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2101.02120 | Dennis Soemers | \'Eric Piette, Cameron Browne and Dennis J. N. J. Soemers | Ludii Game Logic Guide | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This technical report outlines the fundamental workings of the game logic
behind Ludii, a general game system, that can be used to play a wide variety of
games. Ludii is a program developed for the ERC-funded Digital Ludeme Project,
in which mathematical and computational approaches are used to study how games
were played, and spread, throughout history. This report explains how general
game states and equipment are represented in Ludii, and how the rule ludemes
dictating play are implemented behind the scenes, giving some insight into the
core game logic behind the Ludii general game player. This guide is intended to
help game designers using the Ludii game description language to understand it
more completely and make fuller use of its features when describing their
games.
| [
{
"version": "v1",
"created": "Wed, 6 Jan 2021 16:22:37 GMT"
},
{
"version": "v2",
"created": "Thu, 2 Jun 2022 13:06:50 GMT"
}
] | 1,654,214,400,000 | [
[
"Piette",
"Éric",
""
],
[
"Browne",
"Cameron",
""
],
[
"Soemers",
"Dennis J. N. J.",
""
]
] |
2101.02178 | Oscar Hsu LiJen | Oscar LiJen Hsu | Improving Training Result of Partially Observable Markov Decision
Process by Filtering Beliefs | 7 pages with rich pictures to show the idea of POMDP | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this study I proposed a filtering beliefs method for improving performance
of Partially Observable Markov Decision Processes(POMDPs), which is a method
wildly used in autonomous robot and many other domains concerning control
policy. My method search and compare every similar belief pair. Because a
similar belief have insignificant influence on control policy, the belief is
filtered out for reducing training time. The empirical results show that the
proposed method outperforms the point-based approximate POMDPs in terms of the
quality of training results as well as the efficiency of the method.
| [
{
"version": "v1",
"created": "Tue, 5 Jan 2021 04:24:54 GMT"
}
] | 1,609,977,600,000 | [
[
"Hsu",
"Oscar LiJen",
""
]
] |
2101.02179 | Mark McPherson | Mark McPherson | The case for psychometric artificial general intelligence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A short review of the literature on measurement and detection of artificial
general intelligence is made. Proposed benchmarks and tests for artificial
general intelligence are critically evaluated against multiple criteria. Based
on the findings, the most promising approaches are identified and some useful
directions for future work are proposed.
| [
{
"version": "v1",
"created": "Sun, 27 Dec 2020 23:45:03 GMT"
}
] | 1,609,977,600,000 | [
[
"McPherson",
"Mark",
""
]
] |
2101.02456 | Devika Jay | Jahnvi Patel, Devika Jay, Balaraman Ravindran, K.Shanti Swarup | Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time
Reactive Power Market_1 | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In real time electricity markets, the objective of generation companies while
bidding is to maximize their profit. The strategies for learning optimal
bidding have been formulated through game theoretical approaches and stochastic
optimization problems. Similar studies in reactive power markets have not been
reported so far because the network voltage operating conditions have an
increased impact on reactive power markets than on active power markets.
Contrary to active power markets, the bids of rivals are not directly related
to fuel costs in reactive power markets. Hence, the assumption of a suitable
probability distribution function is unrealistic, making the strategies adopted
in active power markets unsuitable for learning optimal bids in reactive power
market mechanisms. Therefore, a bidding strategy is to be learnt from market
observations and experience in imperfect oligopolistic competition-based
markets. In this paper, a pioneer work on learning optimal bidding strategies
from observation and experience in a three-stage reactive power market is
reported.
| [
{
"version": "v1",
"created": "Thu, 7 Jan 2021 09:44:00 GMT"
}
] | 1,610,064,000,000 | [
[
"Patel",
"Jahnvi",
""
],
[
"Jay",
"Devika",
""
],
[
"Ravindran",
"Balaraman",
""
],
[
"Swarup",
"K. Shanti",
""
]
] |
2101.02459 | Ningxin Xu | Ningxin Xu, Cheng Yang, Yixin Zhu, Xiaowei Hu, Changhu Wang | Incorporating Vision Bias into Click Models for Image-oriented Search
Engine | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most typical click models assume that the probability of a document to be
examined by users only depends on position, such as PBM and UBM. It works well
in various kinds of search engines. However, in a search engine where massive
candidate documents display images as responses to the query, the examination
probability should not only depend on position. The visual appearance of an
image-oriented document also plays an important role in its opportunity to be
examined. In this paper, we assume that vision bias exists in an image-oriented
search engine as another crucial factor affecting the examination probability
aside from position. Specifically, we apply this assumption to classical click
models and propose an extended model, to better capture the examination
probabilities of documents. We use regression-based EM algorithm to predict the
vision bias given the visual features extracted from candidate documents.
Empirically, we evaluate our model on a dataset developed from a real-world
online image-oriented search engine, and demonstrate that our proposed model
can achieve significant improvements over its baseline model in data fitness
and sparsity handling.
| [
{
"version": "v1",
"created": "Thu, 7 Jan 2021 10:01:31 GMT"
}
] | 1,610,064,000,000 | [
[
"Xu",
"Ningxin",
""
],
[
"Yang",
"Cheng",
""
],
[
"Zhu",
"Yixin",
""
],
[
"Hu",
"Xiaowei",
""
],
[
"Wang",
"Changhu",
""
]
] |
2101.02634 | Dongjie Wang | Dongjie Wang, Pengyang Wang, Kunpeng Liu, Yuanchun Zhou, Charles
Hughes, Yanjie Fu | Reinforced Imitative Graph Representation Learning for Mobile User
Profiling: An Adversarial Training Perspective | AAAI 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study the problem of mobile user profiling, which is a
critical component for quantifying users' characteristics in the human mobility
modeling pipeline. Human mobility is a sequential decision-making process
dependent on the users' dynamic interests. With accurate user profiles, the
predictive model can perfectly reproduce users' mobility trajectories. In the
reverse direction, once the predictive model can imitate users' mobility
patterns, the learned user profiles are also optimal. Such intuition motivates
us to propose an imitation-based mobile user profiling framework by exploiting
reinforcement learning, in which the agent is trained to precisely imitate
users' mobility patterns for optimal user profiles. Specifically, the proposed
framework includes two modules: (1) representation module, which produces state
combining user profiles and spatio-temporal context in real-time; (2) imitation
module, where Deep Q-network (DQN) imitates the user behavior (action) based on
the state that is produced by the representation module. However, there are two
challenges in running the framework effectively. First, epsilon-greedy strategy
in DQN makes use of the exploration-exploitation trade-off by randomly pick
actions with the epsilon probability. Such randomness feeds back to the
representation module, causing the learned user profiles unstable. To solve the
problem, we propose an adversarial training strategy to guarantee the
robustness of the representation module. Second, the representation module
updates users' profiles in an incremental manner, requiring integrating the
temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM),
we introduce a gated mechanism to incorporate new and old user characteristics
into the user profile.
| [
{
"version": "v1",
"created": "Thu, 7 Jan 2021 17:10:00 GMT"
}
] | 1,610,064,000,000 | [
[
"Wang",
"Dongjie",
""
],
[
"Wang",
"Pengyang",
""
],
[
"Liu",
"Kunpeng",
""
],
[
"Zhou",
"Yuanchun",
""
],
[
"Hughes",
"Charles",
""
],
[
"Fu",
"Yanjie",
""
]
] |
2101.02648 | Quratul-Ain Mahesar | Quratul-ain Mahesar and Simon Parsons | Argument Schemes and Dialogue for Explainable Planning | arXiv admin note: text overlap with arXiv:2005.05849 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) is being increasingly deployed in practical
applications. However, there is a major concern whether AI systems will be
trusted by humans. In order to establish trust in AI systems, there is a need
for users to understand the reasoning behind their solutions. Therefore,
systems should be able to explain and justify their output. In this paper, we
propose an argument scheme-based approach to provide explanations in the domain
of AI planning. We present novel argument schemes to create arguments that
explain a plan and its key elements; and a set of critical questions that allow
interaction between the arguments and enable the user to obtain further
information regarding the key elements of the plan. Furthermore, we present a
novel dialogue system using the argument schemes and critical questions for
providing interactive dialectical explanations.
| [
{
"version": "v1",
"created": "Thu, 7 Jan 2021 17:43:12 GMT"
},
{
"version": "v2",
"created": "Sun, 14 Feb 2021 23:03:42 GMT"
}
] | 1,613,433,600,000 | [
[
"Mahesar",
"Quratul-ain",
""
],
[
"Parsons",
"Simon",
""
]
] |
2101.02991 | Pathan Faisal Khan | Faisal Khan and Debdeep Bose | Artificial Intelligence enabled Smart Learning | 4 | ETH Learning and Teaching Journal: ICED 2020 Proceedings (2020)
153-156 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) is a discipline of computer science that deals
with machine intelligence. It is essential to bring AI into the context of
learning because it helps in analysing the enormous amounts of data that is
collected from individual students, teachers and academic staff. The major
priorities of implementing AI in education are making innovative use of
existing digital technologies for learning, and teaching practices that
significantly improve traditional educational methods. The main problem with
traditional learning is that it cannot be suited to every student in class.
Some students may grasp the concepts well, while some may have difficulties in
understanding them and some may be more auditory or visual learners. The World
Bank report on education has indicated that the learning gap created by this
problem causes many students to drop out (World Development Report, 2018).
Personalised learning has been able to solve this grave problem.
| [
{
"version": "v1",
"created": "Fri, 8 Jan 2021 12:49:33 GMT"
}
] | 1,610,323,200,000 | [
[
"Khan",
"Faisal",
""
],
[
"Bose",
"Debdeep",
""
]
] |
2101.03210 | Sarvenaz Chaeibakhsh | Sarvenaz Chaeibakhsh, Roya Sabbagh Novin, Tucker Hermans, Andrew
Merryweather and Alan Kuntz | Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls | Accepted in: "10th International Conference on Operations Research
and Enterprise Systems". 13 pages, 10 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite years of research into patient falls in hospital rooms, falls and
related injuries remain a serious concern to patient safety. In this work, we
formulate a gradient-free constrained optimization problem to generate and
reconfigure the hospital room interior layout to minimize the risk of falls. We
define a cost function built on a hospital room fall model that takes into
account the supportive or hazardous effect of the patient's surrounding
objects, as well as simulated patient trajectories inside the room. We define a
constraint set that ensures the functionality of the generated room layouts in
addition to conforming to architectural guidelines. We solve this problem
efficiently using a variant of simulated annealing. We present results for two
real-world hospital room types and demonstrate a significant improvement of 18%
on average in patient fall risk when compared with a traditional hospital room
layout and 41% when compared with randomly generated layouts.
| [
{
"version": "v1",
"created": "Fri, 8 Jan 2021 20:31:10 GMT"
}
] | 1,610,409,600,000 | [
[
"Chaeibakhsh",
"Sarvenaz",
""
],
[
"Novin",
"Roya Sabbagh",
""
],
[
"Hermans",
"Tucker",
""
],
[
"Merryweather",
"Andrew",
""
],
[
"Kuntz",
"Alan",
""
]
] |
2101.03563 | Tristan Cazenave | Tristan Cazenave and Jean-Baptiste Sevestre and Matthieu Toulemont | Stabilized Nested Rollout Policy Adaptation | arXiv admin note: text overlap with arXiv:2003.10024 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm for
single player games. In this paper we propose to modify NRPA in order to
improve the stability of the algorithm. Experiments show it improves the
algorithm for different application domains: SameGame, Traveling Salesman with
Time Windows and Expression Discovery.
| [
{
"version": "v1",
"created": "Sun, 10 Jan 2021 15:05:14 GMT"
}
] | 1,610,409,600,000 | [
[
"Cazenave",
"Tristan",
""
],
[
"Sevestre",
"Jean-Baptiste",
""
],
[
"Toulemont",
"Matthieu",
""
]
] |
2101.03936 | Rocsildes Canoy | Rocsildes Canoy, V\'ictor Bucarey, Jayanta Mandi, Tias Guns | Learn-n-Route: Learning implicit preferences for vehicle routing | arXiv admin note: text overlap with arXiv:1909.07893 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate a learning decision support system for vehicle routing, where
the routing engine learns implicit preferences that human planners have when
manually creating route plans (or routings). The goal is to use these learned
subjective preferences on top of the distance-based objective criterion in
vehicle routing systems. This is an alternative to the practice of
distinctively formulating a custom VRP for every company with its own routing
requirements. Instead, we assume the presence of past vehicle routing solutions
over similar sets of customers, and learn to make similar choices. The learning
approach is based on the concept of learning a Markov model, which corresponds
to a probabilistic transition matrix, rather than a deterministic distance
matrix. This nevertheless allows us to use existing arc routing VRP software in
creating the actual routings, and to optimize over both distances and
preferences at the same time. For the learning, we explore different schemes to
construct the probabilistic transition matrix that can co-evolve with changing
preferences over time. Our results on a use-case with a small transportation
company show that our method is able to generate results that are close to the
manually created solutions, without needing to characterize all constraints and
sub-objectives explicitly. Even in the case of changes in the customer sets,
our method is able to find solutions that are closer to the actual routings
than when using only distances, and hence, solutions that require fewer manual
changes when transformed into practical routings.
| [
{
"version": "v1",
"created": "Mon, 11 Jan 2021 14:57:46 GMT"
}
] | 1,610,409,600,000 | [
[
"Canoy",
"Rocsildes",
""
],
[
"Bucarey",
"Víctor",
""
],
[
"Mandi",
"Jayanta",
""
],
[
"Guns",
"Tias",
""
]
] |
2101.04017 | Antonio Lieto | Antonio Lieto, Gian Luca Pozzato, Stefano Zoia, Viviana Patti, Rossana
Damiano | A Commonsense Reasoning Framework for Explanatory Emotion Attribution,
Generation and Re-classification | 50 pages. This work has been partially funded from the European
Research Council (ERC) under the European Union'sHorizon 2020 research and
innovation programme, grant agreement n{\deg}870811 | Knowledge-Based Systems, 2021 | 10.1016/j.knosys.2021.107166 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present DEGARI (Dynamic Emotion Generator And ReclassIfier), an
explainable system for emotion attribution and recommendation. This system
relies on a recently introduced commonsense reasoning framework, the TCL logic,
which is based on a human-like procedure for the automatic generation of novel
concepts in a Description Logics knowledge base. Starting from an ontological
formalization of emotions based on the Plutchik model, known as ArsEmotica, the
system exploits the logic TCL to automatically generate novel commonsense
semantic representations of compound emotions (e.g. Love as derived from the
combination of Joy and Trust according to Plutchik). The generated emotions
correspond to prototypes, i.e. commonsense representations of given concepts,
and have been used to reclassify emotion-related contents in a variety of
artistic domains, ranging from art datasets to the editorial contents available
in RaiPlay, the online platform of RAI Radiotelevisione Italiana (the Italian
public broadcasting company). We show how the reported results (evaluated in
the light of the obtained reclassifications, the user ratings assigned to such
reclassifications, and their explainability) are encouraging, and pave the way
to many further research directions.
| [
{
"version": "v1",
"created": "Mon, 11 Jan 2021 16:44:38 GMT"
},
{
"version": "v2",
"created": "Fri, 14 May 2021 13:58:59 GMT"
},
{
"version": "v3",
"created": "Wed, 26 May 2021 13:48:08 GMT"
},
{
"version": "v4",
"created": "Mon, 31 May 2021 20:53:30 GMT"
},
{
"version": "v5",
"created": "Wed, 2 Jun 2021 11:10:56 GMT"
}
] | 1,622,678,400,000 | [
[
"Lieto",
"Antonio",
""
],
[
"Pozzato",
"Gian Luca",
""
],
[
"Zoia",
"Stefano",
""
],
[
"Patti",
"Viviana",
""
],
[
"Damiano",
"Rossana",
""
]
] |
2101.04640 | Filip Ilievski | Filip Ilievski, Alessandro Oltramari, Kaixin Ma, Bin Zhang, Deborah L.
McGuinness, Pedro Szekely | Dimensions of Commonsense Knowledge | null | Knowledge-Based Systems 2021 | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Commonsense knowledge is essential for many AI applications, including those
in natural language processing, visual processing, and planning. Consequently,
many sources that include commonsense knowledge have been designed and
constructed over the past decades. Recently, the focus has been on large
text-based sources, which facilitate easier integration with neural (language)
models and application to textual tasks, typically at the expense of the
semantics of the sources and their harmonization. Efforts to consolidate
commonsense knowledge have yielded partial success, with no clear path towards
a comprehensive solution. We aim to organize these sources around a common set
of dimensions of commonsense knowledge. We survey a wide range of popular
commonsense sources with a special focus on their relations. We consolidate
these relations into 13 knowledge dimensions. This consolidation allows us to
unify the separate sources and to compute indications of their coverage,
overlap, and gaps with respect to the knowledge dimensions. Moreover, we
analyze the impact of each dimension on downstream reasoning tasks that require
commonsense knowledge, observing that the temporal and desire/goal dimensions
are very beneficial for reasoning on current downstream tasks, while
distinctness and lexical knowledge have little impact. These results reveal
preferences for some dimensions in current evaluation, and potential neglect of
others.
| [
{
"version": "v1",
"created": "Tue, 12 Jan 2021 17:52:39 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Jul 2021 06:28:37 GMT"
}
] | 1,627,603,200,000 | [
[
"Ilievski",
"Filip",
""
],
[
"Oltramari",
"Alessandro",
""
],
[
"Ma",
"Kaixin",
""
],
[
"Zhang",
"Bin",
""
],
[
"McGuinness",
"Deborah L.",
""
],
[
"Szekely",
"Pedro",
""
]
] |
2101.05050 | Stassa Patsantzis | Stassa Patsantzis, Stephen H. Muggleton | Top Program Construction and Reduction for polynomial time
Meta-Interpretive Learning | 25 pages, 3 figures, to be published in Machine Learning Journal
Special Issue on Learning and Reasoning | Mach.Learn. 100, 755-778 (2021) | 10.1007/s10994-020-05945-w | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Meta-Interpretive Learners, like most ILP systems, learn by searching for a
correct hypothesis in the hypothesis space, the powerset of all constructible
clauses. We show how this exponentially-growing search can be replaced by the
construction of a Top program: the set of clauses in all correct hypotheses
that is itself a correct hypothesis. We give an algorithm for Top program
construction and show that it constructs a correct Top program in polynomial
time and from a finite number of examples. We implement our algorithm in Prolog
as the basis of a new MIL system, Louise, that constructs a Top program and
then reduces it by removing redundant clauses. We compare Louise to the
state-of-the-art search-based MIL system Metagol in experiments on grid world
navigation, graph connectedness and grammar learning datasets and find that
Louise improves on Metagol's predictive accuracy when the hypothesis space and
the target theory are both large, or when the hypothesis space does not include
a correct hypothesis because of "classification noise" in the form of
mislabelled examples. When the hypothesis space or the target theory are small,
Louise and Metagol perform equally well.
| [
{
"version": "v1",
"created": "Wed, 13 Jan 2021 13:39:21 GMT"
}
] | 1,631,577,600,000 | [
[
"Patsantzis",
"Stassa",
""
],
[
"Muggleton",
"Stephen H.",
""
]
] |
2101.05125 | Stephen Clark | Stephen Clark, Alexander Lerchner, Tamara von Glehn, Olivier Tieleman,
Richard Tanburn, Misha Dashevskiy, Matko Bosnjak | Formalising Concepts as Grounded Abstractions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The notion of concept has been studied for centuries, by philosophers,
linguists, cognitive scientists, and researchers in artificial intelligence
(Margolis & Laurence, 1999). There is a large literature on formal,
mathematical models of concepts, including a whole sub-field of AI -- Formal
Concept Analysis -- devoted to this topic (Ganter & Obiedkov, 2016). Recently,
researchers in machine learning have begun to investigate how methods from
representation learning can be used to induce concepts from raw perceptual data
(Higgins, Sonnerat, et al., 2018). The goal of this report is to provide a
formal account of concepts which is compatible with this latest work in deep
learning.
The main technical goal of this report is to show how techniques from
representation learning can be married with a lattice-theoretic formulation of
conceptual spaces. The mathematics of partial orders and lattices is a standard
tool for modelling conceptual spaces (Ch.2, Mitchell (1997), Ganter and
Obiedkov (2016)); however, there is no formal work that we are aware of which
defines a conceptual lattice on top of a representation that is induced using
unsupervised deep learning (Goodfellow et al., 2016). The advantages of
partially-ordered lattice structures are that these provide natural mechanisms
for use in concept discovery algorithms, through the meets and joins of the
lattice.
| [
{
"version": "v1",
"created": "Wed, 13 Jan 2021 15:22:01 GMT"
}
] | 1,610,582,400,000 | [
[
"Clark",
"Stephen",
""
],
[
"Lerchner",
"Alexander",
""
],
[
"von Glehn",
"Tamara",
""
],
[
"Tieleman",
"Olivier",
""
],
[
"Tanburn",
"Richard",
""
],
[
"Dashevskiy",
"Misha",
""
],
[
"Bosnjak",
"Matko",
""
]
] |
2101.05851 | Chenda Zhang | Chenda Zhang, Hedvig Kjellstr\"om | A Subjective Model of Human Decision Making Based on Quantum Decision
Theory | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Computer modeling of human decision making is of large importance for, e.g.,
sustainable transport, urban development, and online recommendation systems. In
this paper we present a model for predicting the behavior of an individual
during a binary game under different amounts of risk, gain, and time pressure.
The model is based on Quantum Decision Theory (QDT), which has been shown to
enable modeling of the irrational and subjective aspects of the decision
making, not accounted for by the classical Cumulative Prospect Theory (CPT).
Experiments on two different datasets show that our QDT-based approach
outperforms both a CPT-based approach and data driven approaches such as
feed-forward neural networks and random forests.
| [
{
"version": "v1",
"created": "Thu, 14 Jan 2021 20:02:51 GMT"
}
] | 1,610,928,000,000 | [
[
"Zhang",
"Chenda",
""
],
[
"Kjellström",
"Hedvig",
""
]
] |
2101.06091 | Sepinoud Azimi | Ivan Porres, Sepinoud Azimi, S\'ebastien Lafond, Johan Lilius, Johanna
Salokannel, Mirva Salokorpi | On the Verification and Validation of AI Navigation Algorithms | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper explores the state of the art on to methods to verify and validate
navigation algorithms for autonomous surface ships. We perform a systematic
mapping study to find research works published in the last 10 years proposing
new algorithms for autonomous navigation and collision avoidance and we have
extracted what verification and validation approaches have been applied on
these algorithms. We observe that most research works use simulations to
validate their algorithms. However, these simulations often involve just a few
scenarios designed manually. This raises the question if the algorithms have
been validated properly. To remedy this, we propose the use of a systematic
scenario-based testing approach to validate navigation algorithms extensively.
| [
{
"version": "v1",
"created": "Fri, 15 Jan 2021 13:15:23 GMT"
}
] | 1,610,928,000,000 | [
[
"Porres",
"Ivan",
""
],
[
"Azimi",
"Sepinoud",
""
],
[
"Lafond",
"Sébastien",
""
],
[
"Lilius",
"Johan",
""
],
[
"Salokannel",
"Johanna",
""
],
[
"Salokorpi",
"Mirva",
""
]
] |
2101.06177 | Miquel Junyent | Miquel Junyent, Vicen\c{c} G\'omez, Anders Jonsson | Hierarchical Width-Based Planning and Learning | null | Proceedings of the Thirty-First International Conference on
Automated Planning and Scheduling (ICAPS 2021) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Width-based search methods have demonstrated state-of-the-art performance in
a wide range of testbeds, from classical planning problems to image-based
simulators such as Atari games. These methods scale independently of the size
of the state-space, but exponentially in the problem width. In practice,
running the algorithm with a width larger than 1 is computationally
intractable, prohibiting IW from solving higher width problems. In this paper,
we present a hierarchical algorithm that plans at two levels of abstraction. A
high-level planner uses abstract features that are incrementally discovered
from low-level pruning decisions. We illustrate this algorithm in classical
planning PDDL domains as well as in pixel-based simulator domains. In classical
planning, we show how IW(1) at two levels of abstraction can solve problems of
width 2. For pixel-based domains, we show how in combination with a learned
policy and a learned value function, the proposed hierarchical IW can
outperform current flat IW-based planners in Atari games with sparse rewards.
| [
{
"version": "v1",
"created": "Fri, 15 Jan 2021 15:37:46 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Mar 2021 15:42:37 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Sep 2021 09:21:22 GMT"
}
] | 1,651,190,400,000 | [
[
"Junyent",
"Miquel",
""
],
[
"Gómez",
"Vicenç",
""
],
[
"Jonsson",
"Anders",
""
]
] |
2101.06373 | Shalini Pandey | Shalini Pandey, George Karypis, Jaideep Srivastava | An Empirical Comparison of Deep Learning Models for Knowledge Tracing on
Large-Scale Dataset | Accepted at AAAI workshop on AI in Education, Imagining Post-COVID
Education with AI, 2021 | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Knowledge tracing (KT) is the problem of modeling each student's mastery of
knowledge concepts (KCs) as (s)he engages with a sequence of learning
activities. It is an active research area to help provide learners with
personalized feedback and materials. Various deep learning techniques have been
proposed for solving KT. Recent release of large-scale student performance
dataset \cite{choi2019ednet} motivates the analysis of performance of deep
learning approaches that have been proposed to solve KT. Our analysis can help
understand which method to adopt when large dataset related to student
performance is available. We also show that incorporating contextual
information such as relation between exercises and student forget behavior
further improves the performance of deep learning models.
| [
{
"version": "v1",
"created": "Sat, 16 Jan 2021 04:58:17 GMT"
}
] | 1,611,014,400,000 | [
[
"Pandey",
"Shalini",
""
],
[
"Karypis",
"George",
""
],
[
"Srivastava",
"Jaideep",
""
]
] |
2101.06569 | Yankai Chen | Yankai Chen and Yaozu Wu and Shicheng Ma and Irwin King | A Literature Review of Recent Graph Embedding Techniques for Biomedical
Data | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid development of biomedical software and hardware, a large
amount of relational data interlinking genes, proteins, chemical components,
drugs, diseases, and symptoms has been collected for modern biomedical
research. Many graph-based learning methods have been proposed to analyze such
type of data, giving a deeper insight into the topology and knowledge behind
the biomedical data, which greatly benefit to both academic research and
industrial application for human healthcare. However, the main difficulty is
how to handle high dimensionality and sparsity of the biomedical graphs.
Recently, graph embedding methods provide an effective and efficient way to
address the above issues. It converts graph-based data into a low dimensional
vector space where the graph structural properties and knowledge information
are well preserved. In this survey, we conduct a literature review of recent
developments and trends in applying graph embedding methods for biomedical
data. We also introduce important applications and tasks in the biomedical
domain as well as associated public biomedical datasets.
| [
{
"version": "v1",
"created": "Sun, 17 Jan 2021 01:53:50 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Jan 2021 10:21:55 GMT"
}
] | 1,611,187,200,000 | [
[
"Chen",
"Yankai",
""
],
[
"Wu",
"Yaozu",
""
],
[
"Ma",
"Shicheng",
""
],
[
"King",
"Irwin",
""
]
] |
2101.06573 | Stefan Maetschke | Stefan Maetschke and David Martinez Iraola and Pieter Barnard and
Elaheh ShafieiBavani and Peter Zhong and Ying Xu and Antonio Jimeno Yepes | Understanding in Artificial Intelligence | 28 pages, 282 references | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current Artificial Intelligence (AI) methods, most based on deep learning,
have facilitated progress in several fields, including computer vision and
natural language understanding. The progress of these AI methods is measured
using benchmarks designed to solve challenging tasks, such as visual question
answering. A question remains of how much understanding is leveraged by these
methods and how appropriate are the current benchmarks to measure understanding
capabilities. To answer these questions, we have analysed existing benchmarks
and their understanding capabilities, defined by a set of understanding
capabilities, and current research streams. We show how progress has been made
in benchmark development to measure understanding capabilities of AI methods
and we review as well how current methods develop understanding capabilities.
| [
{
"version": "v1",
"created": "Sun, 17 Jan 2021 02:29:50 GMT"
}
] | 1,611,014,400,000 | [
[
"Maetschke",
"Stefan",
""
],
[
"Iraola",
"David Martinez",
""
],
[
"Barnard",
"Pieter",
""
],
[
"ShafieiBavani",
"Elaheh",
""
],
[
"Zhong",
"Peter",
""
],
[
"Xu",
"Ying",
""
],
[
"Yepes",
"Antonio Jimeno",
""
]
] |
2101.06883 | Guangyu Huo | Guangyu Huo, Yong Zhang, Junbin Gao, Boyue Wang, Yongli Hu, and Baocai
Yin | CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional
Network for Clustering | null | IEEE Transactions on Knowledge and Data Engineering 2021 | 10.1109/TKDE.2021.3125020 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the powerful learning ability of deep convolutional networks, deep
clustering methods can extract the most discriminative information from
individual data and produce more satisfactory clustering results. However,
existing deep clustering methods usually ignore the relationship between the
data. Fortunately, the graph convolutional network can handle such
relationship, opening up a new research direction for deep clustering. In this
paper, we propose a cross-attention based deep clustering framework, named
Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN),
which contains four main modules: the cross-attention fusion module which
innovatively concatenates the Content Auto-encoder module (CAE) relating to the
individual data and Graph Convolutional Auto-encoder module (GAE) relating to
the relationship between the data in a layer-by-layer manner, and the
self-supervised model that highlights the discriminative information for
clustering tasks. While the cross-attention fusion module fuses two kinds of
heterogeneous representation, the CAE module supplements the content
information for the GAE module, which avoids the over-smoothing problem of GCN.
In the GAE module, two novel loss functions are proposed that reconstruct the
content and relationship between the data, respectively. Finally, the
self-supervised module constrains the distributions of the middle layer
representations of CAE and GAE to be consistent. Experimental results on
different types of datasets prove the superiority and robustness of the
proposed CaEGCN.
| [
{
"version": "v1",
"created": "Mon, 18 Jan 2021 05:21:59 GMT"
}
] | 1,641,772,800,000 | [
[
"Huo",
"Guangyu",
""
],
[
"Zhang",
"Yong",
""
],
[
"Gao",
"Junbin",
""
],
[
"Wang",
"Boyue",
""
],
[
"Hu",
"Yongli",
""
],
[
"Yin",
"Baocai",
""
]
] |
2101.07007 | Honglin Li | Honglin Li, Roonak Rezvani, Magdalena Anita Kolanko, David J. Sharp,
Maitreyee Wairagkar, Ravi Vaidyanathan, Ramin Nilforooshan, Payam Barnaghi | An attention model to analyse the risk of agitation and urinary tract
infections in people with dementia | 11 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Behavioural symptoms and urinary tract infections (UTI) are among the most
common problems faced by people with dementia. One of the key challenges in the
management of these conditions is early detection and timely intervention in
order to reduce distress and avoid unplanned hospital admissions. Using in-home
sensing technologies and machine learning models for sensor data integration
and analysis provides opportunities to detect and predict clinically
significant events and changes in health status. We have developed an
integrated platform to collect in-home sensor data and performed an
observational study to apply machine learning models for agitation and UTI risk
analysis. We collected a large dataset from 88 participants with a mean age of
82 and a standard deviation of 6.5 (47 females and 41 males) to evaluate a new
deep learning model that utilises attention and rational mechanism. The
proposed solution can process a large volume of data over a period of time and
extract significant patterns in a time-series data (i.e. attention) and use the
extracted features and patterns to train risk analysis models (i.e. rational).
The proposed model can explain the predictions by indicating which time-steps
and features are used in a long series of time-series data. The model provides
a recall of 91\% and precision of 83\% in detecting the risk of agitation and
UTIs. This model can be used for early detection of conditions such as UTIs and
managing of neuropsychiatric symptoms such as agitation in association with
initial treatment and early intervention approaches. In our study we have
developed a set of clinical pathways for early interventions using the alerts
generated by the proposed model and a clinical monitoring team has been set up
to use the platform and respond to the alerts according to the created
intervention plans.
| [
{
"version": "v1",
"created": "Mon, 18 Jan 2021 11:15:15 GMT"
}
] | 1,611,014,400,000 | [
[
"Li",
"Honglin",
""
],
[
"Rezvani",
"Roonak",
""
],
[
"Kolanko",
"Magdalena Anita",
""
],
[
"Sharp",
"David J.",
""
],
[
"Wairagkar",
"Maitreyee",
""
],
[
"Vaidyanathan",
"Ravi",
""
],
[
"Nilforooshan",
"Ramin",
""
],
[
"Barnaghi",
"Payam",
""
]
] |
2101.07067 | Salma Chaieb | Salma Chaieb and Brahim Hnich and Ali Ben Mrad | Data Obsolescence Detection in the Light of Newly Acquired Valid
Observations | null | Applied Intelligence, 1-23 (2022) | 10.1007/s10489-022-03212-0 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The information describing the conditions of a system or a person is
constantly evolving and may become obsolete and contradict other information. A
database, therefore, must be consistently updated upon the acquisition of new
valid observations that contradict obsolete ones contained in the database. In
this paper, we propose a novel approach for dealing with the information
obsolescence problem. Our approach aims to detect, in real-time, contradictions
between observations and then identify the obsolete ones, given a
representation model. Since we work within an uncertain environment
characterized by the lack of information, we choose to use a Bayesian network
as our representation model and propose a new approximate concept,
$\epsilon$-Contradiction. The new concept is parameterised by a confidence
level of having a contradiction in a set of observations. We propose a
polynomial-time algorithm for detecting obsolete information. We show that the
resulting obsolete information is better represented by an AND-OR tree than a
simple set of observations. Finally, we demonstrate the effectiveness of our
approach on a real elderly fall-prevention database and showcase how this tree
can be used to give reliable recommendations to doctors. Our experiments give
systematically and substantially very good results.
| [
{
"version": "v1",
"created": "Mon, 18 Jan 2021 13:24:06 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Jul 2021 11:08:27 GMT"
},
{
"version": "v3",
"created": "Wed, 4 May 2022 13:12:07 GMT"
}
] | 1,651,708,800,000 | [
[
"Chaieb",
"Salma",
""
],
[
"Hnich",
"Brahim",
""
],
[
"Mrad",
"Ali Ben",
""
]
] |
2101.07220 | Dakota Thompson | Amro M. Farid, Dakota Thompson, Wester Schoonenberg | A Tensor-Based Formulation of Hetero-functional Graph Theory | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Recently, hetero-functional graph theory (HFGT) has developed as a means to
mathematically model the structure of large-scale complex flexible engineering
systems. It does so by fusing concepts from network science and model-based
systems engineering (MBSE). For the former, it utilizes multiple graph-based
data structures to support a matrix-based quantitative analysis. For the
latter, HFGT inherits the heterogeneity of conceptual and ontological
constructs found in model-based systems engineering including system form,
system function, and system concept. These diverse conceptual constructs
indicate multi-dimensional rather than two-dimensional relationships. This
paper provides the first tensor-based treatment of hetero-functional graph
theory. In particular, it addresses the ``system concept" and the
hetero-functional adjacency matrix from the perspective of tensors and
introduces the hetero-functional incidence tensor as a new data structure. The
tensor-based formulation described in this work makes a stronger tie between
HFGT and its ontological foundations in MBSE. Finally, the tensor-based
formulation facilitates several analytical results that provide an
understanding of the relationships between HFGT and multi-layer networks.
| [
{
"version": "v1",
"created": "Thu, 14 Jan 2021 15:08:19 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Oct 2022 18:50:14 GMT"
}
] | 1,665,705,600,000 | [
[
"Farid",
"Amro M.",
""
],
[
"Thompson",
"Dakota",
""
],
[
"Schoonenberg",
"Wester",
""
]
] |
2101.07337 | Zijian Zhang | Zijian Zhang, Jaspreet Singh, Ujwal Gadiraju, Avishek Anand | Dissonance Between Human and Machine Understanding | 23 pages, 5 figures | [J]. Proceedings of the ACM on Human-Computer Interaction, 2019,
3(CSCW): 1-23 | 10.1145/3359158 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Complex machine learning models are deployed in several critical domains
including healthcare and autonomous vehicles nowadays, albeit as functional
black boxes. Consequently, there has been a recent surge in interpreting
decisions of such complex models in order to explain their actions to humans.
Models that correspond to human interpretation of a task are more desirable in
certain contexts and can help attribute liability, build trust, expose biases
and in turn build better models. It is, therefore, crucial to understand how
and which models conform to human understanding of tasks. In this paper, we
present a large-scale crowdsourcing study that reveals and quantifies the
dissonance between human and machine understanding, through the lens of an
image classification task. In particular, we seek to answer the following
questions: Which (well-performing) complex ML models are closer to humans in
their use of features to make accurate predictions? How does task difficulty
affect the feature selection capability of machines in comparison to humans?
Are humans consistently better at selecting features that make image
recognition more accurate? Our findings have important implications on
human-machine collaboration, considering that a long term goal in the field of
artificial intelligence is to make machines capable of learning and reasoning
like humans.
| [
{
"version": "v1",
"created": "Mon, 18 Jan 2021 21:45:35 GMT"
}
] | 1,611,100,800,000 | [
[
"Zhang",
"Zijian",
""
],
[
"Singh",
"Jaspreet",
""
],
[
"Gadiraju",
"Ujwal",
""
],
[
"Anand",
"Avishek",
""
]
] |
2101.07498 | Benjamin Goertzel | Ben Goertzel | Paraconsistent Foundations for Quantum Probability | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is argued that a fuzzy version of 4-truth-valued paraconsistent logic
(with truth values corresponding to True, False, Both and Neither) can be
approximately isomorphically mapped into the complex-number algebra of quantum
probabilities. I.e., p-bits (paraconsistent bits) can be transformed into close
approximations of qubits. The approximation error can be made arbitrarily
small, at least in a formal sense, and can be related to the degree of
irreducible "evidential error" assumed to plague an observer's observations.
This logical correspondence manifests itself in program space via an
approximate mapping between probabilistic and quantum types in programming
languages.
| [
{
"version": "v1",
"created": "Tue, 19 Jan 2021 07:48:41 GMT"
}
] | 1,611,100,800,000 | [
[
"Goertzel",
"Ben",
""
]
] |
2101.07523 | Nicolas Becu | Ahmed Laatabi, Nicolas Becu (LIENSs), Nicolas Marilleau (UMMISCO),
C\'ecilia Pignon-Mussaud (LIENSs), Marion Amalric (CITERES), X. Bertin
(LIENSs), Brice Anselme (PRODIG), Elise Beck (PACTE) | Mapping and Describing Geospatial Data to Generalize Complex Mapping and
Describing Geospatial Data to Generalize Complex Models: The Case of
LittoSIM-GEN Models | null | International Journal of Geospatial and Environmental Research,
KAGES, 2020 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For some scientific questions, empirical data are essential to develop
reliable simulation models. These data usually come from different sources with
diverse and heterogeneous formats. The design of complex data-driven models is
often shaped by the structure of the data available in research projects.
Hence, applying such models to other case studies requires either to get
similar data or to transform new data to fit the model inputs. It is the case
of agent-based models (ABMs) that use advanced data structures such as
Geographic Information Systems data. We faced this problem in the LittoSIM-GEN
project when generalizing our participatory flooding model (LittoSIM) to new
territories. From this experience, we provide a mapping approach to structure,
describe, and automatize the integration of geospatial data into ABMs.
| [
{
"version": "v1",
"created": "Tue, 19 Jan 2021 09:16:05 GMT"
}
] | 1,611,100,800,000 | [
[
"Laatabi",
"Ahmed",
"",
"LIENSs"
],
[
"Becu",
"Nicolas",
"",
"LIENSs"
],
[
"Marilleau",
"Nicolas",
"",
"UMMISCO"
],
[
"Pignon-Mussaud",
"Cécilia",
"",
"LIENSs"
],
[
"Amalric",
"Marion",
"",
"CITERES"
],
[
"Bertin",
"X.",
"",
"LIENSs"
],
[
"Anselme",
"Brice",
"",
"PRODIG"
],
[
"Beck",
"Elise",
"",
"PACTE"
]
] |
2101.07570 | Thomas K.F. Chiu | Thomas K.F. Chiu, Helen Meng, Ching-Sing Chai, Irwin King, Savio Wong
and Yeung Yam | Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI)
Curriculum | 8 pages 5 figures | IEEE Transactions on Education 65, no. 1 (2021): 30-39 | 0.1109/TE.2021.3085878 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for
the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary
education and evaluated its efficacy. While AI is conventionally taught in
tertiary level education, our co-creation process successfully developed the
curriculum that has been used in secondary school teaching in Hong Kong and
received positive feedback. Background: AI4Future is a cross-sector project
that engages five major partners - CUHK Faculty of Engineering and Faculty of
Education, Hong Kong secondary schools, the government and the AI industry. A
team of 14 professors with expertise in engineering and education collaborated
with 17 principals and teachers from 6 secondary schools to co-create the
curriculum. This team formation bridges the gap between researchers in
engineering and education, together with practitioners in education context.
Research Questions: What are the main features of the curriculum content
developed through the co-creation process? Would the curriculum significantly
improve the students perceived competence in, as well as attitude and
motivation towards AI? What are the teachers perceptions of the co-creation
process that aims to accommodate and foster teacher autonomy? Methodology: This
study adopted a mix of quantitative and qualitative methods and involved 335
student participants. Findings: 1) two main features of learning resources, 2)
the students perceived greater competence, and developed more positive attitude
to learn AI, and 3) the co-creation process generated a variety of resources
which enhanced the teachers knowledge in AI, as well as fostered teachers
autonomy in bringing the subject matter into their classrooms.
| [
{
"version": "v1",
"created": "Tue, 19 Jan 2021 11:26:19 GMT"
}
] | 1,703,116,800,000 | [
[
"Chiu",
"Thomas K. F.",
""
],
[
"Meng",
"Helen",
""
],
[
"Chai",
"Ching-Sing",
""
],
[
"King",
"Irwin",
""
],
[
"Wong",
"Savio",
""
],
[
"Yam",
"Yeung",
""
]
] |
2101.08035 | C. Maria Keet | C. Maria Keet | Bias in ontologies -- a preliminary assessment | 10 pages, 4 figures, 2 tables, soon to be submitted to an
international conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logical theories in the form of ontologies and similar artefacts in computing
and IT are used for structuring, annotating, and querying data, among others,
and therewith influence data analytics regarding what is fed into the
algorithms. Algorithmic bias is a well-known notion, but what does bias mean in
the context of ontologies that provide a structuring mechanism for an
algorithm's input? What are the sources of bias there and how would they
manifest themselves in ontologies? We examine and enumerate types of bias
relevant for ontologies, and whether they are explicit or implicit. These eight
types are illustrated with examples from extant production-level ontologies and
samples from the literature. We then assessed three concurrently developed
COVID-19 ontologies on bias and detected different subsets of types of bias in
each one, to a greater or lesser extent. This first characterisation aims
contribute to a sensitisation of ethical aspects of ontologies primarily
regarding representation of information and knowledge.
| [
{
"version": "v1",
"created": "Wed, 20 Jan 2021 09:28:08 GMT"
}
] | 1,611,187,200,000 | [
[
"Keet",
"C. Maria",
""
]
] |
2101.08153 | Daniel Kroening | Mirco Giacobbe, Mohammadhosein Hasanbeig, Daniel Kroening, Hjalmar
Wijk | Shielding Atari Games with Bounded Prescience | To appear at AAMAS 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep reinforcement learning (DRL) is applied in safety-critical domains such
as robotics and autonomous driving. It achieves superhuman abilities in many
tasks, however whether DRL agents can be shown to act safely is an open
problem. Atari games are a simple yet challenging exemplar for evaluating the
safety of DRL agents and feature a diverse portfolio of game mechanics. The
safety of neural agents has been studied before using methods that either
require a model of the system dynamics or an abstraction; unfortunately, these
are unsuitable to Atari games because their low-level dynamics are complex and
hidden inside their emulator. We present the first exact method for analysing
and ensuring the safety of DRL agents for Atari games. Our method only requires
access to the emulator. First, we give a set of 43 properties that characterise
"safe behaviour" for 30 games. Second, we develop a method for exploring all
traces induced by an agent and a game and consider a variety of sources of game
non-determinism. We observe that the best available DRL agents reliably satisfy
only very few properties; several critical properties are violated by all
agents. Finally, we propose a countermeasure that combines a bounded
explicit-state exploration with shielding. We demonstrate that our method
improves the safety of all agents over multiple properties.
| [
{
"version": "v1",
"created": "Wed, 20 Jan 2021 14:22:04 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jan 2021 14:08:01 GMT"
}
] | 1,611,532,800,000 | [
[
"Giacobbe",
"Mirco",
""
],
[
"Hasanbeig",
"Mohammadhosein",
""
],
[
"Kroening",
"Daniel",
""
],
[
"Wijk",
"Hjalmar",
""
]
] |
2101.08169 | Paulo Andr\'e Lima de Castro | Paulo Andr\'e Lima de Castro | mt5se: An Open Source Framework for Building Autonomous Trading Robots | This paper replaces an old version of the framework, called mt5b3,
which is now deprecated | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Autonomous trading robots have been studied in artificial intelligence area
for quite some time. Many AI techniques have been tested for building
autonomous agents able to trade financial assets. These initiatives include
traditional neural networks, fuzzy logic, reinforcement learning but also more
recent approaches like deep neural networks and deep reinforcement learning.
Many developers claim to be successful in creating robots with great
performance when simulating execution with historical price series, so called
backtesting. However, when these robots are used in real markets frequently
they present poor performance in terms of risks and return. In this paper, we
propose an open source framework (mt5se) that helps the development,
backtesting, live testing and real operation of autonomous traders. We built
and tested several traders using mt5se. The results indicate that it may help
the development of better traders. Furthermore, we discuss the simple
architecture that is used in many studies and propose an alternative multiagent
architecture. Such architecture separates two main concerns for portfolio
manager (PM) : price prediction and capital allocation. More than achieve a
high accuracy, a PM should increase profits when it is right and reduce loss
when it is wrong. Furthermore, price prediction is highly dependent of asset's
nature and history, while capital allocation is dependent only on analyst's
prediction performance and assets' correlation. Finally, we discuss some
promising technologies in the area.
| [
{
"version": "v1",
"created": "Wed, 20 Jan 2021 15:01:02 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Dec 2021 12:19:21 GMT"
},
{
"version": "v3",
"created": "Tue, 28 Jun 2022 23:14:56 GMT"
}
] | 1,656,547,200,000 | [
[
"de Castro",
"Paulo André Lima",
""
]
] |
2101.08758 | Pedro Saleiro | S\'ergio Jesus, Catarina Bel\'em, Vladimir Balayan, Jo\~ao Bento,
Pedro Saleiro, Pedro Bizarro, Jo\~ao Gama | How can I choose an explainer? An Application-grounded Evaluation of
Post-hoc Explanations | Accepted at FAccT'21, the ACM Conference on Fairness, Accountability,
and Transparency | null | 10.1145/3442188.3445941 10.1145/3442188.3445941 10.1145/3442188.3445941
10.1145/3442188.3445941 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There have been several research works proposing new Explainable AI (XAI)
methods designed to generate model explanations having specific properties, or
desiderata, such as fidelity, robustness, or human-interpretability. However,
explanations are seldom evaluated based on their true practical impact on
decision-making tasks. Without that assessment, explanations might be chosen
that, in fact, hurt the overall performance of the combined system of ML model
+ end-users. This study aims to bridge this gap by proposing XAI Test, an
application-grounded evaluation methodology tailored to isolate the impact of
providing the end-user with different levels of information. We conducted an
experiment following XAI Test to evaluate three popular post-hoc explanation
methods -- LIME, SHAP, and TreeInterpreter -- on a real-world fraud detection
task, with real data, a deployed ML model, and fraud analysts. During the
experiment, we gradually increased the information provided to the fraud
analysts in three stages: Data Only, i.e., just transaction data without access
to model score nor explanations, Data + ML Model Score, and Data + ML Model
Score + Explanations. Using strong statistical analysis, we show that, in
general, these popular explainers have a worse impact than desired. Some of the
conclusion highlights include: i) showing Data Only results in the highest
decision accuracy and the slowest decision time among all variants tested, ii)
all the explainers improve accuracy over the Data + ML Model Score variant but
still result in lower accuracy when compared with Data Only; iii) LIME was the
least preferred by users, probably due to its substantially lower variability
of explanations from case to case.
| [
{
"version": "v1",
"created": "Thu, 21 Jan 2021 18:15:13 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jan 2021 12:05:16 GMT"
}
] | 1,611,532,800,000 | [
[
"Jesus",
"Sérgio",
""
],
[
"Belém",
"Catarina",
""
],
[
"Balayan",
"Vladimir",
""
],
[
"Bento",
"João",
""
],
[
"Saleiro",
"Pedro",
""
],
[
"Bizarro",
"Pedro",
""
],
[
"Gama",
"João",
""
]
] |
2101.08986 | Stefano Giani | Kavyashree Ranawat and Stefano Giani | Artificial intelligence prediction of stock prices using social media | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The primary objective of this work is to develop a Neural Network based on
LSTM to predict stock market movements using tweets. Word embeddings, used in
the LSTM network, are initialised using Stanford's GloVe embeddings, pretrained
specifically on 2 billion tweets. To overcome the limited size of the dataset,
an augmentation strategy is proposed to split each input sequence into 150
subsets. To achieve further improvements in the original configuration,
hyperparameter optimisation is performed. The effects of variation in
hyperparameters such as dropout rate, batch size, and LSTM hidden state output
size are assessed individually. Furthermore, an exhaustive set of parameter
combinations is examined to determine the optimal model configuration. The best
performance on the validation dataset is achieved by hyperparameter combination
0.4,8,100 for the dropout, batch size, and hidden units respectively. The final
testing accuracy of the model is 76.14%.
| [
{
"version": "v1",
"created": "Fri, 22 Jan 2021 07:47:37 GMT"
}
] | 1,611,532,800,000 | [
[
"Ranawat",
"Kavyashree",
""
],
[
"Giani",
"Stefano",
""
]
] |
2101.09328 | Michael Walton | Andrew Fuchs, Michael Walton, Theresa Chadwick, Doug Lange | Theory of Mind for Deep Reinforcement Learning in Hanabi | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The partially observable card game Hanabi has recently been proposed as a new
AI challenge problem due to its dependence on implicit communication
conventions and apparent necessity of theory of mind reasoning for efficient
play. In this work, we propose a mechanism for imbuing Reinforcement Learning
agents with a theory of mind to discover efficient cooperative strategies in
Hanabi. The primary contributions of this work are threefold: First, a formal
definition of a computationally tractable mechanism for computing hand
probabilities in Hanabi. Second, an extension to conventional Deep
Reinforcement Learning that introduces reasoning over finitely nested theory of
mind belief hierarchies. Finally, an intrinsic reward mechanism enabled by
theory of mind that incentivizes agents to share strategically relevant private
knowledge with their teammates. We demonstrate the utility of our algorithm
against Rainbow, a state-of-the-art Reinforcement Learning agent.
| [
{
"version": "v1",
"created": "Fri, 22 Jan 2021 20:56:42 GMT"
}
] | 1,611,619,200,000 | [
[
"Fuchs",
"Andrew",
""
],
[
"Walton",
"Michael",
""
],
[
"Chadwick",
"Theresa",
""
],
[
"Lange",
"Doug",
""
]
] |
2101.09495 | Can Gao | Can Gao, Jie Zhoua, Duoqian Miao, Xiaodong Yue, Jun Wan | Granular conditional entropy-based attribute reduction for partially
labeled data with proxy labels | 22 pages, 5 figures, and 5 tables. Preprint submitted to Information
Sciences | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Attribute reduction is one of the most important research topics in the
theory of rough sets, and many rough sets-based attribute reduction methods
have thus been presented. However, most of them are specifically designed for
dealing with either labeled data or unlabeled data, while many real-world
applications come in the form of partial supervision. In this paper, we propose
a rough sets-based semi-supervised attribute reduction method for partially
labeled data. Particularly, with the aid of prior class distribution
information about data, we first develop a simple yet effective strategy to
produce the proxy labels for unlabeled data. Then the concept of information
granularity is integrated into the information-theoretic measure, based on
which, a novel granular conditional entropy measure is proposed, and its
monotonicity is proved in theory. Furthermore, a fast heuristic algorithm is
provided to generate the optimal reduct of partially labeled data, which could
accelerate the process of attribute reduction by removing irrelevant examples
and excluding redundant attributes simultaneously. Extensive experiments
conducted on UCI data sets demonstrate that the proposed semi-supervised
attribute reduction method is promising and even compares favourably with the
supervised methods on labeled data and unlabeled data with true labels in terms
of classification performance.
| [
{
"version": "v1",
"created": "Sat, 23 Jan 2021 12:50:09 GMT"
}
] | 1,611,619,200,000 | [
[
"Gao",
"Can",
""
],
[
"Zhoua",
"Jie",
""
],
[
"Miao",
"Duoqian",
""
],
[
"Yue",
"Xiaodong",
""
],
[
"Wan",
"Jun",
""
]
] |
2101.09562 | Olivier Teytaud | Dennis J. N. J. Soemers, Vegard Mella, Cameron Browne, Olivier Teytaud | Deep Learning for General Game Playing with Ludii and Polygames | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Combinations of Monte-Carlo tree search and Deep Neural Networks, trained
through self-play, have produced state-of-the-art results for automated
game-playing in many board games. The training and search algorithms are not
game-specific, but every individual game that these approaches are applied to
still requires domain knowledge for the implementation of the game's rules, and
constructing the neural network's architecture -- in particular the shapes of
its input and output tensors. Ludii is a general game system that already
contains over 500 different games, which can rapidly grow thanks to its
powerful and user-friendly game description language. Polygames is a framework
with training and search algorithms, which has already produced superhuman
players for several board games. This paper describes the implementation of a
bridge between Ludii and Polygames, which enables Polygames to train and
evaluate models for games that are implemented and run through Ludii. We do not
require any game-specific domain knowledge anymore, and instead leverage our
domain knowledge of the Ludii system and its abstract state and move
representations to write functions that can automatically determine the
appropriate shapes for input and output tensors for any game implemented in
Ludii. We describe experimental results for short training runs in a wide
variety of different board games, and discuss several open problems and avenues
for future research.
| [
{
"version": "v1",
"created": "Sat, 23 Jan 2021 19:08:33 GMT"
}
] | 1,611,619,200,000 | [
[
"Soemers",
"Dennis J. N. J.",
""
],
[
"Mella",
"Vegard",
""
],
[
"Browne",
"Cameron",
""
],
[
"Teytaud",
"Olivier",
""
]
] |
2101.09791 | Nitesh Kumar | Nitesh Kumar and Ond\v{r}ej Ku\v{z}elka | Context-Specific Likelihood Weighting | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Sampling is a popular method for approximate inference when exact inference
is impractical. Generally, sampling algorithms do not exploit context-specific
independence (CSI) properties of probability distributions. We introduce
context-specific likelihood weighting (CS-LW), a new sampling methodology,
which besides exploiting the classical conditional independence properties,
also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW
is based on partial assignments of random variables and requires fewer samples
for convergence due to the sampling variance reduction. Furthermore, the speed
of generating samples increases. Our novel notion of contextual assignments
theoretically justifies CS-LW. We empirically show that CS-LW is competitive
with state-of-the-art algorithms for approximate inference in the presence of a
significant amount of CSIs.
| [
{
"version": "v1",
"created": "Sun, 24 Jan 2021 20:23:14 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Feb 2021 12:25:58 GMT"
},
{
"version": "v3",
"created": "Sat, 27 Feb 2021 09:46:24 GMT"
}
] | 1,614,643,200,000 | [
[
"Kumar",
"Nitesh",
""
],
[
"Kuželka",
"Ondřej",
""
]
] |
2101.10162 | Giulia Francescutto | Giulia Francescutto, Konstantin Schekotihin, Mohammed M. S. El-Kholany | Solving a Multi-resource Partial-ordering Flexible Variant of the
Job-shop Scheduling Problem with Hybrid ASP | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Many complex activities of production cycles, such as quality control or
fault analysis, require highly experienced specialists to perform various
operations on (semi)finished products using different tools. In practical
scenarios, the selection of a next operation is complicated, since each expert
has only a local view on the total set of operations to be performed. As a
result, decisions made by the specialists are suboptimal and might cause
significant costs. In this paper, we consider a Multi-resource Partial-ordering
Flexible Job-shop Scheduling (MPF-JSS) problem where partially-ordered
sequences of operations must be scheduled on multiple required resources, such
as tools and specialists. The resources are flexible and can perform one or
more operations depending on their properties. The problem is modeled using
Answer Set Programming (ASP) in which the time assignments are efficiently done
using Difference Logic. Moreover, we suggest two multi-shot solving strategies
aiming at the identification of the time bounds allowing for a solution of the
schedule optimization problem. Experiments conducted on a set of instances
extracted from a medium-sized semiconductor fault analysis lab indicate that
our approach can find schedules for 87 out of 91 considered real-world
instances.
| [
{
"version": "v1",
"created": "Mon, 25 Jan 2021 15:21:32 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jan 2021 09:07:04 GMT"
}
] | 1,611,705,600,000 | [
[
"Francescutto",
"Giulia",
""
],
[
"Schekotihin",
"Konstantin",
""
],
[
"El-Kholany",
"Mohammed M. S.",
""
]
] |
2101.10179 | Marcus Westberg | Marcus Westberg, Kary Fr\"amling | Cognitive Perspectives on Context-based Decisions and Explanations | Part of IJCAI-PRICAI 2020 Workshop on XAI. Proceedings archived on
https://sites.google.com/view/xai2020/home | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When human cognition is modeled in Philosophy and Cognitive Science, there is
a pervasive idea that humans employ mental representations in order to navigate
the world and make predictions about outcomes of future actions. By
understanding how these representational structures work, we not only
understand more about human cognition but also gain a better understanding for
how humans rationalise and explain decisions. This has an influencing effect on
explainable AI, where the goal is to provide explanations of computer
decision-making for a human audience. We show that the Contextual Importance
and Utility method for XAI share an overlap with the current new wave of
action-oriented predictive representational structures, in ways that makes CIU
a reliable tool for creating explanations that humans can relate to and trust.
| [
{
"version": "v1",
"created": "Mon, 25 Jan 2021 15:49:52 GMT"
}
] | 1,611,619,200,000 | [
[
"Westberg",
"Marcus",
""
],
[
"Främling",
"Kary",
""
]
] |
2101.10670 | Tobias Joppen | Tobias Joppen and Johannes F\"urnkranz | Ordinal Monte Carlo Tree Search | preprint. arXiv admin note: substantial text overlap with
arXiv:1901.04274 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In many problem settings, most notably in game playing, an agent receives a
possibly delayed reward for its actions. Often, those rewards are handcrafted
and not naturally given. Even simple terminal-only rewards, like winning equals
one and losing equals minus one, can not be seen as an unbiased statement,
since these values are chosen arbitrarily, and the behavior of the learner may
change with different encodings. It is hard to argue about good rewards and the
performance of an agent often depends on the design of the reward signal. In
particular, in domains where states by nature only have an ordinal ranking and
where meaningful distance information between game state values is not
available, a numerical reward signal is necessarily biased. In this paper we
take a look at MCTS, a popular algorithm to solve MDPs, highlight a reoccurring
problem concerning its use of rewards, and show that an ordinal treatment of
the rewards overcomes this problem. Using the General Video Game Playing
framework we show dominance of our newly proposed ordinal MCTS algorithm over
other MCTS variants, based on a novel bandit algorithm that we also introduce
and test versus UCB.
| [
{
"version": "v1",
"created": "Tue, 26 Jan 2021 10:01:27 GMT"
}
] | 1,611,705,600,000 | [
[
"Joppen",
"Tobias",
""
],
[
"Fürnkranz",
"Johannes",
""
]
] |
2101.10964 | Oren Neumann | Oren Neumann, Claudius Gros | Investment vs. reward in a competitive knapsack problem | null | Learning Meets Combinatorial Algorithms at NeurIPS2020 (2020) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural selection drives species to develop brains, with sizes that increase
with the complexity of the tasks to be tackled. Our goal is to investigate the
balance between the metabolic costs of larger brains compared to the advantage
they provide in solving general and combinatorial problems. Defining advantage
as the performance relative to competitors, a two-player game based on the
knapsack problem is used. Within this framework, two opponents compete over
shared resources, with the goal of collecting more resources than the opponent.
Neural nets of varying sizes are trained using a variant of the AlphaGo Zero
algorithm. A surprisingly simple relation, $N_A/(N_A+N_B)$, is found for the
relative win rate of a net with $N_A$ neurons against one with $N_B$. Success
increases linearly with investments in additional resources when the networks
sizes are very different, i.e. when $N_A \ll N_B$, with returns diminishing
when both networks become comparable in size.
| [
{
"version": "v1",
"created": "Tue, 26 Jan 2021 17:47:56 GMT"
}
] | 1,611,792,000,000 | [
[
"Neumann",
"Oren",
""
],
[
"Gros",
"Claudius",
""
]
] |
2101.11844 | Iena Petronella Derks | Iena Petronella Derks and Alta de Waal | A Taxonomy of Explainable Bayesian Networks | null | In: Gerber A. (eds) Artificial Intelligence Research. SACAIR 2021.
Communications in Computer and Information Science, vol 1342. Springer, Cham
(2020) | 10.1007/978-3-030-66151-9_14 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial Intelligence (AI), and in particular, the explainability thereof,
has gained phenomenal attention over the last few years. Whilst we usually do
not question the decision-making process of these systems in situations where
only the outcome is of interest, we do however pay close attention when these
systems are applied in areas where the decisions directly influence the lives
of humans. It is especially noisy and uncertain observations close to the
decision boundary which results in predictions which cannot necessarily be
explained that may foster mistrust among end-users. This drew attention to AI
methods for which the outcomes can be explained. Bayesian networks are
probabilistic graphical models that can be used as a tool to manage
uncertainty. The probabilistic framework of a Bayesian network allows for
explainability in the model, reasoning and evidence. The use of these methods
is mostly ad hoc and not as well organised as explainability methods in the
wider AI research field. As such, we introduce a taxonomy of explainability in
Bayesian networks. We extend the existing categorisation of explainability in
the model, reasoning or evidence to include explanation of decisions. The
explanations obtained from the explainability methods are illustrated by means
of a simple medical diagnostic scenario. The taxonomy introduced in this paper
has the potential not only to encourage end-users to efficiently communicate
outcomes obtained, but also support their understanding of how and, more
importantly, why certain predictions were made.
| [
{
"version": "v1",
"created": "Thu, 28 Jan 2021 07:29:57 GMT"
}
] | 1,611,878,400,000 | [
[
"Derks",
"Iena Petronella",
""
],
[
"de Waal",
"Alta",
""
]
] |
2101.11870 | Anthony Hunter | Emmanuel Hadoux and Anthony Hunter and Sylwia Polberg | Strategic Argumentation Dialogues for Persuasion: Framework and
Experiments Based on Modelling the Beliefs and Concerns of the Persuadee | The Data Appendix containing the arguments, argument graphs,
assignment of concerns to arguments, preferences over concerns, and
assignment of beliefs to arguments, is available at the link
http://www0.cs.ucl.ac.uk/staff/a.hunter/papers/unistudydata.zip The code is
available at https://github.com/ComputationalPersuasion/MCCP | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Persuasion is an important and yet complex aspect of human intelligence. When
undertaken through dialogue, the deployment of good arguments, and therefore
counterarguments, clearly has a significant effect on the ability to be
successful in persuasion. Two key dimensions for determining whether an
argument is good in a particular dialogue are the degree to which the intended
audience believes the argument and counterarguments, and the impact that the
argument has on the concerns of the intended audience. In this paper, we
present a framework for modelling persuadees in terms of their beliefs and
concerns, and for harnessing these models in optimizing the choice of move in
persuasion dialogues. Our approach is based on the Monte Carlo Tree Search
which allows optimization in real-time. We provide empirical results of a study
with human participants showing that our automated persuasion system based on
this technology is superior to a baseline system that does not take the beliefs
and concerns into account in its strategy.
| [
{
"version": "v1",
"created": "Thu, 28 Jan 2021 08:49:24 GMT"
}
] | 1,611,878,400,000 | [
[
"Hadoux",
"Emmanuel",
""
],
[
"Hunter",
"Anthony",
""
],
[
"Polberg",
"Sylwia",
""
]
] |
2101.12047 | Samuel Alexander | Samuel Alexander, Bill Hibbard | Measuring Intelligence and Growth Rate: Variations on Hibbard's
Intelligence Measure | 25 pages | Journal of Artificial General Intelligence 12(1), 2021 | 10.2478/jagi-2021-0001 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In 2011, Hibbard suggested an intelligence measure for agents who compete in
an adversarial sequence prediction game. We argue that Hibbard's idea should
actually be considered as two separate ideas: first, that the intelligence of
such agents can be measured based on the growth rates of the runtimes of the
competitors that they defeat; and second, one specific (somewhat arbitrary)
method for measuring said growth rates. Whereas Hibbard's intelligence measure
is based on the latter growth-rate-measuring method, we survey other methods
for measuring function growth rates, and exhibit the resulting Hibbard-like
intelligence measures and taxonomies. Of particular interest, we obtain
intelligence taxonomies based on Big-O and Big-Theta notation systems, which
taxonomies are novel in that they challenge conventional notions of what an
intelligence measure should look like. We discuss how intelligence measurement
of sequence predictors can indirectly serve as intelligence measurement for
agents with Artificial General Intelligence (AGIs).
| [
{
"version": "v1",
"created": "Mon, 25 Jan 2021 01:54:08 GMT"
}
] | 1,611,878,400,000 | [
[
"Alexander",
"Samuel",
""
],
[
"Hibbard",
"Bill",
""
]
] |
2101.12639 | Tristan Cazenave | Tristan Cazenave and Swann Legras and V\'eronique Ventos | Optimizing $\alpha\mu$ | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | $\alpha\mu$ is a search algorithm which repairs two defaults of Perfect
Information Monte Carlo search: strategy fusion and non locality. In this paper
we optimize $\alpha\mu$ for the game of Bridge, avoiding useless computations.
The proposed optimizations are general and apply to other imperfect information
turn-based games. We define multiple optimizations involving Pareto fronts, and
show that these optimizations speed up the search. Some of these optimizations
are cuts that stop the search at a node, while others keep track of which
possible worlds have become redundant, avoiding unnecessary, costly
evaluations. We also measure the benefits of parallelizing the double dummy
searches at the leaves of the $\alpha\mu$ search tree.
| [
{
"version": "v1",
"created": "Fri, 29 Jan 2021 15:20:03 GMT"
}
] | 1,612,137,600,000 | [
[
"Cazenave",
"Tristan",
""
],
[
"Legras",
"Swann",
""
],
[
"Ventos",
"Véronique",
""
]
] |
2102.00333 | Peyman Setoodeh | Milad Vaali Esfahaani, Yanbo Xue, and Peyman Setoodeh | Deep Reinforcement Learning-Based Product Recommender for Online
Advertising | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In online advertising, recommender systems try to propose items from a list
of products to potential customers according to their interests. Such systems
have been increasingly deployed in E-commerce due to the rapid growth of
information technology and availability of large datasets. The ever-increasing
progress in the field of artificial intelligence has provided powerful tools
for dealing with such real-life problems. Deep reinforcement learning (RL) that
deploys deep neural networks as universal function approximators can be viewed
as a valid approach for design and implementation of recommender systems. This
paper provides a comparative study between value-based and policy-based deep RL
algorithms for designing recommender systems for online advertising. The
RecoGym environment is adopted for training these RL-based recommender systems,
where the long short term memory (LSTM) is deployed to build value and policy
networks in these two approaches, respectively. LSTM is used to take account of
the key role that order plays in the sequence of item observations by users.
The designed recommender systems aim at maximising the click-through rate (CTR)
for the recommended items. Finally, guidelines are provided for choosing proper
RL algorithms for different scenarios that the recommender system is expected
to handle.
| [
{
"version": "v1",
"created": "Sat, 30 Jan 2021 23:05:04 GMT"
}
] | 1,612,224,000,000 | [
[
"Esfahaani",
"Milad Vaali",
""
],
[
"Xue",
"Yanbo",
""
],
[
"Setoodeh",
"Peyman",
""
]
] |
2102.00339 | Peyman Setoodeh | Aref Hakimzadeh, Yanbo Xue, and Peyman Setoodeh | Enacted Visual Perception: A Computational Model based on Piaget
Equilibrium | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Maurice Merleau-Ponty's phenomenology of perception, analysis of
perception accounts for an element of intentionality, and in effect therefore,
perception and action cannot be viewed as distinct procedures. In the same line
of thinking, Alva No\"{e} considers perception as a thoughtful activity that
relies on capacities for action and thought. Here, by looking into psychology
as a source of inspiration, we propose a computational model for the action
involved in visual perception based on the notion of equilibrium as defined by
Jean Piaget. In such a model, Piaget's equilibrium reflects the mind's status,
which is used to control the observation process. The proposed model is built
around a modified version of convolutional neural networks (CNNs) with enhanced
filter performance, where characteristics of filters are adaptively adjusted
via a high-level control signal that accounts for the thoughtful activity in
perception. While the CNN plays the role of the visual system, the control
signal is assumed to be a product of mind.
| [
{
"version": "v1",
"created": "Sat, 30 Jan 2021 23:52:01 GMT"
}
] | 1,612,224,000,000 | [
[
"Hakimzadeh",
"Aref",
""
],
[
"Xue",
"Yanbo",
""
],
[
"Setoodeh",
"Peyman",
""
]
] |
2102.00417 | Pranay Lohia | Pranay Lohia | Priority-based Post-Processing Bias Mitigation for Individual and Group
Fairness | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Previous post-processing bias mitigation algorithms on both group and
individual fairness don't work on regression models and datasets with
multi-class numerical labels. We propose a priority-based post-processing bias
mitigation on both group and individual fairness with the notion that similar
individuals should get similar outcomes irrespective of socio-economic factors
and more the unfairness, more the injustice. We establish this proposition by a
case study on tariff allotment in a smart grid. Our novel framework establishes
it by using a user segmentation algorithm to capture the consumption strategy
better. This process ensures priority-based fair pricing for group and
individual facing the maximum injustice. It upholds the notion of fair tariff
allotment to the entire population taken into consideration without modifying
the in-built process for tariff calculation. We also validate our method and
show superior performance to previous work on a real-world dataset in criminal
sentencing.
| [
{
"version": "v1",
"created": "Sun, 31 Jan 2021 09:25:28 GMT"
}
] | 1,612,224,000,000 | [
[
"Lohia",
"Pranay",
""
]
] |
2102.00521 | Saksham Consul | Saksham Consul, Lovis Heindrich, Jugoslav Stojcheski, Falk Lieder | Improving Human Decision-Making by Discovering Efficient Strategies for
Hierarchical Planning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | To make good decisions in the real world people need efficient planning
strategies because their computational resources are limited. Knowing which
planning strategies would work best for people in different situations would be
very useful for understanding and improving human decision-making. But our
ability to compute those strategies used to be limited to very small and very
simple planning tasks. To overcome this computational bottleneck, we introduce
a cognitively-inspired reinforcement learning method that can overcome this
limitation by exploiting the hierarchical structure of human behavior. The
basic idea is to decompose sequential decision problems into two sub-problems:
setting a goal and planning how to achieve it. This hierarchical decomposition
enables us to discover optimal strategies for human planning in larger and more
complex tasks than was previously possible. The discovered strategies
outperform existing planning algorithms and achieve a super-human level of
computational efficiency. We demonstrate that teaching people to use those
strategies significantly improves their performance in sequential
decision-making tasks that require planning up to eight steps ahead. By
contrast, none of the previous approaches was able to improve human performance
on these problems. These findings suggest that our cognitively-informed
approach makes it possible to leverage reinforcement learning to improve human
decision-making in complex sequential decision-problems. Future work can
leverage our method to develop decision support systems that improve human
decision making in the real world.
| [
{
"version": "v1",
"created": "Sun, 31 Jan 2021 19:46:00 GMT"
}
] | 1,612,224,000,000 | [
[
"Consul",
"Saksham",
""
],
[
"Heindrich",
"Lovis",
""
],
[
"Stojcheski",
"Jugoslav",
""
],
[
"Lieder",
"Falk",
""
]
] |
2102.00567 | Hassan Moussa Mr | Hassan Moussa | Using Recursive KMeans and Dijkstra Algorithm to Solve CVRP | null | null | 10.13140/RG.2.2.20970.85447 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Capacitated vehicle routing problem (CVRP) is being one of the most common
optimization problems in our days, considering the wide usage of routing
algorithms in multiple fields such as transportation domain, food delivery,
network routing, ... Capacitated vehicle routing problem is classified as an
NP-Hard problem, hence normal optimization algorithm can't solve it. In our
paper, we discuss a new way to solve the mentioned problem, using a recursive
approach of the most known clustering algorithm "K-Means", one of the known
shortest path algorithm "Dijkstra", and some mathematical operations. In this
paper, we will show how to implement those methods together in order to get the
nearest solution of the optimal route, since research and development are still
on go, this research paper may be extended with another one, that will involve
the implementational results of this thoric side.
| [
{
"version": "v1",
"created": "Mon, 1 Feb 2021 00:03:03 GMT"
}
] | 1,640,649,600,000 | [
[
"Moussa",
"Hassan",
""
]
] |
2102.00572 | Peyman Setoodeh | Aref Hakimzadeh, Yanbo Xue, and Peyman Setoodeh | Interpretable Reinforcement Learning Inspired by Piaget's Theory of
Cognitive Development | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Endeavors for designing robots with human-level cognitive abilities have led
to different categories of learning machines. According to Skinner's theory,
reinforcement learning (RL) plays a key role in human intuition and cognition.
Majority of the state-of-the-art methods including deep RL algorithms are
strongly influenced by the connectionist viewpoint. Such algorithms can
significantly benefit from theories of mind and learning in other disciplines.
This paper entertains the idea that theories such as language of thought
hypothesis (LOTH), script theory, and Piaget's cognitive development theory
provide complementary approaches, which will enrich the RL field. Following
this line of thinking, a general computational building block is proposed for
Piaget's schema theory that supports the notions of productivity,
systematicity, and inferential coherence as described by Fodor in contrast with
the connectionism theory. Abstraction in the proposed method is completely upon
the system itself and is not externally constrained by any predefined
architecture. The whole process matches the Neisser's perceptual cycle model.
Performed experiments on three typical control problems followed by behavioral
analysis confirm the interpretability of the proposed method and its
competitiveness compared to the state-of-the-art algorithms. Hence, the
proposed framework can be viewed as a step towards achieving human-like
cognition in artificial intelligent systems.
| [
{
"version": "v1",
"created": "Mon, 1 Feb 2021 00:29:01 GMT"
}
] | 1,612,224,000,000 | [
[
"Hakimzadeh",
"Aref",
""
],
[
"Xue",
"Yanbo",
""
],
[
"Setoodeh",
"Peyman",
""
]
] |
2102.00617 | Hao Zhan | Dan Wan and Hao Zhan | The Controllability of Planning, Responsibility, and Security in
Automatic Driving Technology | 49th International Conference on Computers and Industrial
Engineering, CIE 2019. arXiv admin note: substantial text overlap with
arXiv:1906.07861 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | People hope automated driving technology is always in a stable and
controllable state; specifically, it can be divided into controllable planning,
controllable responsibility, and controllable information. When this
controllability is undermined, it brings about the problems, e.g., trolley
dilemma, responsibility attribution, information leakage, and security. This
article discusses these three types of issues separately and clarifies the
misunderstandings.
| [
{
"version": "v1",
"created": "Mon, 1 Feb 2021 03:41:37 GMT"
}
] | 1,612,224,000,000 | [
[
"Wan",
"Dan",
""
],
[
"Zhan",
"Hao",
""
]
] |
2102.00834 | Koen Holtman | Koen Holtman | Counterfactual Planning in AGI Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present counterfactual planning as a design approach for creating a range
of safety mechanisms that can be applied in hypothetical future AI systems
which have Artificial General Intelligence.
The key step in counterfactual planning is to use an AGI machine learning
system to construct a counterfactual world model, designed to be different from
the real world the system is in. A counterfactual planning agent determines the
action that best maximizes expected utility in this counterfactual planning
world, and then performs the same action in the real world.
We use counterfactual planning to construct an AGI agent emergency stop
button, and a safety interlock that will automatically stop the agent before it
undergoes an intelligence explosion. We also construct an agent with an input
terminal that can be used by humans to iteratively improve the agent's reward
function, where the incentive for the agent to manipulate this improvement
process is suppressed. As an example of counterfactual planning in a non-agent
AGI system, we construct a counterfactual oracle.
As a design approach, counterfactual planning is built around the use of a
graphical notation for defining mathematical counterfactuals. This two-diagram
notation also provides a compact and readable language for reasoning about the
complex types of self-referencing and indirect representation which are
typically present inside machine learning agents.
| [
{
"version": "v1",
"created": "Fri, 29 Jan 2021 13:44:14 GMT"
}
] | 1,612,224,000,000 | [
[
"Holtman",
"Koen",
""
]
] |
2102.00997 | Gorka Azkune | Aitzol Elu, Gorka Azkune, Oier Lopez de Lacalle, Ignacio
Arganda-Carreras, Aitor Soroa, Eneko Agirre | Inferring spatial relations from textual descriptions of images | Accepted in Pattern Recognition | Pattern Recognition, Volume 113, 2021, 107847 | 10.1016/j.patcog.2021.107847 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generating an image from its textual description requires both a certain
level of language understanding and common sense knowledge about the spatial
relations of the physical entities being described. In this work, we focus on
inferring the spatial relation between entities, a key step in the process of
composing scenes based on text. More specifically, given a caption containing a
mention to a subject and the location and size of the bounding box of that
subject, our goal is to predict the location and size of an object mentioned in
the caption. Previous work did not use the caption text information, but a
manually provided relation holding between the subject and the object. In fact,
the used evaluation datasets contain manually annotated ontological triplets
but no captions, making the exercise unrealistic: a manual step was required;
and systems did not leverage the richer information in captions. Here we
present a system that uses the full caption, and Relations in Captions
(REC-COCO), a dataset derived from MS-COCO which allows to evaluate spatial
relation inference from captions directly. Our experiments show that: (1) it is
possible to infer the size and location of an object with respect to a given
subject directly from the caption; (2) the use of full text allows to place the
object better than using a manually annotated relation. Our work paves the way
for systems that, given a caption, decide which entities need to be depicted
and their respective location and sizes, in order to then generate the final
image.
| [
{
"version": "v1",
"created": "Mon, 1 Feb 2021 17:21:13 GMT"
}
] | 1,612,310,400,000 | [
[
"Elu",
"Aitzol",
""
],
[
"Azkune",
"Gorka",
""
],
[
"de Lacalle",
"Oier Lopez",
""
],
[
"Arganda-Carreras",
"Ignacio",
""
],
[
"Soroa",
"Aitor",
""
],
[
"Agirre",
"Eneko",
""
]
] |
2102.01190 | Weihua Li | Xing Su, Yan Kong, Weihua Li | The 4th International Workshop on Smart Simulation and Modelling for
Complex Systems | IJCAI2019 workshop | null | null | SSMCS2019 | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Computer-based modelling and simulation have become useful tools to
facilitate humans to understand systems in different domains, such as physics,
astrophysics, chemistry, biology, economics, engineering and social science. A
complex system is featured with a large number of interacting components
(agents, processes, etc.), whose aggregate activities are nonlinear and
self-organized. Complex systems are hard to be simulated or modelled by using
traditional computational approaches due to complex relationships among system
components, distributed features of resources, and dynamics of environments.
Meanwhile, smart systems such as multi-agent systems have demonstrated
advantages and great potentials in modelling and simulating complex systems.
| [
{
"version": "v1",
"created": "Mon, 1 Feb 2021 21:40:28 GMT"
}
] | 1,612,310,400,000 | [
[
"Su",
"Xing",
""
],
[
"Kong",
"Yan",
""
],
[
"Li",
"Weihua",
""
]
] |
2102.01538 | Yuanpeng He | Yuanpeng He, Lijian Li, Tianxiang Zhan | A Matrix-based Distance of Pythagorean Fuzzy Set and its Application in
Medical Diagnosis | 31 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The pythagorean fuzzy set (PFS) which is developed based on intuitionistic
fuzzy set, is more efficient in elaborating and disposing uncertainties in
indeterminate situations, which is a very reason of that PFS is applied in
various kinds of fields. How to measure the distance between two pythagorean
fuzzy sets is still an open issue. Mnay kinds of methods have been proposed to
present the of the question in former reaserches. However, not all of existing
methods can accurately manifest differences among pythagorean fuzzy sets and
satisfy the property of similarity. And some other kinds of methods neglect the
relationship among three variables of pythagorean fuzzy set. To addrees the
proplem, a new method of measuring distance is proposed which meets the
requirements of axiom of distance measurement and is able to indicate the
degree of distinction of PFSs well. Then some numerical examples are offered to
to verify that the method of measuring distances can avoid the situation that
some counter? intuitive and irrational results are produced and is more
effective, reasonable and advanced than other similar methods. Besides, the
proposed method of measuring distances between PFSs is applied in a real
environment of application which is the medical diagnosis and is compared with
other previous methods to demonstrate its superiority and efficiency. And the
feasibility of the proposed method in handling uncertainties in practice is
also proved at the same time.
| [
{
"version": "v1",
"created": "Sun, 31 Jan 2021 15:59:09 GMT"
},
{
"version": "v2",
"created": "Thu, 23 May 2024 12:59:12 GMT"
}
] | 1,716,508,800,000 | [
[
"He",
"Yuanpeng",
""
],
[
"Li",
"Lijian",
""
],
[
"Zhan",
"Tianxiang",
""
]
] |
2102.02009 | Tanvir Alam | Tanvir Alam, Jens Schneider | Social Network Analysis of Hadith Narrators from Sahih Bukhari | Social Network Analysis of Hadith Narrators from Sahih Bukhari | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The ahadith, prophetic traditions for the Muslims around the world, are
narrations originating from the sayings and the deeds of Prophet Muhammad
(pbuh). They are considered one of the fundamental sources of Islamic
legislation along with the Quran. The list of persons involved in the narration
of each hadith is carefully scrutinized by scholars studying the hadith, with
respect to their reputation and authenticity of the hadith. This is due to the
its legislative importance in Islamic principles. There were many narrators who
contributed to this responsibility of preserving prophetic narrations over the
centuries. But to date, no systematic and comprehensive study, based on the
social network, has been adapted to understand the contribution of early hadith
narrators and the propagation of hadith across generations. In this study, we
represented the chain of narrators of the hadith collection from Sahih Bukhari
as a social graph. Based on social network analysis (SNA) on this graph, we
found that the network of narrators is a scale-free network. We identified a
list of influential narrators from the companions as well as the narrators from
the second and third-generation who contribute significantly in the propagation
of hadith collected in Sahih Bukhari. We discovered sixteen communities from
the narrators of Sahih Bukhari. In each of these communities, there are other
narrators who contributed significantly to the propagation of prophetic
narrations. We also found that most narrators were centered in Makkah and
Madinah in the era of companions and, then, gradually the center of hadith
narrators shifted towards Kufa, Baghdad and central Asia over a period of time.
To the best of our knowledge, this the first comprehensive and systematic study
based on SNA, representing the narrators as a social graph to analyze their
contribution to the preservation and propagation of hadith.
| [
{
"version": "v1",
"created": "Wed, 3 Feb 2021 11:24:32 GMT"
}
] | 1,612,396,800,000 | [
[
"Alam",
"Tanvir",
""
],
[
"Schneider",
"Jens",
""
]
] |
2102.02134 | Farouq Zitouni | Farouq Zitouni, Saad Harous, Abdelghani Belkeram, Lokman Elhakim Baba
Hammou | The Archerfish Hunting Optimizer: a novel metaheuristic algorithm for
global optimization | 41 pages, 14 figures, 41 pages, 132 references, 30 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Global optimization solves real-world problems numerically or analytically by
minimizing their objective functions. Most of the analytical algorithms are
greedy and computationally intractable. Metaheuristics are nature-inspired
optimization algorithms. They numerically find a near-optimal solution for
optimization problems in a reasonable amount of time. We propose a novel
metaheuristic algorithm for global optimization. It is based on the shooting
and jumping behaviors of the archerfish for hunting aerial insects. We name it
the Archerfish Hunting Optimizer (AHO). We Perform two sorts of comparisons to
validate the proposed algorithm's performance. First, AHO is compared to the 12
recent metaheuristic algorithms (the accepted algorithms for the 2020's
competition on single objective bound-constrained numerical optimization) on
ten test functions of the benchmark CEC 2020 for unconstrained optimization.
Second, the performance of AHO and 3 recent metaheuristic algorithms, is
evaluated using five engineering design problems taken from the benchmark CEC
2020 for non-convex constrained optimization. The experimental results are
evaluated using the Wilcoxon signed-rank and the Friedman tests. The
statistical indicators illustrate that the Archerfish Hunting Optimizer has an
excellent ability to accomplish higher performance in competition with the
well-established optimizers.
| [
{
"version": "v1",
"created": "Wed, 3 Feb 2021 16:22:31 GMT"
}
] | 1,612,396,800,000 | [
[
"Zitouni",
"Farouq",
""
],
[
"Harous",
"Saad",
""
],
[
"Belkeram",
"Abdelghani",
""
],
[
"Hammou",
"Lokman Elhakim Baba",
""
]
] |
2102.02311 | Sander Beckers | Sander Beckers | Causal Sufficiency and Actual Causation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pearl opened the door to formally defining actual causation using causal
models. His approach rests on two strategies: first, capturing the widespread
intuition that X=x causes Y=y iff X=x is a Necessary Element of a Sufficient
Set for Y=y, and second, showing that his definition gives intuitive answers on
a wide set of problem cases. This inspired dozens of variations of his
definition of actual causation, the most prominent of which are due to Halpern
& Pearl. Yet all of them ignore Pearl's first strategy, and the second strategy
taken by itself is unable to deliver a consensus. This paper offers a way out
by going back to the first strategy: it offers six formal definitions of causal
sufficiency and two interpretations of necessity. Combining the two gives
twelve new definitions of actual causation. Several interesting results about
these definitions and their relation to the various Halpern & Pearl definitions
are presented. Afterwards the second strategy is evaluated as well. In order to
maximize neutrality, the paper relies mostly on the examples and intuitions of
Halpern & Pearl. One definition comes out as being superior to all others, and
is therefore suggested as a new definition of actual causation.
| [
{
"version": "v1",
"created": "Wed, 3 Feb 2021 22:12:49 GMT"
}
] | 1,612,483,200,000 | [
[
"Beckers",
"Sander",
""
]
] |
2102.02785 | Sirin Botan | Sirin Botan and Ronald de Haan and Marija Slavkovik and Zoi
Terzopoulou | Egalitarian Judgment Aggregation | Extended version of paper in proceedings of the 20th International
Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Egalitarian considerations play a central role in many areas of social choice
theory. Applications of egalitarian principles range from ensuring everyone
gets an equal share of a cake when deciding how to divide it, to guaranteeing
balance with respect to gender or ethnicity in committee elections. Yet, the
egalitarian approach has received little attention in judgment aggregation -- a
powerful framework for aggregating logically interconnected issues. We make the
first steps towards filling that gap. We introduce axioms capturing two
classical interpretations of egalitarianism in judgment aggregation and situate
these within the context of existing axioms in the pertinent framework of
belief merging. We then explore the relationship between these axioms and
several notions of strategyproofness from social choice theory at large.
Finally, a novel egalitarian judgment aggregation rule stems from our analysis;
we present complexity results concerning both outcome determination and
strategic manipulation for that rule.
| [
{
"version": "v1",
"created": "Thu, 4 Feb 2021 18:07:31 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Mar 2021 13:23:01 GMT"
}
] | 1,615,334,400,000 | [
[
"Botan",
"Sirin",
""
],
[
"de Haan",
"Ronald",
""
],
[
"Slavkovik",
"Marija",
""
],
[
"Terzopoulou",
"Zoi",
""
]
] |
2102.02864 | Jing Gu | Jing Gu, Mostafa Mirshekari, Zhou Yu, Aaron Sisto | ChainCQG: Flow-Aware Conversational Question Generation | EACL 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conversational systems enable numerous valuable applications, and
question-answering is an important component underlying many of these. However,
conversational question-answering remains challenging due to the lack of
realistic, domain-specific training data. Inspired by this bottleneck, we focus
on conversational question generation as a means to generate synthetic
conversations for training and evaluation purposes. We present a number of
novel strategies to improve conversational flow and accommodate varying
question types and overall fluidity. Specifically, we design ChainCQG as a
two-stage architecture that learns question-answer representations across
multiple dialogue turns using a flow propagation training strategy.ChainCQG
significantly outperforms both answer-aware and answer-unaware SOTA baselines
(e.g., up to 48% BLEU-1 improvement). Additionally, our model is able to
generate different types of questions, with improved fluidity and coreference
alignment.
| [
{
"version": "v1",
"created": "Thu, 4 Feb 2021 19:56:51 GMT"
}
] | 1,612,742,400,000 | [
[
"Gu",
"Jing",
""
],
[
"Mirshekari",
"Mostafa",
""
],
[
"Yu",
"Zhou",
""
],
[
"Sisto",
"Aaron",
""
]
] |
2102.03002 | Yiwei Bai | Yiwei Bai, Wenting Zhao, Carla P. Gomes | Zero Training Overhead Portfolios for Learning to Solve Combinatorial
Problems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | There has been an increasing interest in harnessing deep learning to tackle
combinatorial optimization (CO) problems in recent years. Typical CO deep
learning approaches leverage the problem structure in the model architecture.
Nevertheless, the model selection is still mainly based on the conventional
machine learning setting. Due to the discrete nature of CO problems, a single
model is unlikely to learn the problem entirely. We introduce ZTop, which
stands for Zero Training Overhead Portfolio, a simple yet effective model
selection and ensemble mechanism for learning to solve combinatorial problems.
ZTop is inspired by algorithm portfolios, a popular CO ensembling strategy,
particularly restart portfolios, which periodically restart a randomized CO
algorithm, de facto exploring the search space with different heuristics. We
have observed that well-trained models acquired in the same training
trajectory, with similar top validation performance, perform well on very
different validation instances. Following this observation, ZTop ensembles a
set of well-trained models, each providing a unique heuristic with zero
training overhead, and applies them, sequentially or in parallel, to solve the
test instances. We show how ZTopping, i.e., using a ZTop ensemble strategy with
a given deep learning approach, can significantly improve the performance of
the current state-of-the-art deep learning approaches on three prototypical CO
domains, the hardest unique-solution Sudoku instances, challenging routing
problems, and the graph maximum cut problem, as well as on multi-label
classification, a machine learning task with a large combinatorial label space.
| [
{
"version": "v1",
"created": "Fri, 5 Feb 2021 05:23:26 GMT"
}
] | 1,612,742,400,000 | [
[
"Bai",
"Yiwei",
""
],
[
"Zhao",
"Wenting",
""
],
[
"Gomes",
"Carla P.",
""
]
] |
2102.03053 | Julian Bernhard | Julian Bernhard and Alois Knoll | Risk-Constrained Interactive Safety under Behavior Uncertainty for
Autonomous Driving | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Balancing safety and efficiency when planning in dense traffic is
challenging. Interactive behavior planners incorporate prediction uncertainty
and interactivity inherent to these traffic situations. Yet, their use of
single-objective optimality impedes interpretability of the resulting safety
goal. Safety envelopes which restrict the allowed planning region yield
interpretable safety under the presence of behavior uncertainty, yet, they
sacrifice efficiency in dense traffic due to conservative driving. Studies show
that humans balance safety and efficiency in dense traffic by accepting a
probabilistic risk of violating the safety envelope. In this work, we adopt
this safety objective for interactive planning. Specifically, we formalize this
safety objective, present the Risk-Constrained Robust Stochastic Bayesian Game
modeling interactive decisions satisfying a maximum risk of violating a safety
envelope under uncertainty of other traffic participants' behavior and solve it
using our variant of Multi-Agent Monte Carlo Tree Search. We demonstrate in
simulation that our approach outperforms baselines approaches, and by reaching
the specified violation risk level over driven simulation time, provides an
interpretable and tunable safety objective for interactive planning.
| [
{
"version": "v1",
"created": "Fri, 5 Feb 2021 08:33:39 GMT"
}
] | 1,612,742,400,000 | [
[
"Bernhard",
"Julian",
""
],
[
"Knoll",
"Alois",
""
]
] |
2102.03064 | Yotam Amitai | Yotam Amitai and Ofra Amir | "I Don't Think So": Summarizing Policy Disagreements for Agent
Comparison | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | With Artificial Intelligence on the rise, human interaction with autonomous
agents becomes more frequent. Effective human-agent collaboration requires
users to understand the agent's behavior, as failing to do so may cause reduced
productivity, misuse or frustration. Agent strategy summarization methods are
used to describe the strategy of an agent to its destined user through
demonstration. A summary's objective is to maximize the user's understanding of
the agent's aptitude by showcasing its behaviour in a selected set of world
states. While shown to be useful, we show that current methods are limited when
tasked with comparing between agents, as each summary is independently
generated for a specific agent. In this paper, we propose a novel method for
generating dependent and contrastive summaries that emphasize the differences
between agent policies by identifying states in which the agents disagree on
the best course of action. We conduct user studies to assess the usefulness of
disagreement-based summaries for identifying superior agents and conveying
agent differences. Results show disagreement-based summaries lead to improved
user performance compared to summaries generated using HIGHLIGHTS, a strategy
summarization algorithm which generates summaries for each agent independently.
| [
{
"version": "v1",
"created": "Fri, 5 Feb 2021 09:09:00 GMT"
},
{
"version": "v2",
"created": "Thu, 2 Dec 2021 13:51:45 GMT"
}
] | 1,638,489,600,000 | [
[
"Amitai",
"Yotam",
""
],
[
"Amir",
"Ofra",
""
]
] |
2102.03119 | Julian Bernhard | Julian Bernhard, Stefan Pollok and Alois Knoll | Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for
Automated Driving using Distributional Reinforcement Learning | Published at IEEE IV 2019 | null | 10.1109/IVS.2019.8813791 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | For highly automated driving above SAE level~3, behavior generation
algorithms must reliably consider the inherent uncertainties of the traffic
environment, e.g. arising from the variety of human driving styles. Such
uncertainties can generate ambiguous decisions, requiring the algorithm to
appropriately balance low-probability hazardous events, e.g. collisions, and
high-probability beneficial events, e.g. quickly crossing the intersection.
State-of-the-art behavior generation algorithms lack a distributional treatment
of decision outcome. This impedes a proper risk evaluation in ambiguous
situations, often encouraging either unsafe or conservative behavior. Thus, we
propose a two-step approach for risk-sensitive behavior generation combining
offline distribution learning with online risk assessment. Specifically, we
first learn an optimal policy in an uncertain environment with Deep
Distributional Reinforcement Learning. During execution, the optimal
risk-sensitive action is selected by applying established risk criteria, such
as the Conditional Value at Risk, to the learned state-action return
distributions. In intersection crossing scenarios, we evaluate different risk
criteria and demonstrate that our approach increases safety, while maintaining
an active driving style. Our approach shall encourage further studies about the
benefits of risk-sensitive approaches for self-driving vehicles.
| [
{
"version": "v1",
"created": "Fri, 5 Feb 2021 11:45:12 GMT"
}
] | 1,612,742,400,000 | [
[
"Bernhard",
"Julian",
""
],
[
"Pollok",
"Stefan",
""
],
[
"Knoll",
"Alois",
""
]
] |
2102.03467 | Tristan Cazenave | Tristan Cazenave | Improving Model and Search for Computer Go | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The standard for Deep Reinforcement Learning in games, following Alpha Zero,
is to use residual networks and to increase the depth of the network to get
better results. We propose to improve mobile networks as an alternative to
residual networks and experimentally show the playing strength of the networks
according to both their width and their depth. We also propose a generalization
of the PUCT search algorithm that improves on PUCT.
| [
{
"version": "v1",
"created": "Sat, 6 Feb 2021 01:20:17 GMT"
},
{
"version": "v2",
"created": "Fri, 9 Apr 2021 10:50:20 GMT"
}
] | 1,618,185,600,000 | [
[
"Cazenave",
"Tristan",
""
]
] |
2102.03529 | Martin Suda | Martin Suda | Vampire With a Brain Is a Good ITP Hammer | 14.5 pages excluding references | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vampire has been for a long time the strongest first-order automatic theorem
prover, widely used for hammer-style proof automation in ITPs such as Mizar,
Isabelle, HOL, and Coq. In this work, we considerably improve the performance
of Vampire in hammering over the full Mizar library by enhancing its saturation
procedure with efficient neural guidance. In particular, we employ a recently
proposed recursive neural network classifying the generated clauses based only
on their derivation history. Compared to previous neural methods based on
considering the logical content of the clauses, our architecture makes
evaluating a single clause much less time consuming. The resulting system shows
good learning capability and improves on the state-of-the-art performance on
the Mizar library, while proving many theorems that the related ENIGMA system
could not prove in a similar hammering evaluation.
| [
{
"version": "v1",
"created": "Sat, 6 Feb 2021 07:24:53 GMT"
},
{
"version": "v2",
"created": "Tue, 11 May 2021 15:52:19 GMT"
}
] | 1,620,777,600,000 | [
[
"Suda",
"Martin",
""
]
] |
2102.03555 | Davide Andrea Guastella | Davide Andrea Guastella | Scheduling Plans of Tasks | Internship done at LIP6 in 2017 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present a heuristic algorithm for solving the problem of scheduling plans
of tasks. The plans are ordered vectors of tasks, and tasks are basic
operations carried out by resources. Plans are tied by temporal, precedence and
resource constraints that makes the scheduling problem hard to solve in
polynomial time. The proposed heuristic, that has a polynomial worst-case time
complexity, searches for a feasible schedule that maximize the number of plans
scheduled, along a fixed time window, with respect to temporal, precedence and
resource constraints.
| [
{
"version": "v1",
"created": "Sat, 6 Feb 2021 10:14:54 GMT"
}
] | 1,612,828,800,000 | [
[
"Guastella",
"Davide Andrea",
""
]
] |
2102.03896 | Simon Zhuang | Simon Zhuang, Dylan Hadfield-Menell | Consequences of Misaligned AI | null | NeurIPS 2020 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | AI systems often rely on two key components: a specified goal or reward
function and an optimization algorithm to compute the optimal behavior for that
goal. This approach is intended to provide value for a principal: the user on
whose behalf the agent acts. The objectives given to these agents often refer
to a partial specification of the principal's goals. We consider the cost of
this incompleteness by analyzing a model of a principal and an agent in a
resource constrained world where the $L$ attributes of the state correspond to
different sources of utility for the principal. We assume that the reward
function given to the agent only has support on $J < L$ attributes. The
contributions of our paper are as follows: 1) we propose a novel model of an
incomplete principal-agent problem from artificial intelligence; 2) we provide
necessary and sufficient conditions under which indefinitely optimizing for any
incomplete proxy objective leads to arbitrarily low overall utility; and 3) we
show how modifying the setup to allow reward functions that reference the full
state or allowing the principal to update the proxy objective over time can
lead to higher utility solutions. The results in this paper argue that we
should view the design of reward functions as an interactive and dynamic
process and identifies a theoretical scenario where some degree of
interactivity is desirable.
| [
{
"version": "v1",
"created": "Sun, 7 Feb 2021 19:34:04 GMT"
}
] | 1,612,828,800,000 | [
[
"Zhuang",
"Simon",
""
],
[
"Hadfield-Menell",
"Dylan",
""
]
] |
2102.03919 | Scott Cheng-Hsin Yang | Scott Cheng-Hsin Yang, Wai Keen Vong, Ravi B. Sojitra, Tomas Folke,
Patrick Shafto | Mitigating belief projection in explainable artificial intelligence via
Bayesian Teaching | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | State-of-the-art deep-learning systems use decision rules that are
challenging for humans to model. Explainable AI (XAI) attempts to improve human
understanding but rarely accounts for how people typically reason about
unfamiliar agents. We propose explicitly modeling the human explainee via
Bayesian Teaching, which evaluates explanations by how much they shift
explainees' inferences toward a desired goal. We assess Bayesian Teaching in a
binary image classification task across a variety of contexts. Absent
intervention, participants predict that the AI's classifications will match
their own, but explanations generated by Bayesian Teaching improve their
ability to predict the AI's judgements by moving them away from this prior
belief. Bayesian Teaching further allows each case to be broken down into
sub-examples (here saliency maps). These sub-examples complement whole examples
by improving error detection for familiar categories, whereas whole examples
help predict correct AI judgements of unfamiliar cases.
| [
{
"version": "v1",
"created": "Sun, 7 Feb 2021 21:23:24 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Apr 2021 15:05:32 GMT"
}
] | 1,619,481,600,000 | [
[
"Yang",
"Scott Cheng-Hsin",
""
],
[
"Vong",
"Wai Keen",
""
],
[
"Sojitra",
"Ravi B.",
""
],
[
"Folke",
"Tomas",
""
],
[
"Shafto",
"Patrick",
""
]
] |
2102.04225 | Yuanpeng Li | Yuanpeng Li | Concepts, Properties and an Approach for Compositional Generalization | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Compositional generalization is the capacity to recognize and imagine a large
amount of novel combinations from known components. It is a key in human
intelligence, but current neural networks generally lack such ability. This
report connects a series of our work for compositional generalization, and
summarizes an approach. The first part contains concepts and properties. The
second part looks into a machine learning approach. The approach uses
architecture design and regularization to regulate information of
representations. This report focuses on basic ideas with intuitive and
illustrative explanations. We hope this work would be helpful to clarify
fundamentals of compositional generalization and lead to advance artificial
intelligence.
| [
{
"version": "v1",
"created": "Mon, 8 Feb 2021 14:22:30 GMT"
}
] | 1,612,828,800,000 | [
[
"Li",
"Yuanpeng",
""
]
] |
2102.04972 | Shane Mueller | Shane T. Mueller, Elizabeth S. Veinott, Robert R. Hoffman, Gary Klein,
Lamia Alam, Tauseef Mamun, and William J. Clancey | Principles of Explanation in Human-AI Systems | AAAI-2021, Explainable Agency in Artificial Intelligence WS, AAAI,
Feb, 2021, Virtual Conference, United States | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explainable Artificial Intelligence (XAI) has re-emerged in response to the
development of modern AI and ML systems. These systems are complex and
sometimes biased, but they nevertheless make decisions that impact our lives.
XAI systems are frequently algorithm-focused; starting and ending with an
algorithm that implements a basic untested idea about explainability. These
systems are often not tested to determine whether the algorithm helps users
accomplish any goals, and so their explainability remains unproven. We propose
an alternative: to start with human-focused principles for the design, testing,
and implementation of XAI systems, and implement algorithms to serve that
purpose. In this paper, we review some of the basic concepts that have been
used for user-centered XAI systems over the past 40 years of research. Based on
these, we describe the "Self-Explanation Scorecard", which can help developers
understand how they can empower users by enabling self-explanation. Finally, we
present a set of empirically-grounded, user-centered design principles that may
guide developers to create successful explainable systems.
| [
{
"version": "v1",
"created": "Tue, 9 Feb 2021 17:43:45 GMT"
}
] | 1,612,915,200,000 | [
[
"Mueller",
"Shane T.",
""
],
[
"Veinott",
"Elizabeth S.",
""
],
[
"Hoffman",
"Robert R.",
""
],
[
"Klein",
"Gary",
""
],
[
"Alam",
"Lamia",
""
],
[
"Mamun",
"Tauseef",
""
],
[
"Clancey",
"William J.",
""
]
] |
2102.05147 | Kolawole Ogunsina | Kolawole Ogunsina, Marios Papamichalis, Daniel DeLaurentis | Relational Dynamic Bayesian Network Modeling for Uncertainty
Quantification and Propagation in Airline Disruption Management | Published in Elsevier Journal of Engineering Applications of
Artificial Intelligence | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Disruption management during the airline scheduling process can be
compartmentalized into proactive and reactive processes depending upon the time
of schedule execution. The state of the art for decision-making in airline
disruption management involves a heuristic human-centric approach that does not
categorically study uncertainty in proactive and reactive processes for
managing airline schedule disruptions. Hence, this paper introduces an
uncertainty transfer function model (UTFM) framework that characterizes
uncertainty for proactive airline disruption management before schedule
execution, reactive airline disruption management during schedule execution,
and proactive airline disruption management after schedule execution to enable
the construction of quantitative tools that can allow an intelligent agent to
rationalize complex interactions and procedures for robust airline disruption
management. Specifically, we use historical scheduling and operations data from
a major U.S. airline to facilitate the development and assessment of the UTFM,
defined by hidden Markov models (a special class of probabilistic graphical
models) that can efficiently perform pattern learning and inference on portions
of large data sets. We employ the UTFM to assess two independent and separately
disrupted flight legs from the airline route network. Assessment of a flight
leg from Dallas to Houston, disrupted by air traffic control hold for bad
weather at Dallas, revealed that proactive disruption management for turnaround
in Dallas before schedule execution is impractical because of zero transition
probability between turnaround and taxi-out.
| [
{
"version": "v1",
"created": "Tue, 9 Feb 2021 21:57:04 GMT"
},
{
"version": "v2",
"created": "Mon, 3 May 2021 13:35:51 GMT"
},
{
"version": "v3",
"created": "Wed, 23 Mar 2022 17:23:30 GMT"
}
] | 1,648,080,000,000 | [
[
"Ogunsina",
"Kolawole",
""
],
[
"Papamichalis",
"Marios",
""
],
[
"DeLaurentis",
"Daniel",
""
]
] |
2102.06112 | Hugo Latapie | Hugo Latapie, Ozkan Kilic, Gaowen Liu, Yan Yan, Ramana Kompella, Pei
Wang, Kristinn R. Thorisson, Adam Lawrence, Yuhong Sun, Jayanth Srinivasa | A Metamodel and Framework for Artificial General Intelligence From
Theory to Practice | arXiv admin note: text overlap with arXiv:2008.12879 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a new metamodel-based knowledge representation that
significantly improves autonomous learning and adaptation. While interest in
hybrid machine learning / symbolic AI systems leveraging, for example,
reasoning and knowledge graphs, is gaining popularity, we find there remains a
need for both a clear definition of knowledge and a metamodel to guide the
creation and manipulation of knowledge. Some of the benefits of the metamodel
we introduce in this paper include a solution to the symbol grounding problem,
cumulative learning, and federated learning. We have applied the metamodel to
problems ranging from time series analysis, computer vision, and natural
language understanding and have found that the metamodel enables a wide variety
of learning mechanisms ranging from machine learning, to graph network analysis
and learning by reasoning engines to interoperate in a highly synergistic way.
Our metamodel-based projects have consistently exhibited unprecedented
accuracy, performance, and ability to generalize. This paper is inspired by the
state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular
computing community, as well as Alfred Korzybski's general semantics. One
surprising consequence of the metamodel is that it not only enables a new level
of autonomous learning and optimal functioning for machine intelligences, but
may also shed light on a path to better understanding how to improve human
cognition.
| [
{
"version": "v1",
"created": "Thu, 11 Feb 2021 16:45:58 GMT"
}
] | 1,613,088,000,000 | [
[
"Latapie",
"Hugo",
""
],
[
"Kilic",
"Ozkan",
""
],
[
"Liu",
"Gaowen",
""
],
[
"Yan",
"Yan",
""
],
[
"Kompella",
"Ramana",
""
],
[
"Wang",
"Pei",
""
],
[
"Thorisson",
"Kristinn R.",
""
],
[
"Lawrence",
"Adam",
""
],
[
"Sun",
"Yuhong",
""
],
[
"Srinivasa",
"Jayanth",
""
]
] |
2102.06145 | Marina Speranskaya | Marina Speranskaya, Martin Schmitt, Benjamin Roth | Ranking vs. Classifying: Measuring Knowledge Base Completion Quality | AKBC 2020 accepted paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge base completion (KBC) methods aim at inferring missing facts from
the information present in a knowledge base (KB) by estimating the likelihood
of candidate facts. In the prevailing evaluation paradigm, models do not
actually decide whether a new fact should be accepted or not but are solely
judged on the position of true facts in a likelihood ranking with other
candidates. We argue that consideration of binary predictions is essential to
reflect the actual KBC quality, and propose a novel evaluation paradigm,
designed to provide more transparent model selection criteria for a realistic
scenario. We construct the data set FB14k-QAQ where instead of single facts, we
use KB queries, i.e., facts where one entity is replaced with a variable, and
construct corresponding sets of entities that are correct answers. We randomly
remove some of these correct answers from the data set, simulating the
realistic scenario of real-world entities missing from a KB. This way, we can
explicitly measure a model's ability to handle queries that have more correct
answers in the real world than in the KB, including the special case of queries
without any valid answer. The latter especially contrasts the ranking setting.
We evaluate a number of state-of-the-art KB embeddings models on our new
benchmark. The differences in relative performance between ranking-based and
classification-based evaluation that we observe in our experiments confirm our
hypothesis that good performance on the ranking task does not necessarily
translate to good performance on the actual completion task. Our results
motivate future work on KB embedding models with better prediction separability
and, as a first step in that direction, we propose a simple variant of TransE
that encourages thresholding and achieves a significant improvement in
classification F1 score relative to the original TransE.
| [
{
"version": "v1",
"created": "Tue, 2 Feb 2021 17:53:48 GMT"
}
] | 1,613,088,000,000 | [
[
"Speranskaya",
"Marina",
""
],
[
"Schmitt",
"Martin",
""
],
[
"Roth",
"Benjamin",
""
]
] |
2102.06943 | Aymen Ben Said | Mikhail Shchukin, Aymen Ben Said, Andre Lobo Teixeira | Goods Transportation Problem Solving via Routing Algorithm | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper outlines the ideas behind developing a graph-based
heuristic-driven routing algorithm designed for a particular instance of a
goods transportation problem with a single good type. The proposed algorithm
solves the optimization problem of satisfying the demand of goods on a given
undirected transportation graph with minimizing the estimated cost for each
traversed segment of the delivery path. The operation of the routing algorithm
is discussed and overall evaluation of the proposed problem solving technique
is given.
| [
{
"version": "v1",
"created": "Sat, 13 Feb 2021 15:23:47 GMT"
}
] | 1,613,433,600,000 | [
[
"Shchukin",
"Mikhail",
""
],
[
"Said",
"Aymen Ben",
""
],
[
"Teixeira",
"Andre Lobo",
""
]
] |
2102.07120 | Yilun Zhou | Ganesh Ghalme, Vineet Nair, Vishakha Patil, Yilun Zhou | Long-Term Resource Allocation Fairness in Average Markov Decision
Process (AMDP) Environment | AAMAS 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Fairness has emerged as an important concern in automated decision-making in
recent years, especially when these decisions affect human welfare. In this
work, we study fairness in temporally extended decision-making settings,
specifically those formulated as Markov Decision Processes (MDPs). Our proposed
notion of fairness ensures that each state's long-term visitation frequency is
at least a specified fraction. This quota-based notion of fairness is natural
in many resource-allocation settings where the dynamics of a single resource
being allocated is governed by an MDP and the distribution of the shared
resource is captured by its state-visitation frequency. In an average-reward
MDP (AMDP) setting, we formulate the problem as a bilinear saddle point program
and, for a generative model, solve it using a Stochastic Mirror Descent (SMD)
based algorithm. The proposed solution guarantees a simultaneous approximation
on the expected average-reward and fairness requirement. We give sample
complexity bounds for the proposed algorithm and validate our theoretical
results with experiments on simulated data.
| [
{
"version": "v1",
"created": "Sun, 14 Feb 2021 10:20:53 GMT"
},
{
"version": "v2",
"created": "Tue, 2 Mar 2021 12:45:15 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Feb 2022 22:51:49 GMT"
}
] | 1,644,451,200,000 | [
[
"Ghalme",
"Ganesh",
""
],
[
"Nair",
"Vineet",
""
],
[
"Patil",
"Vishakha",
""
],
[
"Zhou",
"Yilun",
""
]
] |
2102.07213 | Evandro Ruiz Dr. | Cristina Godoy Bernardo de Oliveira and Evandro Eduardo Seron Ruiz | Why Talking about ethics is not enough: a proposal for Fintech's AI
ethics | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | As the potential applications of Artificial Intelligence (AI) in the
financial sector increases, ethical issues become gradually latent. The
distrust of individuals, social groups, and governments about the risks arising
from Fintech's activities is growing. Due to this scenario, the preparation of
recommendations and Ethics Guidelines is increasing and the risks of being
chosen the principles and ethical values most appropriate to companies are
high. Thus, this exploratory research aims to analyze the benefits of the
application of the stakeholder theory and the idea of Social License to build
an environment of trust and for the realization of ethical principles by
Fintech. The formation of a Fintech association for the creation of a Social
License will allow early-stage Fintech to participate from the beginning of its
activities in the elaboration of a dynamic ethical code and with the
participation of stakeholders.
| [
{
"version": "v1",
"created": "Sun, 14 Feb 2021 18:23:42 GMT"
}
] | 1,613,433,600,000 | [
[
"de Oliveira",
"Cristina Godoy Bernardo",
""
],
[
"Ruiz",
"Evandro Eduardo Seron",
""
]
] |
2102.07246 | Xuejiao Tang | Ruijun Chen, Jiong Qiu and Xuejiao Tang | Responsibility Management through Responsibility Networks | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | The safety management is critically important in the workplace.
Unfortunately, responsibility issues therein such as inefficient supervision,
poor evaluation and inadequate perception have not been properly addressed. To
this end, in this paper, we deploy the Internet of Responsibilities (IoR) for
responsibility management. Through the building of IoR framework, hierarchical
responsibility management, automated responsibility evaluation at all level and
efficient responsibility perception are achieved. The practical deployment of
IoR system showed its effective responsibility management capability in various
workplaces.
| [
{
"version": "v1",
"created": "Sun, 14 Feb 2021 21:06:33 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Feb 2021 01:21:18 GMT"
},
{
"version": "v3",
"created": "Thu, 7 Dec 2023 22:35:50 GMT"
}
] | 1,702,252,800,000 | [
[
"Chen",
"Ruijun",
""
],
[
"Qiu",
"Jiong",
""
],
[
"Tang",
"Xuejiao",
""
]
] |
2102.07333 | Susannah Kate Devitt | Angela Daly, S Kate Devitt, Monique Mann | AI Ethics Needs Good Data | 20 pages, under peer review in Pieter Verdegem (ed), AI for Everyone?
Critical Perspectives. University of Westminster Press | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | In this chapter we argue that discourses on AI must transcend the language of
'ethics' and engage with power and political economy in order to constitute
'Good Data'. In particular, we must move beyond the depoliticised language of
'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good'
given the limitations of ethics as a frame through which AI issues can be
viewed. In order to circumvent these limits, we use instead the language and
conceptualisation of 'Good Data', as a more expansive term to elucidate the
values, rights and interests at stake when it comes to AI's development and
deployment, as well as that of other digital technologies. Good Data
considerations move beyond recurring themes of data protection/privacy and the
FAT (fairness, transparency and accountability) movement to include explicit
political economy critiques of power. Instead of yet more ethics principles
(that tend to say the same or similar things anyway), we offer four 'pillars'
on which Good Data AI can be built: community, rights, usability and politics.
Overall we view AI's 'goodness' as an explicly political (economy) question of
power and one which is always related to the degree which AI is created and
used to increase the wellbeing of society and especially to increase the power
of the most marginalized and disenfranchised. We offer recommendations and
remedies towards implementing 'better' approaches towards AI. Our strategies
enable a different (but complementary) kind of evaluation of AI as part of the
broader socio-technical systems in which AI is built and deployed.
| [
{
"version": "v1",
"created": "Mon, 15 Feb 2021 04:16:27 GMT"
}
] | 1,613,433,600,000 | [
[
"Daly",
"Angela",
""
],
[
"Devitt",
"S Kate",
""
],
[
"Mann",
"Monique",
""
]
] |
2102.07339 | Yuxia Geng | Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z. Pan, Zhiquan Ye, Zonggang
Yuan, Yantao Jia, Huajun Chen | OntoZSL: Ontology-enhanced Zero-shot Learning | Accepted to The Web Conference (WWW) 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Zero-shot Learning (ZSL), which aims to predict for those classes that have
never appeared in the training data, has arisen hot research interests. The key
of implementing ZSL is to leverage the prior knowledge of classes which builds
the semantic relationship between classes and enables the transfer of the
learned models (e.g., features) from training classes (i.e., seen classes) to
unseen classes. However, the priors adopted by the existing methods are
relatively limited with incomplete semantics. In this paper, we explore richer
and more competitive prior knowledge to model the inter-class relationship for
ZSL via ontology-based knowledge representation and semantic embedding.
Meanwhile, to address the data imbalance between seen classes and unseen
classes, we developed a generative ZSL framework with Generative Adversarial
Networks (GANs). Our main findings include: (i) an ontology-enhanced ZSL
framework that can be applied to different domains, such as image
classification (IMGC) and knowledge graph completion (KGC); (ii) a
comprehensive evaluation with multiple zero-shot datasets from different
domains, where our method often achieves better performance than the
state-of-the-art models. In particular, on four representative ZSL baselines of
IMGC, the ontology-based class semantics outperform the previous priors e.g.,
the word embeddings of classes by an average of 12.4 accuracy points in the
standard ZSL across two example datasets (see Figure 4).
| [
{
"version": "v1",
"created": "Mon, 15 Feb 2021 04:39:58 GMT"
}
] | 1,613,433,600,000 | [
[
"Geng",
"Yuxia",
""
],
[
"Chen",
"Jiaoyan",
""
],
[
"Chen",
"Zhuo",
""
],
[
"Pan",
"Jeff Z.",
""
],
[
"Ye",
"Zhiquan",
""
],
[
"Yuan",
"Zonggang",
""
],
[
"Jia",
"Yantao",
""
],
[
"Chen",
"Huajun",
""
]
] |
2102.07412 | Mohammad Mohammadamini | Hadi Veisi, Hawre Hosseini, Mohammad Mohammadamini (LIA), Wirya Fathy,
Aso Mahmudi | Jira: a Kurdish Speech Recognition System Designing and Building Speech
Corpus and Pronunciation Lexicon | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce the first large vocabulary speech recognition
system (LVSR) for the Central Kurdish language, named Jira. The Kurdish
language is an Indo-European language spoken by more than 30 million people in
several countries, but due to the lack of speech and text resources, there is
no speech recognition system for this language. To fill this gap, we introduce
the first speech corpus and pronunciation lexicon for the Kurdish language.
Regarding speech corpus, we designed a sentence collection in which the ratio
of di-phones in the collection resembles the real data of the Central Kurdish
language. The designed sentences are uttered by 576 speakers in a controlled
environment with noise-free microphones (called AsoSoft Speech-Office) and in
Telegram social network environment using mobile phones (denoted as AsoSoft
Speech-Crowdsourcing), resulted in 43.68 hours of speech. Besides, a test set
including 11 different document topics is designed and recorded in two
corresponding speech conditions (i.e., Office and Crowdsourcing). Furthermore,
a 60K pronunciation lexicon is prepared in this research in which we faced
several challenges and proposed solutions for them. The Kurdish language has
several dialects and sub-dialects that results in many lexical variations. Our
methods for script standardization of lexical variations and automatic
pronunciation of the lexicon tokens are presented in detail. To setup the
recognition engine, we used the Kaldi toolkit. A statistical tri-gram language
model that is extracted from the AsoSoft text corpus is used in the system.
Several standard recipes including HMM-based models (i.e., mono, tri1, tr2,
tri2, tri3), SGMM, and DNN methods are used to generate the acoustic model.
These methods are trained with AsoSoft Speech-Office and AsoSoft
Speech-Crowdsourcing and a combination of them. The best performance achieved
by the SGMM acoustic model which results in 13.9% of the average word error
rate (on different document topics) and 4.9% for the general topic.
| [
{
"version": "v1",
"created": "Mon, 15 Feb 2021 09:27:54 GMT"
}
] | 1,613,433,600,000 | [
[
"Veisi",
"Hadi",
"",
"LIA"
],
[
"Hosseini",
"Hawre",
"",
"LIA"
],
[
"Mohammadamini",
"Mohammad",
"",
"LIA"
],
[
"Fathy",
"Wirya",
""
],
[
"Mahmudi",
"Aso",
""
]
] |
2102.07539 | Sisay Chala | Sisay Chala, Bekele Debisa, Amante Diriba, Silas Getachew, Chala Getu,
Solomon Shiferaw | Crowdsourcing Parallel Corpus for English-Oromo Neural Machine
Translation using Community Engagement Platform | 7 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Even though Afaan Oromo is the most widely spoken language in the Cushitic
family by more than fifty million people in the Horn and East Africa, it is
surprisingly resource-scarce from a technological point of view. The increasing
amount of various useful documents written in English language brings to
investigate the machine that can translate those documents and make it easily
accessible for local language. The paper deals with implementing a translation
of English to Afaan Oromo and vice versa using Neural Machine Translation. But
the implementation is not very well explored due to the limited amount and
diversity of the corpus. However, using a bilingual corpus of just over 40k
sentence pairs we have collected, this study showed a promising result. About a
quarter of this corpus is collected via Community Engagement Platform (CEP)
that was implemented to enrich the parallel corpus through crowdsourcing
translations.
| [
{
"version": "v1",
"created": "Mon, 15 Feb 2021 13:22:30 GMT"
}
] | 1,613,433,600,000 | [
[
"Chala",
"Sisay",
""
],
[
"Debisa",
"Bekele",
""
],
[
"Diriba",
"Amante",
""
],
[
"Getachew",
"Silas",
""
],
[
"Getu",
"Chala",
""
],
[
"Shiferaw",
"Solomon",
""
]
] |
2102.07545 | Keisuke Fujii | Keisuke Fujii | Data-driven Analysis for Understanding Team Sports Behaviors | 9 pages, 2 figures. This is the first draft and the final version
will be published in the Journal of Robotics and Mechatronics | J. Robot. Mechatron., Vol.33, No.3, pp. 505-514, 2021 | 10.20965/jrm.2021.p0505 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Understanding the principles of real-world biological multi-agent behaviors
is a current challenge in various scientific and engineering fields. The rules
regarding the real-world biological multi-agent behaviors such as team sports
are often largely unknown due to their inherently higher-order interactions,
cognition, and body dynamics. Estimation of the rules from data, i.e.,
data-driven approaches such as machine learning, provides an effective way for
the analysis of such behaviors. Although most data-driven models have
non-linear structures and high prediction performances, it is sometimes hard to
interpret them. This survey focuses on data-driven analysis for quantitative
understanding of invasion team sports behaviors such as basketball and
football, and introduces two main approaches for understanding such multi-agent
behaviors: (1) extracting easily interpretable features or rules from data and
(2) generating and controlling behaviors in visually-understandable ways. The
first approach involves the visualization of learned representations and the
extraction of mathematical structures behind the behaviors. The second approach
can be used to test hypotheses by simulating and controlling future and
counterfactual behaviors. Lastly, the potential practical applications of
extracted rules, features, and generated behaviors are discussed. These
approaches can contribute to a better understanding of multi-agent behaviors in
the real world.
| [
{
"version": "v1",
"created": "Mon, 15 Feb 2021 13:31:45 GMT"
},
{
"version": "v2",
"created": "Sun, 28 Feb 2021 07:27:48 GMT"
}
] | 1,624,320,000,000 | [
[
"Fujii",
"Keisuke",
""
]
] |
2102.07599 | Suiyi Ling | Kevin Riou, Suiyi Ling, Guillaume Gallot, Patrick Le Callet | Seeing by haptic glance: reinforcement learning-based 3D object
Recognition | 5 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Human is able to conduct 3D recognition by a limited number of haptic
contacts between the target object and his/her fingers without seeing the
object. This capability is defined as `haptic glance' in cognitive
neuroscience. Most of the existing 3D recognition models were developed based
on dense 3D data. Nonetheless, in many real-life use cases, where robots are
used to collect 3D data by haptic exploration, only a limited number of 3D
points could be collected. In this study, we thus focus on solving the
intractable problem of how to obtain cognitively representative 3D key-points
of a target object with limited interactions between the robot and the object.
A novel reinforcement learning based framework is proposed, where the haptic
exploration procedure (the agent iteratively predicts the next position for the
robot to explore) is optimized simultaneously with the objective 3D recognition
with actively collected 3D points. As the model is rewarded only when the 3D
object is accurately recognized, it is driven to find the sparse yet efficient
haptic-perceptual 3D representation of the object. Experimental results show
that our proposed model outperforms the state of the art models.
| [
{
"version": "v1",
"created": "Mon, 15 Feb 2021 15:38:22 GMT"
}
] | 1,613,433,600,000 | [
[
"Riou",
"Kevin",
""
],
[
"Ling",
"Suiyi",
""
],
[
"Gallot",
"Guillaume",
""
],
[
"Callet",
"Patrick Le",
""
]
] |
2102.07617 | Yingxu Wang Prof. PhD FIEEE | Yingxu Wang, Fakhri Karray, Sam Kwong, Konstantinos N. Plataniotis,
Henry Leung, Ming Hou, Edward Tunstel, Imre J. Rudas, Ljiljana Trajkovic,
Okyay Kaynak, Janusz Kacprzyk, Mengchu Zhou, Michael H. Smith, Philip Chen
and Shushma Patel | On the Philosophical, Cognitive and Mathematical Foundations of
Symbiotic Autonomous Systems (SAS) | Accepted by Phil. Trans. Royal Society (A): Math, Phys & Engg Sci.,
379(219x), 2021, Oxford, UK | Phil. Trans. Royal Society (A): Math, Phys & Engg Sci., 379(219x),
2021, Oxford, UK | 10.1098/rsta.2020.0362 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive
systems exhibiting autonomous collective intelligence enabled by coherent
symbiosis of human-machine interactions in hybrid societies. Basic research in
the emerging field of SAS has triggered advanced general AI technologies
functioning without human intervention or hybrid symbiotic systems synergizing
humans and intelligent machines into coherent cognitive systems. This work
presents a theoretical framework of SAS underpinned by the latest advances in
intelligence, cognition, computer, and system sciences. SAS are characterized
by the composition of autonomous and symbiotic systems that adopt
bio-brain-social-inspired and heterogeneously synergized structures and
autonomous behaviors. This paper explores their cognitive and mathematical
foundations. The challenge to seamless human-machine interactions in a hybrid
environment is addressed. SAS-based collective intelligence is explored in
order to augment human capability by autonomous machine intelligence towards
the next generation of general AI, autonomous computers, and trustworthy
mission-critical intelligent systems. Emerging paradigms and engineering
applications of SAS are elaborated via an autonomous knowledge learning system
that symbiotically works between humans and cognitive robots.
| [
{
"version": "v1",
"created": "Thu, 11 Feb 2021 05:44:25 GMT"
}
] | 1,631,664,000,000 | [
[
"Wang",
"Yingxu",
""
],
[
"Karray",
"Fakhri",
""
],
[
"Kwong",
"Sam",
""
],
[
"Plataniotis",
"Konstantinos N.",
""
],
[
"Leung",
"Henry",
""
],
[
"Hou",
"Ming",
""
],
[
"Tunstel",
"Edward",
""
],
[
"Rudas",
"Imre J.",
""
],
[
"Trajkovic",
"Ljiljana",
""
],
[
"Kaynak",
"Okyay",
""
],
[
"Kacprzyk",
"Janusz",
""
],
[
"Zhou",
"Mengchu",
""
],
[
"Smith",
"Michael H.",
""
],
[
"Chen",
"Philip",
""
],
[
"Patel",
"Shushma",
""
]
] |
2102.07643 | Alexander Felfernig | Mathias Uta and Alexander Felfernig and Gottfried Schenner and
Johannes Spoecklberger | Consistency-based Merging of Variability Models | M. Uta, A. Felfernig, G. Schenner, and J. Spoecklberger.
Consistency-based Merging of Variability Models, Workshop on Configuration,
pp. 9-12, Graz, Austria, 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Globally operating enterprises selling large and complex products and
services often have to deal with situations where variability models are
locally developed to take into account the requirements of local markets. For
example, cars sold on the U.S. market are represented by variability models in
some or many aspects different from European ones. In order to support global
variability management processes, variability models and the underlying
knowledge bases often need to be integrated. This is a challenging task since
an integrated knowledge base should not produce results which are different
from those produced by the individual knowledge bases. In this paper, we
introduce an approach to variability model integration that is based on the
concepts of contextual modeling and conflict detection. We present the
underlying concepts and the results of a corresponding performance analysis.
| [
{
"version": "v1",
"created": "Mon, 15 Feb 2021 16:28:42 GMT"
}
] | 1,613,433,600,000 | [
[
"Uta",
"Mathias",
""
],
[
"Felfernig",
"Alexander",
""
],
[
"Schenner",
"Gottfried",
""
],
[
"Spoecklberger",
"Johannes",
""
]
] |
2102.07652 | Yuanpeng He | Yuanpeng He | TDQMF: Two-dimensional quantum mass function | 22 pages, 1 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantum mass function has been applied in lots of fields because of its
efficiency and validity of managing uncertainties in the form of quantum which
can be regarded as an extension of classical Dempster-Shafer (D-S) evidence
theory. However, how to handle uncertainties in the form of quantum is still an
open issue. In this paper, a new method is proposed to dispose uncertain
quantum information, which is called two-dimensional quantum mass function
(TDQMF). A TDQMF is consist of two elements, TQ = (Qoriginal, Qindicative),
both of the Qs are quantum mass functions, in which the Qindicative is an
indicator of the reliability on Qoriginal. More flexibility and effectiveness
are offered in handling uncertainty in the field of quantum by the proposed
method compared with primary quantum mass function. Besides, some numerical
examples are provided and some practical applications are given to verify its
correctness and validity
| [
{
"version": "v1",
"created": "Sun, 31 Jan 2021 14:15:41 GMT"
}
] | 1,613,433,600,000 | [
[
"He",
"Yuanpeng",
""
]
] |
2102.07716 | Eric Langlois | Eric D. Langlois and Tom Everitt | How RL Agents Behave When Their Actions Are Modified | 10 pages (+6 appendix); 7 figures. Published in the AAAI 2021
Conference on AI. Code is available at https://github.com/edlanglois/mamdp | Proceedings of the AAAI Conference on Artificial Intelligence,
35(13), 11586-11594 (2021) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning in complex environments may require supervision to
prevent the agent from attempting dangerous actions. As a result of supervisor
intervention, the executed action may differ from the action specified by the
policy. How does this affect learning? We present the Modified-Action Markov
Decision Process, an extension of the MDP model that allows actions to differ
from the policy. We analyze the asymptotic behaviours of common reinforcement
learning algorithms in this setting and show that they adapt in different ways:
some completely ignore modifications while others go to various lengths in
trying to avoid action modifications that decrease reward. By choosing the
right algorithm, developers can prevent their agents from learning to
circumvent interruptions or constraints, and better control agent responses to
other kinds of action modification, like self-damage.
| [
{
"version": "v1",
"created": "Mon, 15 Feb 2021 18:10:03 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Jun 2021 05:06:29 GMT"
}
] | 1,625,097,600,000 | [
[
"Langlois",
"Eric D.",
""
],
[
"Everitt",
"Tom",
""
]
] |
2102.07917 | Luis Claudio Sugi Afonso | Nathalia Q. Ascen\c{c}\~ao, Luis C. S. Afonso, Danilo Colombo, Luciano
Oliveira, Jo\~ao P. Papa | Information Ranking Using Optimum-Path Forest | null | null | 10.1109/IJCNN48605.2020.9207689 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The task of learning to rank has been widely studied by the machine learning
community, mainly due to its use and great importance in information retrieval,
data mining, and natural language processing. Therefore, ranking accurately and
learning to rank are crucial tasks. Context-Based Information Retrieval systems
have been of great importance to reduce the effort of finding relevant data.
Such systems have evolved by using machine learning techniques to improve their
results, but they are mainly dependent on user feedback. Although information
retrieval has been addressed in different works along with classifiers based on
Optimum-Path Forest (OPF), these have so far not been applied to the learning
to rank task. Therefore, the main contribution of this work is to evaluate
classifiers based on Optimum-Path Forest, in such a context. Experiments were
performed considering the image retrieval and ranking scenarios, and the
performance of OPF-based approaches was compared to the well-known SVM-Rank
pairwise technique and a baseline based on distance calculation. The
experiments showed competitive results concerning precision and outperformed
traditional techniques in terms of computational load.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 02:01:29 GMT"
}
] | 1,613,520,000,000 | [
[
"Ascenção",
"Nathalia Q.",
""
],
[
"Afonso",
"Luis C. S.",
""
],
[
"Colombo",
"Danilo",
""
],
[
"Oliveira",
"Luciano",
""
],
[
"Papa",
"João P.",
""
]
] |
2102.07960 | Maryam Majidi | Maryam Majidi and Rahil Mahdian Toroghi | A Combination of Multi-Objective Genetic Algorithm and Deep Learning for
Music Harmony Generation | 14 pages, 8 figures, 1 table | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Automatic Music Generation (AMG) has become an interesting research topic for
many scientists in artificial intelligence, who are also interested in the
music industry. One of the main challenges in AMG is that there is no clear
objective evaluation criterion that can measure the music grammar, structural
rules, and audience satisfaction. Also, original music contains different
elements that should work together, such as melody, harmony, and rhythm; but in
the most of previous works, AMG works only for one element (e.g., melody).
Therefore, in this paper, we propose a Multi-Objective Genetic Algorithm
(MO-GA) to generate polyphonic music pieces, considering grammar and listener
satisfaction. In this method, we use three objective functions. The first
objective function is the accuracy of the generated music piece, based on music
theory; and the other two objective functions are modeled scores provided by
music experts and ordinary listeners. The scoring of experts and listeners
separately are modeled using Bi-directional Long Short-Term Memory (Bi-LSTM)
neural networks. The proposed music generation system tries to maximize
mentioned objective functions to generate a new piece of music, including
melody and harmony. The results show that the proposed method can generate
pleasant pieces with desired styles and lengths, along with harmonic sounds
that follow the grammar.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 05:05:54 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Jun 2021 06:16:38 GMT"
},
{
"version": "v3",
"created": "Fri, 3 Jun 2022 17:11:53 GMT"
}
] | 1,654,473,600,000 | [
[
"Majidi",
"Maryam",
""
],
[
"Toroghi",
"Rahil Mahdian",
""
]
] |
2102.08029 | Rukshan Wijesinghe | Rukshan Wijesinghe, Kasun Vithanage, Dumindu Tissera, Alex Xavier,
Subha Fernando and Jayathu Samarawickrama | Transferring Domain Knowledge with an Adviser in Continuous Tasks | Accepted by the 25th Pacific-Asia Conference on Knowledge Discovery
and Data Mining (PAKDD-2021) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in Reinforcement Learning (RL) have surpassed human-level
performance in many simulated environments. However, existing reinforcement
learning techniques are incapable of explicitly incorporating already known
domain-specific knowledge into the learning process. Therefore, the agents have
to explore and learn the domain knowledge independently through a trial and
error approach, which consumes both time and resources to make valid responses.
Hence, we adapt the Deep Deterministic Policy Gradient (DDPG) algorithm to
incorporate an adviser, which allows integrating domain knowledge in the form
of pre-learned policies or pre-defined relationships to enhance the agent's
learning process. Our experiments on OpenAi Gym benchmark tasks show that
integrating domain knowledge through advisers expedites the learning and
improves the policy towards better optima.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 09:03:33 GMT"
}
] | 1,613,520,000,000 | [
[
"Wijesinghe",
"Rukshan",
""
],
[
"Vithanage",
"Kasun",
""
],
[
"Tissera",
"Dumindu",
""
],
[
"Xavier",
"Alex",
""
],
[
"Fernando",
"Subha",
""
],
[
"Samarawickrama",
"Jayathu",
""
]
] |
2102.08035 | Raid Al-Nima | Raid R. Al-Nima, Fawaz S. Abdullah, Ali N. Hamoodi | Design a Technology Based on the Fusion of Genetic Algorithm, Neural
network and Fuzzy logic | 11 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes the design and development of a prototype technique for
artificial intelligence based on the fusion of genetic algorithm, neural
network and fuzzy logic. It starts by establishing a relationship between the
neural network and fuzzy logic. Then, it combines the genetic algorithm with
them. Information fusions are at the confidence level, where matching scores
can be reported and discussed. The technique is called the Genetic Neuro-Fuzzy
(GNF). It can be used for high accuracy real-time environments.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 09:17:58 GMT"
}
] | 1,613,520,000,000 | [
[
"Al-Nima",
"Raid R.",
""
],
[
"Abdullah",
"Fawaz S.",
""
],
[
"Hamoodi",
"Ali N.",
""
]
] |
2102.08124 | Brian Chmiel | Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Seffi Naor,
Daniel Soudry | Accelerated Sparse Neural Training: A Provable and Efficient Method to
Find N:M Transposable Masks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Unstructured pruning reduces the memory footprint in deep neural networks
(DNNs). Recently, researchers proposed different types of structural pruning
intending to reduce also the computation complexity. In this work, we first
suggest a new measure called mask-diversity which correlates with the expected
accuracy of the different types of structural pruning. We focus on the recently
suggested N:M fine-grained block sparsity mask, in which for each block of M
weights, we have at least N zeros. While N:M fine-grained block sparsity allows
acceleration in actual modern hardware, it can be used only to accelerate the
inference phase. In order to allow for similar accelerations in the training
phase, we suggest a novel transposable fine-grained sparsity mask, where the
same mask can be used for both forward and backward passes. Our transposable
mask guarantees that both the weight matrix and its transpose follow the same
sparsity pattern; thus, the matrix multiplication required for passing the
error backward can also be accelerated. We formulate the problem of finding the
optimal transposable-mask as a minimum-cost flow problem. Additionally, to
speed up the minimum-cost flow computation, we also introduce a fast
linear-time approximation that can be used when the masks dynamically change
during training. Our experiments suggest a 2x speed-up in the matrix
multiplications with no accuracy degradation over vision and language models.
Finally, to solve the problem of switching between different structure
constraints, we suggest a method to convert a pre-trained model with
unstructured sparsity to an N:M fine-grained block sparsity model with little
to no training. A reference implementation can be found at
https://github.com/papers-submission/structured_transposable_masks.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 12:44:16 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Oct 2021 07:16:22 GMT"
}
] | 1,634,860,800,000 | [
[
"Hubara",
"Itay",
""
],
[
"Chmiel",
"Brian",
""
],
[
"Island",
"Moshe",
""
],
[
"Banner",
"Ron",
""
],
[
"Naor",
"Seffi",
""
],
[
"Soudry",
"Daniel",
""
]
] |
2102.08180 | Todd Robinson | Todd Robinson | Value of Information for Argumentation based Intelligence Analysis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Argumentation provides a representation of arguments and attacks between
these arguments. Argumentation can be used to represent a reasoning process
over evidence to reach conclusions. Within such a reasoning process,
understanding the value of information can improve the quality of decision
making based on the output of the reasoning process. The value of an item of
information is inherently dependent on the available evidence and the question
being answered by the reasoning. In this paper we introduce a value of
information on argument frameworks to identify the most valuable arguments
within the finite set of arguments in the framework, and the arguments and
attacks which could be added to change the output of an evaluation. We
demonstrate the value of information within an argument framework representing
an intelligence analysis in the maritime domain. Understanding the value of
information in an intelligence analysis will allow analysts to balance the
value against the costs and risks of collection, to effectively request further
collection of intelligence to increase the confidence in the analysis of
hypotheses.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 14:28:33 GMT"
}
] | 1,613,520,000,000 | [
[
"Robinson",
"Todd",
""
]
] |
2102.08307 | Niall Creech | Niall Creech, Natalia Criado Pacheco, Simon Miles | Dynamic neighbourhood optimisation for task allocation using multi-agent | 28 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In large-scale systems there are fundamental challenges when centralised
techniques are used for task allocation. The number of interactions is limited
by resource constraints such as on computation, storage, and network
communication. We can increase scalability by implementing the system as a
distributed task-allocation system, sharing tasks across many agents. However,
this also increases the resource cost of communications and synchronisation,
and is difficult to scale.
In this paper we present four algorithms to solve these problems. The
combination of these algorithms enable each agent to improve their task
allocation strategy through reinforcement learning, while changing how much
they explore the system in response to how optimal they believe their current
strategy is, given their past experience. We focus on distributed agent systems
where the agents' behaviours are constrained by resource usage limits, limiting
agents to local rather than system-wide knowledge. We evaluate these algorithms
in a simulated environment where agents are given a task composed of multiple
subtasks that must be allocated to other agents with differing capabilities, to
then carry out those tasks. We also simulate real-life system effects such as
networking instability. Our solution is shown to solve the task allocation
problem to 6.7% of the theoretical optimal within the system configurations
considered. It provides 5x better performance recovery over no-knowledge
retention approaches when system connectivity is impacted, and is tested
against systems up to 100 agents with less than a 9% impact on the algorithms'
performance.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 17:49:14 GMT"
},
{
"version": "v2",
"created": "Wed, 11 May 2022 09:46:30 GMT"
}
] | 1,652,313,600,000 | [
[
"Creech",
"Niall",
""
],
[
"Pacheco",
"Natalia Criado",
""
],
[
"Miles",
"Simon",
""
]
] |
2102.08317 | Niall Creech | Niall Creech, Natalia Criado Pacheco, Simon Miles | Resource allocation in dynamic multiagent systems | 22 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Resource allocation and task prioritisation are key problem domains in the
fields of autonomous vehicles, networking, and cloud computing. The challenge
in developing efficient and robust algorithms comes from the dynamic nature of
these systems, with many components communicating and interacting in complex
ways. The multi-group resource allocation optimisation (MG-RAO) algorithm we
present uses multiple function approximations of resource demand over time,
alongside reinforcement learning techniques, to develop a novel method of
optimising resource allocation in these multi-agent systems. This method is
applicable where there are competing demands for shared resources, or in task
prioritisation problems. Evaluation is carried out in a simulated environment
containing multiple competing agents. We compare the new algorithm to an
approach where child agents distribute their resources uniformly across all the
tasks they can be allocated. We also contrast the performance of the algorithm
where resource allocation is modelled separately for groups of agents, as to
being modelled jointly over all agents. The MG-RAO algorithm shows a 23 - 28%
improvement over fixed resource allocation in the simulated environments.
Results also show that, in a volatile system, using the MG-RAO algorithm
configured so that child agents model resource allocation for all agents as a
whole has 46.5% of the performance of when it is set to model multiple groups
of agents. These results demonstrate the ability of the algorithm to solve
resource allocation problems in multi-agent systems and to perform well in
dynamic environments.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 17:56:23 GMT"
}
] | 1,613,520,000,000 | [
[
"Creech",
"Niall",
""
],
[
"Pacheco",
"Natalia Criado",
""
],
[
"Miles",
"Simon",
""
]
] |
2102.08482 | Bashar Awwad Shiekh Hasan | Robert McCluskey, Amir Enshaei, Bashar Awwad Shiekh Hasan | Finding the Ground-Truth from Multiple Labellers: Why Parameters of the
Task Matter | 16 pages, 5 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Employing multiple workers to label data for machine learning models has
become increasingly important in recent years with greater demand to collect
huge volumes of labelled data to train complex models while mitigating the risk
of incorrect and noisy labelling. Whether it is large scale data gathering on
popular crowd-sourcing platforms or smaller sets of workers in high-expertise
labelling exercises, there are various methods recommended to gather a
consensus from employed workers and establish ground-truth labels. However,
there is very little research on how the various parameters of a labelling task
can impact said methods. These parameters include the number of workers, worker
expertise, number of labels in a taxonomy and sample size. In this paper,
Majority Vote, CrowdTruth and Binomial Expectation Maximisation are
investigated against the permutations of these parameters in order to provide
better understanding of the parameter settings to give an advantage in
ground-truth inference. Findings show that both Expectation Maximisation and
CrowdTruth are only likely to give an advantage over majority vote under
certain parameter conditions, while there are many cases where the methods can
be shown to have no major impact. Guidance is given as to what parameters
methods work best under, while the experimental framework provides a way of
testing other established methods and also testing new methods that can attempt
to provide advantageous performance where the methods in this paper did not. A
greater level of understanding regarding optimal crowd-sourcing parameters is
also achieved.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 22:51:11 GMT"
}
] | 1,613,606,400,000 | [
[
"McCluskey",
"Robert",
""
],
[
"Enshaei",
"Amir",
""
],
[
"Hasan",
"Bashar Awwad Shiekh",
""
]
] |
2102.08689 | Zhe Chen | Zhe Chen, Daniel Harabor, Jiaoyang Li, Peter J. Stuckey | Symmetry Breaking for k-Robust Multi-Agent Path Finding | 8 pages. Accepted by Thirty-Fifth AAAI Conference on Artificial
Intelligence | Proceedings of the AAAI Conference on Artificial Intelligence,
35(14), 12267-12274 (2021) | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by
unexpected events. To address such situations recent work describes k-Robust
Conflict-BasedSearch (k-CBS): an algorithm that produces coordinated and
collision-free plan that is robust for up to k delays. In this work we
introducing a variety of pairwise symmetry breaking constraints, specific to
k-robust planning, that can efficiently find compatible and optimal paths for
pairs of conflicting agents. We give a thorough description of the new
constraints and report large improvements to success rate ina range of domains
including: (i) classic MAPF benchmarks;(ii) automated warehouse domains and;
(iii) on maps from the 2019 Flatland Challenge, a recently introduced railway
domain where k-robust planning can be fruitfully applied to schedule trains.
| [
{
"version": "v1",
"created": "Wed, 17 Feb 2021 11:09:33 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Oct 2021 05:00:21 GMT"
}
] | 1,635,465,600,000 | [
[
"Chen",
"Zhe",
""
],
[
"Harabor",
"Daniel",
""
],
[
"Li",
"Jiaoyang",
""
],
[
"Stuckey",
"Peter J.",
""
]
] |
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