counterfactual
Explanations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We predict credit applications with off-the-shelf, interchangeable black-box
classifiers and we explain single predictions with counterfactual explanations.
Counterfactual explanations expose the minimal changes required on the input
data to obtain a different result e.g., approved vs rejected application.
Despite their effectiveness, counterfactuals are mainly designed for changing
an undesired outcome of a prediction i.e. loan rejected. Counterfactuals,
however, can be difficult to interpret, especially when a high number of
features are involved in the explanation. Our contribution is two-fold: i) we
propose positive counterfactuals, i.e. we adapt counterfactual explanations to
also explain accepted loan applications, and ii) we propose two weighting
strategies to generate more interpretable counterfactuals. Experiments on the
HELOC loan applications dataset show that our contribution outperforms the
baseline counterfactual generation strategy, by leading to smaller and hence
more interpretable counterfactuals.
| [
{
"version": "v1",
"created": "Tue, 13 Nov 2018 12:12:57 GMT"
},
{
"version": "v2",
"created": "Fri, 16 Nov 2018 10:04:21 GMT"
}
] | 1,542,585,600,000 | [
[
"Grath",
"Rory Mc",
""
],
[
"Costabello",
"Luca",
""
],
[
"Van",
"Chan Le",
""
],
[
"Sweeney",
"Paul",
""
],
[
"Kamiab",
"Farbod",
""
],
[
"Shen",
"Zhao",
""
],
[
"Lecue",
"Freddy",
""
]
] |
1811.05297 | Mateo Sanchez | Esteban Quintero, Mateo Sanchez, Nicolas Roldan, and Mauricio Toro | Genetic algorithm for optimal distribution in cities | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem to deal with in this project is the problem of routing electric
vehicles, which consists of finding the best routes for this type of vehicle,
so that they reach their destination, without running out of power and
optimizing to the maximum transportation costs. The importance of this problem
is mainly in the sector of shipments in the recent future, when obsolete energy
sources are replaced with renewable sources, where each vehicle contains a
number of packages that must be delivered at specific points in the city , but,
being electric, they do not have an optimal battery life, so having the ideal
routes traced is a vital aspect for the proper functioning of these. Now days
you can see applications of this problem in the cleaning sector, specifically
with the trucks responsible for collecting garbage, which aims to travel the
entire city in the most efficient way, without letting excessive garbage
accumulate.
| [
{
"version": "v1",
"created": "Tue, 13 Nov 2018 14:02:29 GMT"
}
] | 1,542,153,600,000 | [
[
"Quintero",
"Esteban",
""
],
[
"Sanchez",
"Mateo",
""
],
[
"Roldan",
"Nicolas",
""
],
[
"Toro",
"Mauricio",
""
]
] |
1811.05420 | Warren Del-Pinto | Warren Del-Pinto, Renate A. Schmidt | ABox Abduction via Forgetting in ALC (Long Version) | Long version of a paper accepted for publication in the proceedings
of AAAI 2019 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Abductive reasoning generates explanatory hypotheses for new observations
using prior knowledge. This paper investigates the use of forgetting, also
known as uniform interpolation, to perform ABox abduction in description logic
(ALC) ontologies. Non-abducibles are specified by a forgetting signature which
can contain concept, but not role, symbols. The resulting hypotheses are
semantically minimal and each consist of a set of disjuncts. These disjuncts
are each independent explanations, and are not redundant with respect to the
background ontology or the other disjuncts, representing a form of hypothesis
space. The observations and hypotheses handled by the method can contain both
atomic or complex ALC concepts, excluding role assertions, and are not
restricted to Horn clauses. Two approaches to redundancy elimination are
explored for practical use: full and approximate. Using a prototype
implementation, experiments were performed over a corpus of real world
ontologies to investigate the practicality of both approaches across several
settings.
| [
{
"version": "v1",
"created": "Tue, 13 Nov 2018 17:18:48 GMT"
}
] | 1,542,153,600,000 | [
[
"Del-Pinto",
"Warren",
""
],
[
"Schmidt",
"Renate A.",
""
]
] |
1811.05437 | Kristijonas \v{C}yras | Kristijonas \v{C}yras, Dimitrios Letsios, Ruth Misener, Francesca Toni | Argumentation for Explainable Scheduling (Full Paper with Proofs) | Full version (including proofs) of the paper published at AAAI-19 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mathematical optimization offers highly-effective tools for finding solutions
for problems with well-defined goals, notably scheduling. However, optimization
solvers are often unexplainable black boxes whose solutions are inaccessible to
users and which users cannot interact with. We define a novel paradigm using
argumentation to empower the interaction between optimization solvers and
users, supported by tractable explanations which certify or refute solutions. A
solution can be from a solver or of interest to a user (in the context of
'what-if' scenarios). Specifically, we define argumentative and natural
language explanations for why a schedule is (not) feasible, (not) efficient or
(not) satisfying fixed user decisions, based on models of the fundamental
makespan scheduling problem in terms of abstract argumentation frameworks
(AFs). We define three types of AFs, whose stable extensions are in one-to-one
correspondence with schedules that are feasible, efficient and satisfying fixed
decisions, respectively. We extract the argumentative explanations from these
AFs and the natural language explanations from the argumentative ones.
| [
{
"version": "v1",
"created": "Tue, 13 Nov 2018 18:04:26 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Feb 2019 11:45:03 GMT"
}
] | 1,550,707,200,000 | [
[
"Čyras",
"Kristijonas",
""
],
[
"Letsios",
"Dimitrios",
""
],
[
"Misener",
"Ruth",
""
],
[
"Toni",
"Francesca",
""
]
] |
1811.05612 | Sammie Katt | Sammie Katt, Frans Oliehoek, Christopher Amato | Bayesian Reinforcement Learning in Factored POMDPs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian approaches provide a principled solution to the
exploration-exploitation trade-off in Reinforcement Learning. Typical
approaches, however, either assume a fully observable environment or scale
poorly. This work introduces the Factored Bayes-Adaptive POMDP model, a
framework that is able to exploit the underlying structure while learning the
dynamics in partially observable systems. We also present a belief tracking
method to approximate the joint posterior over state and model variables, and
an adaptation of the Monte-Carlo Tree Search solution method, which together
are capable of solving the underlying problem near-optimally. Our method is
able to learn efficiently given a known factorization or also learn the
factorization and the model parameters at the same time. We demonstrate that
this approach is able to outperform current methods and tackle problems that
were previously infeasible.
| [
{
"version": "v1",
"created": "Wed, 14 Nov 2018 02:47:05 GMT"
}
] | 1,542,240,000,000 | [
[
"Katt",
"Sammie",
""
],
[
"Oliehoek",
"Frans",
""
],
[
"Amato",
"Christopher",
""
]
] |
1811.05685 | Haifeng Zhang | Haifeng Zhang, Zilong Guo, Han Cai, Chris Wang, Weinan Zhang, Yong Yu,
Wenxin Li, Jun Wang | Layout Design for Intelligent Warehouse by Evolution with Fitness
Approximation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid growth of the express industry, intelligent warehouses that
employ autonomous robots for carrying parcels have been widely used to handle
the vast express volume. For such warehouses, the warehouse layout design plays
a key role in improving the transportation efficiency. However, this work is
still done by human experts, which is expensive and leads to suboptimal
results. In this paper, we aim to automate the warehouse layout designing
process. We propose a two-layer evolutionary algorithm to efficiently explore
the warehouse layout space, where an auxiliary objective fitness approximation
model is introduced to predict the outcome of the designed warehouse layout and
a two-layer population structure is proposed to incorporate the approximation
model into the ordinary evolution framework. Empirical experiments show that
our method can efficiently design effective warehouse layouts that outperform
both heuristic-designed and vanilla evolution-designed warehouse layouts.
| [
{
"version": "v1",
"created": "Wed, 14 Nov 2018 08:37:01 GMT"
}
] | 1,542,240,000,000 | [
[
"Zhang",
"Haifeng",
""
],
[
"Guo",
"Zilong",
""
],
[
"Cai",
"Han",
""
],
[
"Wang",
"Chris",
""
],
[
"Zhang",
"Weinan",
""
],
[
"Yu",
"Yong",
""
],
[
"Li",
"Wenxin",
""
],
[
"Wang",
"Jun",
""
]
] |
1811.05724 | Umberto Straccia | Umberto Straccia | An Introduction to Fuzzy & Annotated Semantic Web Languages | This is an updated version of [291] and acts as accompanying material
to my invited talk and slides at the 2018 Artificial Intelligence
International Conference (A2IC-18) | null | 10.1007/978-3-319-49493-7_6 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the state of the art in representing and reasoning with fuzzy
knowledge in Semantic Web Languages such as triple languages RDF/RDFS,
conceptual languages of the OWL 2 family and rule languages. We further show
how one may generalise them to so-called annotation domains, that cover also
e.g. temporal and provenance extensions.
| [
{
"version": "v1",
"created": "Wed, 14 Nov 2018 11:02:55 GMT"
}
] | 1,542,240,000,000 | [
[
"Straccia",
"Umberto",
""
]
] |
1811.06564 | Juan Leni | Juan Leni, John Levine, John Quigley | Seq2Seq Mimic Games: A Signaling Perspective | NIPS 2018 Workshop on Emergent Communication (accepted) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the emergence of communication in multiagent adversarial settings
inspired by the classic Imitation game. A class of three player games is used
to explore how agents based on sequence to sequence (Seq2Seq) models can learn
to communicate information in adversarial settings. We propose a modeling
approach, an initial set of experiments and use signaling theory to support our
analysis. In addition, we describe how we operationalize the learning process
of actor-critic Seq2Seq based agents in these communicational games.
| [
{
"version": "v1",
"created": "Thu, 15 Nov 2018 19:16:18 GMT"
}
] | 1,542,585,600,000 | [
[
"Leni",
"Juan",
""
],
[
"Levine",
"John",
""
],
[
"Quigley",
"John",
""
]
] |
1811.07231 | Guillem Franc\`es | Blai Bonet, Guillem Franc\`es, Hector Geffner | Learning Features and Abstract Actions for Computing Generalized Plans | Preprint of paper accepted at AAAI'19 conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generalized planning is concerned with the computation of plans that solve
not one but multiple instances of a planning domain. Recently, it has been
shown that generalized plans can be expressed as mappings of feature values
into actions, and that they can often be computed with fully observable
non-deterministic (FOND) planners. The actions in such plans, however, are not
the actions in the instances themselves, which are not necessarily common to
other instances, but abstract actions that are defined on a set of common
features. The formulation assumes that the features and the abstract actions
are given. In this work, we address this limitation by showing how to learn
them automatically. The resulting account of generalized planning combines
learning and planning in a novel way: a learner, based on a Max SAT
formulation, yields the features and abstract actions from sampled state
transitions, and a FOND planner uses this information, suitably transformed, to
produce the general plans. Correctness guarantees are given and experimental
results on several domains are reported.
| [
{
"version": "v1",
"created": "Sat, 17 Nov 2018 22:05:04 GMT"
}
] | 1,542,672,000,000 | [
[
"Bonet",
"Blai",
""
],
[
"Francès",
"Guillem",
""
],
[
"Geffner",
"Hector",
""
]
] |
1811.07603 | Aswani Kumar Cherukuri Dr | Ishwarya M S, Aswani Kumar Ch | Quantum Inspired High Dimensional Conceptual Space as KID Model for
Elderly Assistance | 18th International conference on Intelligent Systems Design and
Applications, (ISDA) to be held from December 6th, 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a cognitive system that acquires knowledge on
elderly daily activities to ensure their wellness in a smart home using a
Knowledge-Information-Data (KID) model. The novel cognitive framework called
high dimensional conceptual space is proposed and used as KID model. This KID
model is built using geometrical framework of conceptual spaces and formal
concept analysis (FCA) to overcome imprecise concept notation of conceptual
space with the help of topology based FCA. By doing so, conceptual space can be
represented using Hilbert space. This high dimensional conceptual space is
quantum inspired in terms of its concept representation. The knowledge learnt
by the KID model recognizes the daily activities of the elderly. Consequently,
the model identifies the scenario on which the wellness of the elderly has to
be ensured.
| [
{
"version": "v1",
"created": "Mon, 19 Nov 2018 10:51:00 GMT"
}
] | 1,542,672,000,000 | [
[
"S",
"Ishwarya M",
""
],
[
"Ch",
"Aswani Kumar",
""
]
] |
1811.07868 | Patrick Klose | Patrick Klose, Rudolf Mester | Simulated Autonomous Driving in a Realistic Driving Environment using
Deep Reinforcement Learning and a Deterministic Finite State Machine | This paper is submitted to Applications of Intelligent Systems
(APPIS) 2019 for review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the field of Autonomous Driving, the system controlling the vehicle can be
seen as an agent acting in a complex environment and thus naturally fits into
the modern framework of Reinforcement Learning. However, learning to drive can
be a challenging task and current results are often restricted to simplified
driving environments. To advance the field, we present a method to adaptively
restrict the action space of the agent according to its current driving
situation and show that it can be used to swiftly learn to drive in a realistic
environment based on the Deep Q-Network algorithm.
| [
{
"version": "v1",
"created": "Mon, 19 Nov 2018 18:45:00 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Nov 2018 07:47:48 GMT"
}
] | 1,543,190,400,000 | [
[
"Klose",
"Patrick",
""
],
[
"Mester",
"Rudolf",
""
]
] |
1811.08186 | Fernando Mart\'inez Plumed | Fernando Mart\'inez-Plumed and Jos\'e Hern\'andez-Orallo | Analysing Results from AI Benchmarks: Key Indicators and How to Obtain
Them | This report is a preliminary version of a related paper with title
"Dual Indicators to Analyse AI Benchmarks: Difficulty, Discrimination,
Ability and Generality", accepted for publication at IEEE Transactions on
Games. Please refer to and cite the journal paper
(https://doi.org/10.1109/TG.2018.2883773) | IEEE Transactions on Games, 2018 | 10.1109/TG.2018.2883773 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Item response theory (IRT) can be applied to the analysis of the evaluation
of results from AI benchmarks. The two-parameter IRT model provides two
indicators (difficulty and discrimination) on the side of the item (or AI
problem) while only one indicator (ability) on the side of the respondent (or
AI agent). In this paper we analyse how to make this set of indicators dual, by
adding a fourth indicator, generality, on the side of the respondent.
Generality is meant to be dual to discrimination, and it is based on
difficulty. Namely, generality is defined as a new metric that evaluates
whether an agent is consistently good at easy problems and bad at difficult
ones. With the addition of generality, we see that this set of four key
indicators can give us more insight on the results of AI benchmarks. In
particular, we explore two popular benchmarks in AI, the Arcade Learning
Environment (Atari 2600 games) and the General Video Game AI competition. We
provide some guidelines to estimate and interpret these indicators for other AI
benchmarks and competitions.
| [
{
"version": "v1",
"created": "Tue, 20 Nov 2018 11:26:36 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Mar 2019 17:31:24 GMT"
}
] | 1,553,472,000,000 | [
[
"Martínez-Plumed",
"Fernando",
""
],
[
"Hernández-Orallo",
"José",
""
]
] |
1811.08275 | Behzad Ghazanfari | Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor | Autonomous Extraction of a Hierarchical Structure of Tasks in
Reinforcement Learning, A Sequential Associate Rule Mining Approach | arXiv admin note: text overlap with arXiv:1709.04579 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning (RL) techniques, while often powerful, can suffer from
slow learning speeds, particularly in high dimensional spaces. Decomposition of
tasks into a hierarchical structure holds the potential to significantly speed
up learning, generalization, and transfer learning. However, the current task
decomposition techniques often rely on high-level knowledge provided by an
expert (e.g. using dynamic Bayesian networks) to extract a hierarchical task
structure; which is not necessarily available in autonomous systems. In this
paper, we propose a novel method based on Sequential Association Rule Mining
that can extract Hierarchical Structure of Tasks in Reinforcement Learning
(SARM-HSTRL) in an autonomous manner for both Markov decision processes (MDPs)
and factored MDPs. The proposed method leverages association rule mining to
discover the causal and temporal relationships among states in different
trajectories, and extracts a task hierarchy that captures these relationships
among sub-goals as termination conditions of different sub-tasks. We prove that
the extracted hierarchical policy offers a hierarchically optimal policy in
MDPs and factored MDPs. It should be noted that SARM-HSTRL extracts this
hierarchical optimal policy without having dynamic Bayesian networks in
scenarios with a single task trajectory and also with multiple tasks'
trajectories. Furthermore, it has been theoretically and empirically shown that
the extracted hierarchical task structure is consistent with trajectories and
provides the most efficient, reliable, and compact structure under appropriate
assumptions. The numerical results compare the performance of the proposed
SARM-HSTRL method with conventional HRL algorithms in terms of the accuracy in
detecting the sub-goals, the validity of the extracted hierarchies, and the
speed of learning in several testbeds.
| [
{
"version": "v1",
"created": "Sat, 17 Nov 2018 02:09:35 GMT"
}
] | 1,542,758,400,000 | [
[
"Ghazanfari",
"Behzad",
""
],
[
"Afghah",
"Fatemeh",
""
],
[
"Taylor",
"Matthew E.",
""
]
] |
1811.08318 | Thommen George Karimpanal | Thommen George Karimpanal and Roland Bouffanais | Self-Organizing Maps for Storage and Transfer of Knowledge in
Reinforcement Learning | 35 pages, 11 figures, Accepted in the journal Adaptive Behavior.
arXiv admin note: substantial text overlap with arXiv:1807.07530 | Adaptive Behavior 27 (2018) 111-126 | 10.1177/1059712318818568 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The idea of reusing or transferring information from previously learned tasks
(source tasks) for the learning of new tasks (target tasks) has the potential
to significantly improve the sample efficiency of a reinforcement learning
agent. In this work, we describe a novel approach for reusing previously
acquired knowledge by using it to guide the exploration of an agent while it
learns new tasks. In order to do so, we employ a variant of the growing
self-organizing map algorithm, which is trained using a measure of similarity
that is defined directly in the space of the vectorized representations of the
value functions. In addition to enabling transfer across tasks, the resulting
map is simultaneously used to enable the efficient storage of previously
acquired task knowledge in an adaptive and scalable manner. We empirically
validate our approach in a simulated navigation environment, and also
demonstrate its utility through simple experiments using a mobile
micro-robotics platform. In addition, we demonstrate the scalability of this
approach, and analytically examine its relation to the proposed network growth
mechanism. Further, we briefly discuss some of the possible improvements and
extensions to this approach, as well as its relevance to real world scenarios
in the context of continual learning.
| [
{
"version": "v1",
"created": "Sun, 18 Nov 2018 07:27:21 GMT"
}
] | 1,664,323,200,000 | [
[
"Karimpanal",
"Thommen George",
""
],
[
"Bouffanais",
"Roland",
""
]
] |
1811.08759 | Sonam Damani | Khyatti Gupta, Sonam Damani, Kedhar Nath Narahari | Using AI to Design Stone Jewelry | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Jewelry has been an integral part of human culture since ages. One of the
most popular styles of jewelry is created by putting together precious and
semi-precious stones in diverse patterns. While technology is finding its way
in the production process of such jewelry, designing it remains a
time-consuming and involved task. In this paper, we propose a unique approach
using optimization methods coupled with machine learning techniques to generate
novel stone jewelry designs at scale. Our evaluation shows that designs
generated by our approach are highly likeable and visually appealing.
| [
{
"version": "v1",
"created": "Wed, 21 Nov 2018 14:46:36 GMT"
}
] | 1,542,844,800,000 | [
[
"Gupta",
"Khyatti",
""
],
[
"Damani",
"Sonam",
""
],
[
"Narahari",
"Kedhar Nath",
""
]
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.