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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2003.10024 | Tristan Cazenave | Tristan Cazenave | Generalized Nested Rollout Policy Adaptation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm for
single player games. In this paper we propose to generalize NRPA with a
temperature and a bias and to analyze theoretically the algorithms. The
generalized algorithm is named GNRPA. Experiments show it improves on NRPA for
different application domains: SameGame and the Traveling Salesman Problem with
Time Windows.
| [
{
"version": "v1",
"created": "Sun, 22 Mar 2020 23:12:18 GMT"
}
] | 1,585,008,000,000 | [
[
"Cazenave",
"Tristan",
""
]
] |
2003.10378 | James Goodman | James Goodman and Simon Lucas | Weighting NTBEA for Game AI Optimisation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The N-Tuple Bandit Evolutionary Algorithm (NTBEA) has proven very effective
in optimising algorithm parameters in Game AI. A potential weakness is the use
of a simple average of all component Tuples in the model. This study
investigates a refinement to the N-Tuple model used in NTBEA by weighting these
component Tuples by their level of information and specificity of match. We
introduce weighting functions to the model to obtain Weighted- NTBEA and test
this on four benchmark functions and two game environments. These tests show
that vanilla NTBEA is the most reliable and performant of the algorithms
tested. Furthermore we show that given an iteration budget it is better to
execute several independent NTBEA runs, and use part of the budget to find the
best recommendation from these runs.
| [
{
"version": "v1",
"created": "Mon, 23 Mar 2020 16:44:28 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Apr 2020 14:52:11 GMT"
}
] | 1,585,785,600,000 | [
[
"Goodman",
"James",
""
],
[
"Lucas",
"Simon",
""
]
] |
2003.10520 | Christopher Bamford | Chris Bamford, Simon Lucas | Neural Game Engine: Accurate learning of generalizable forward models
from pixels | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Access to a fast and easily copied forward model of a game is essential for
model-based reinforcement learning and for algorithms such as Monte Carlo tree
search, and is also beneficial as a source of unlimited experience data for
model-free algorithms. Learning forward models is an interesting and important
challenge in order to address problems where a model is not available. Building
upon previous work on the Neural GPU, this paper introduces the Neural Game
Engine, as a way to learn models directly from pixels. The learned models are
able to generalise to different size game levels to the ones they were trained
on without loss of accuracy. Results on 10 deterministic General Video Game AI
games demonstrate competitive performance, with many of the games models being
learned perfectly both in terms of pixel predictions and reward predictions.
The pre-trained models are available through the OpenAI Gym interface and are
available publicly for future research here:
\url{https://github.com/Bam4d/Neural-Game-Engine}
| [
{
"version": "v1",
"created": "Mon, 23 Mar 2020 20:04:55 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Mar 2020 20:50:35 GMT"
}
] | 1,585,785,600,000 | [
[
"Bamford",
"Chris",
""
],
[
"Lucas",
"Simon",
""
]
] |
2003.11370 | Martin Thomas Horsch | Martin Thomas Horsch and Silvia Chiacchiera and Bj\"orn Schembera and
Michael A. Seaton and Ilian T. Todorov | Semantic interoperability based on the European Materials and Modelling
Ontology and its ontological paradigm: Mereosemiotics | The co-authors M.T.H. and B.S. acknowledge funding from the German
Research Foundation (DFG) through the National Research Data Infrastructure
for Catalysis-Related Sciences (NFDI4Cat) within the National Research Data
Infrastructure (NFDI) programme of the Joint Science Conference (GWK) | null | 10.5281/zenodo.3902900 | Inprodat e.V. technical report no. 2020-B | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The European Materials and Modelling Ontology (EMMO) has recently been
advanced in the computational molecular engineering and multiscale modelling
communities as a top-level ontology, aiming to support semantic
interoperability and data integration solutions, e.g., for research data
infrastructures. The present work explores how top-level ontologies that are
based on the same paradigm - the same set of fundamental postulates - as the
EMMO can be applied to models of physical systems and their use in
computational engineering practice. This paradigm, which combines mereology (in
its extension as mereotopology) and semiotics (following Peirce's approach), is
here referred to as mereosemiotics. Multiple conceivable ways of implementing
mereosemiotics are compared, and the design space consisting of the possible
types of top-level ontologies following this paradigm is characterized.
| [
{
"version": "v1",
"created": "Sun, 22 Mar 2020 13:19:55 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Apr 2020 11:19:52 GMT"
},
{
"version": "v3",
"created": "Mon, 27 Jul 2020 16:00:45 GMT"
},
{
"version": "v4",
"created": "Thu, 11 Feb 2021 10:08:42 GMT"
}
] | 1,613,088,000,000 | [
[
"Horsch",
"Martin Thomas",
""
],
[
"Chiacchiera",
"Silvia",
""
],
[
"Schembera",
"Björn",
""
],
[
"Seaton",
"Michael A.",
""
],
[
"Todorov",
"Ilian T.",
""
]
] |
2003.11706 | Mehrzad Saremi | Mehrzad Saremi | A Critique on the Interventional Detection of Causal Relationships | 15 pages, 6 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Interventions are of fundamental importance in Pearl's probabilistic
causality regime. In this paper, we will inspect how interventions influence
the interpretation of causation in causal models in specific situation. To this
end, we will introduce a priori relationships as non-causal relationships in a
causal system. Then, we will proceed to discuss the cases that interventions
can lead to spurious causation interpretations. This includes the
interventional detection of a priori relationships, and cases where the
interventional detection of causality forms structural causal models that are
not valid in natural situations. We will also discuss other properties of a
priori relations and SCMs that have a priori information in their structural
equations.
| [
{
"version": "v1",
"created": "Thu, 26 Mar 2020 02:16:05 GMT"
}
] | 1,585,267,200,000 | [
[
"Saremi",
"Mehrzad",
""
]
] |
2003.12828 | Albert Buchard | Albert Buchard, Baptiste Bouvier, Giulia Prando, Rory Beard, Michail
Livieratos, Dan Busbridge, Daniel Thompson, Jonathan Richens, Yuanzhao Zhang,
Adam Baker, Yura Perov, Kostis Gourgoulias, Saurabh Johri | Learning medical triage from clinicians using Deep Q-Learning | 17 pages, 4 figures, 3 tables, preprint, in press | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medical Triage is of paramount importance to healthcare systems, allowing for
the correct orientation of patients and allocation of the necessary resources
to treat them adequately. While reliable decision-tree methods exist to triage
patients based on their presentation, those trees implicitly require human
inference and are not immediately applicable in a fully automated setting. On
the other hand, learning triage policies directly from experts may correct for
some of the limitations of hard-coded decision-trees. In this work, we present
a Deep Reinforcement Learning approach (a variant of DeepQ-Learning) to triage
patients using curated clinical vignettes. The dataset, consisting of 1374
clinical vignettes, was created by medical doctors to represent real-life
cases. Each vignette is associated with an average of 3.8 expert triage
decisions given by medical doctors relying solely on medical history. We show
that this approach is on a par with human performance, yielding safe triage
decisions in 94% of cases, and matching expert decisions in 85% of cases. The
trained agent learns when to stop asking questions, acquires optimized decision
policies requiring less evidence than supervised approaches, and adapts to the
novelty of a situation by asking for more information. Overall, we demonstrate
that a Deep Reinforcement Learning approach can learn effective medical triage
policies directly from expert decisions, without requiring expert knowledge
engineering. This approach is scalable and can be deployed in healthcare
settings or geographical regions with distinct triage specifications, or where
trained experts are scarce, to improve decision making in the early stage of
care.
| [
{
"version": "v1",
"created": "Sat, 28 Mar 2020 16:07:41 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Jun 2020 16:39:37 GMT"
}
] | 1,593,043,200,000 | [
[
"Buchard",
"Albert",
""
],
[
"Bouvier",
"Baptiste",
""
],
[
"Prando",
"Giulia",
""
],
[
"Beard",
"Rory",
""
],
[
"Livieratos",
"Michail",
""
],
[
"Busbridge",
"Dan",
""
],
[
"Thompson",
"Daniel",
""
],
[
"Richens",
"Jonathan",
""
],
[
"Zhang",
"Yuanzhao",
""
],
[
"Baker",
"Adam",
""
],
[
"Perov",
"Yura",
""
],
[
"Gourgoulias",
"Kostis",
""
],
[
"Johri",
"Saurabh",
""
]
] |
2003.13159 | Tanel Tammet | Tanel Tammet | Extending Automated Deduction for Commonsense Reasoning | 19 pages, no figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Commonsense reasoning has long been considered as one of the holy grails of
artificial intelligence. Most of the recent progress in the field has been
achieved by novel machine learning algorithms for natural language processing.
However, without incorporating logical reasoning, these algorithms remain
arguably shallow. With some notable exceptions, developers of practical
automated logic-based reasoners have mostly avoided focusing on the problem.
The paper argues that the methods and algorithms used by existing automated
reasoners for classical first-order logic can be extended towards commonsense
reasoning. Instead of devising new specialized logics we propose a framework of
extensions to the mainstream resolution-based search methods to make these
capable of performing search tasks for practical commonsense reasoning with
reasonable efficiency. The proposed extensions mostly rely on operating on
ordinary proof trees and are devised to handle commonsense knowledge bases
containing inconsistencies, default rules, taxonomies, topics, relevance,
confidence and similarity measures. We claim that machine learning is best
suited for the construction of commonsense knowledge bases while the extended
logic-based methods would be well-suited for actually answering queries from
these knowledge bases.
| [
{
"version": "v1",
"created": "Sun, 29 Mar 2020 23:17:16 GMT"
}
] | 1,585,612,800,000 | [
[
"Tammet",
"Tanel",
""
]
] |
2003.13450 | Son-Il Kwak | I.M. Son, S.I. Kwak, U.J. Han, J.H. Pak, M. Han, J.R. Pyon, U.S. Ryu | A Novel Fuzzy Approximate Reasoning Method Based on Extended Distance
Measure in SISO Fuzzy System | 24 pages, 1 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an original method of fuzzy approximate reasoning that
can open a new direction of research in the uncertainty inference of Artificial
Intelligence(AI) and Computational Intelligence(CI). Fuzzy modus ponens (FMP)
and fuzzy modus tollens(FMT) are two fundamental and basic models of general
fuzzy approximate reasoning in various fuzzy systems. And the reductive
property is one of the essential and important properties in the approximate
reasoning theory and it is a lot of applications. This paper suggests a kind of
extended distance measure (EDM) based approximate reasoning method in the
single input single output(SISO) fuzzy system with discrete fuzzy set vectors
of different dimensions. The EDM based fuzzy approximate reasoning method is
consists of two part, i.e., FMP-EDM and FMT-EDM. The distance measure based
fuzzy reasoning method that the dimension of the antecedent discrete fuzzy set
is equal to one of the consequent discrete fuzzy set has already solved in
other paper. In this paper discrete fuzzy set vectors of different dimensions
mean that the dimension of the antecedent discrete fuzzy set differs from one
of the consequent discrete fuzzy set in the SISO fuzzy system. That is, this
paper is based on EDM. The experimental results highlight that the proposed
approximate reasoning method is comparatively clear and effective with respect
to the reductive property, and in accordance with human thinking than existing
fuzzy reasoning methods.
| [
{
"version": "v1",
"created": "Fri, 27 Mar 2020 02:31:53 GMT"
}
] | 1,585,612,800,000 | [
[
"Son",
"I. M.",
""
],
[
"Kwak",
"S. I.",
""
],
[
"Han",
"U. J.",
""
],
[
"Pak",
"J. H.",
""
],
[
"Han",
"M.",
""
],
[
"Pyon",
"J. R.",
""
],
[
"Ryu",
"U. S.",
""
]
] |
2003.13590 | Tao Qin Dr. | Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan
Yang, Li Zhao, Tao Qin, Tie-Yan Liu, Hsiao-Wuen Hon | Suphx: Mastering Mahjong with Deep Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) has achieved great success in many domains, and
game AI is widely regarded as its beachhead since the dawn of AI. In recent
years, studies on game AI have gradually evolved from relatively simple
environments (e.g., perfect-information games such as Go, chess, shogi or
two-player imperfect-information games such as heads-up Texas hold'em) to more
complex ones (e.g., multi-player imperfect-information games such as
multi-player Texas hold'em and StartCraft II). Mahjong is a popular
multi-player imperfect-information game worldwide but very challenging for AI
research due to its complex playing/scoring rules and rich hidden information.
We design an AI for Mahjong, named Suphx, based on deep reinforcement learning
with some newly introduced techniques including global reward prediction,
oracle guiding, and run-time policy adaptation. Suphx has demonstrated stronger
performance than most top human players in terms of stable rank and is rated
above 99.99% of all the officially ranked human players in the Tenhou platform.
This is the first time that a computer program outperforms most top human
players in Mahjong.
| [
{
"version": "v1",
"created": "Mon, 30 Mar 2020 16:18:16 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Apr 2020 03:46:55 GMT"
}
] | 1,585,785,600,000 | [
[
"Li",
"Junjie",
""
],
[
"Koyamada",
"Sotetsu",
""
],
[
"Ye",
"Qiwei",
""
],
[
"Liu",
"Guoqing",
""
],
[
"Wang",
"Chao",
""
],
[
"Yang",
"Ruihan",
""
],
[
"Zhao",
"Li",
""
],
[
"Qin",
"Tao",
""
],
[
"Liu",
"Tie-Yan",
""
],
[
"Hon",
"Hsiao-Wuen",
""
]
] |
2003.13633 | Francisco \'Alvarez | F. Mart\'inez-\'Alvarez, G. Asencio-Cort\'es, J. F. Torres, D.
Guti\'errez-Avil\'es, L. Melgar-Garc\'ia, R. P\'erez-Chac\'on, C.
Rubio-Escudero, J. C. Riquelme, A. Troncoso | Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on
the COVID-19 propagation model | 30 pages, 4 figures | null | 10.1089/big.2020.0051 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A novel bioinspired metaheuristic is proposed in this work, simulating how
the coronavirus spreads and infects healthy people. From an initial individual
(the patient zero), the coronavirus infects new patients at known rates,
creating new populations of infected people. Every individual can either die or
infect and, afterwards, be sent to the recovered population. Relevant terms
such as re-infection probability, super-spreading rate or traveling rate are
introduced in the model in order to simulate as accurately as possible the
coronavirus activity. The Coronavirus Optimization Algorithm has two major
advantages compared to other similar strategies. First, the input parameters
are already set according to the disease statistics, preventing researchers
from initializing them with arbitrary values. Second, the approach has the
ability of ending after several iterations, without setting this value either.
Infected population initially grows at an exponential rate but after some
iterations, when considering social isolation measures and the high number
recovered and dead people, the number of infected people starts decreasing in
subsequent iterations. Furthermore, a parallel multi-virus version is proposed
in which several coronavirus strains evolve over time and explore wider search
space areas in less iterations. Finally, the metaheuristic has been combined
with deep learning models, in order to find optimal hyperparameters during the
training phase. As application case, the problem of electricity load time
series forecasting has been addressed, showing quite remarkable performance.
| [
{
"version": "v1",
"created": "Mon, 30 Mar 2020 17:10:02 GMT"
},
{
"version": "v2",
"created": "Thu, 16 Apr 2020 11:28:04 GMT"
}
] | 1,596,412,800,000 | [
[
"Martínez-Álvarez",
"F.",
""
],
[
"Asencio-Cortés",
"G.",
""
],
[
"Torres",
"J. F.",
""
],
[
"Gutiérrez-Avilés",
"D.",
""
],
[
"Melgar-García",
"L.",
""
],
[
"Pérez-Chacón",
"R.",
""
],
[
"Rubio-Escudero",
"C.",
""
],
[
"Riquelme",
"J. C.",
""
],
[
"Troncoso",
"A.",
""
]
] |
2003.13668 | Sam Vente | Sam Vente (1), Angelika Kimmig (1), Alun Preece (1), Federico Cerutti
(2) ((1) Cardiff University, (2) University of Brescia) | Increasing negotiation performance at the edge of the network | Accepted for presentation at The 7th International Conference on
Agreement Technologies (AT 2020) | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Automated negotiation has been used in a variety of distributed settings,
such as privacy in the Internet of Things (IoT) devices and power distribution
in Smart Grids. The most common protocol under which these agents negotiate is
the Alternating Offers Protocol (AOP). Under this protocol, agents cannot
express any additional information to each other besides a counter offer. This
can lead to unnecessarily long negotiations when, for example, negotiations are
impossible, risking to waste bandwidth that is a precious resource at the edge
of the network. While alternative protocols exist which alleviate this problem,
these solutions are too complex for low power devices, such as IoT sensors
operating at the edge of the network. To improve this bottleneck, we introduce
an extension to AOP called Alternating Constrained Offers Protocol (ACOP), in
which agents can also express constraints to each other. This allows agents to
both search the possibility space more efficiently and recognise impossible
situations sooner. We empirically show that agents using ACOP can significantly
reduce the number of messages a negotiation takes, independently of the
strategy agents choose. In particular, we show our method significantly reduces
the number of messages when an agreement is not possible. Furthermore, when an
agreement is possible it reaches this agreement sooner with no negative effect
on the utility.
| [
{
"version": "v1",
"created": "Mon, 30 Mar 2020 17:52:59 GMT"
}
] | 1,585,612,800,000 | [
[
"Vente",
"Sam",
"",
"Cardiff University"
],
[
"Kimmig",
"Angelika",
"",
"Cardiff University"
],
[
"Preece",
"Alun",
"",
"Cardiff University"
],
[
"Cerutti",
"Federico",
"",
"University of Brescia"
]
] |
2003.14415 | Samuel Albanie | Samuel Albanie, Jaime Thewmore, Robert McCraith, Joao F. Henriques | State-of-Art-Reviewing: A Radical Proposal to Improve Scientific
Publication | SIGBOVIK 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Peer review forms the backbone of modern scientific manuscript evaluation.
But after two hundred and eighty-nine years of egalitarian service to the
scientific community, does this protocol remain fit for purpose in 2020? In
this work, we answer this question in the negative (strong reject, high
confidence) and propose instead State-Of-the-Art Review (SOAR), a neoteric
reviewing pipeline that serves as a 'plug-and-play' replacement for peer
review. At the heart of our approach is an interpretation of the review process
as a multi-objective, massively distributed and extremely-high-latency
optimisation, which we scalarise and solve efficiently for PAC and CMT-optimal
solutions. We make the following contributions: (1) We propose a highly
scalable, fully automatic methodology for review, drawing inspiration from
best-practices from premier computer vision and machine learning conferences;
(2) We explore several instantiations of our approach and demonstrate that SOAR
can be used to both review prints and pre-review pre-prints; (3) We wander
listlessly in vain search of catharsis from our latest rounds of savage CVPR
rejections.
| [
{
"version": "v1",
"created": "Tue, 31 Mar 2020 17:58:36 GMT"
}
] | 1,585,699,200,000 | [
[
"Albanie",
"Samuel",
""
],
[
"Thewmore",
"Jaime",
""
],
[
"McCraith",
"Robert",
""
],
[
"Henriques",
"Joao F.",
""
]
] |
2004.00048 | Jo\~ao Abrantes | Jo\~ao P. Abrantes, Arnaldo J. Abrantes, Frans A. Oliehoek | Mimicking Evolution with Reinforcement Learning | 18 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolution gave rise to human and animal intelligence here on Earth. We argue
that the path to developing artificial human-like-intelligence will pass
through mimicking the evolutionary process in a nature-like simulation. In
Nature, there are two processes driving the development of the brain: evolution
and learning. Evolution acts slowly, across generations, and amongst other
things, it defines what agents learn by changing their internal reward
function. Learning acts fast, across one's lifetime, and it quickly updates
agents' policy to maximise pleasure and minimise pain. The reward function is
slowly aligned with the fitness function by evolution, however, as agents
evolve the environment and its fitness function also change, increasing the
misalignment between reward and fitness. It is extremely computationally
expensive to replicate these two processes in simulation. This work proposes
Evolution via Evolutionary Reward (EvER) that allows learning to
single-handedly drive the search for policies with increasingly evolutionary
fitness by ensuring the alignment of the reward function with the fitness
function. In this search, EvER makes use of the whole state-action trajectories
that agents go through their lifetime. In contrast, current evolutionary
algorithms discard this information and consequently limit their potential
efficiency at tackling sequential decision problems. We test our algorithm in
two simple bio-inspired environments and show its superiority at generating
more capable agents at surviving and reproducing their genes when compared with
a state-of-the-art evolutionary algorithm.
| [
{
"version": "v1",
"created": "Tue, 31 Mar 2020 18:16:53 GMT"
},
{
"version": "v2",
"created": "Wed, 6 May 2020 16:08:43 GMT"
}
] | 1,588,809,600,000 | [
[
"Abrantes",
"João P.",
""
],
[
"Abrantes",
"Arnaldo J.",
""
],
[
"Oliehoek",
"Frans A.",
""
]
] |
2004.00377 | Aske Plaat | Matthias Muller-Brockhausen, Mike Preuss, Aske Plaat | A New Challenge: Approaching Tetris Link with AI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Decades of research have been invested in making computer programs for
playing games such as Chess and Go. This paper focuses on a new game, Tetris
Link, a board game that is still lacking any scientific analysis. Tetris Link
has a large branching factor, hampering a traditional heuristic planning
approach. We explore heuristic planning and two other approaches: Reinforcement
Learning, Monte Carlo tree search. We document our approach and report on their
relative performance in a tournament. Curiously, the heuristic approach is
stronger than the planning/learning approaches. However, experienced human
players easily win the majority of the matches against the heuristic planning
AIs. We, therefore, surmise that Tetris Link is more difficult than expected.
We offer our findings to the community as a challenge to improve upon.
| [
{
"version": "v1",
"created": "Wed, 1 Apr 2020 12:25:36 GMT"
}
] | 1,585,785,600,000 | [
[
"Muller-Brockhausen",
"Matthias",
""
],
[
"Preuss",
"Mike",
""
],
[
"Plaat",
"Aske",
""
]
] |
2004.00425 | H\'ector Cancela | Joaqu\'in Vel\'azquez, H\'ector Cancela, Pedro Pi\~neyro | A hybrid optimization procedure for solving a tire curing scheduling
problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses a lot-sizing and scheduling problem variant arising from
the study of the curing process of a tire factory. The aim is to find the
minimum makespan needed for producing enough tires to meet the demand
requirements on time, considering the availability and compatibility of
different resources involved. To solve this problem, we suggest a hybrid
approach that consists in first applying a heuristic to obtain an estimated
value of the makespan and then solving a mathematical model to determine the
minimum value. We note that the size of the model (number of variables and
constraints) depends significantly on the estimated makespan. Extensive
numerical experiments over different instances based on real data are presented
to evaluate the effectiveness of the hybrid procedure proposed. From the
results obtained we can note that the hybrid approach is able to achieve the
optimal makespan for many of the instances, even large ones, since the results
provided by the heuristic allow to reduce significantly the size of the
mathematical model.
| [
{
"version": "v1",
"created": "Sun, 29 Mar 2020 18:37:02 GMT"
}
] | 1,585,785,600,000 | [
[
"Velázquez",
"Joaquín",
""
],
[
"Cancela",
"Héctor",
""
],
[
"Piñeyro",
"Pedro",
""
]
] |
2004.00427 | Movses Musaelian | Movses Musaelian, Anane Boateng, Md Zakirul Alam Bhuiyan | A Semi-Dynamic Bus Routing Infrastructure based on MBTA Bus Data | 22 pages, 9 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transportation is quickly evolving in the emerging smart city ecosystem with
personalized ride sharing services quickly advancing. Yet, the public bus
infrastructure has been slow to respond to these trends. With our research, we
propose a semi-dynamic bus routing framework that is data-driven and responsive
to relevant parameters in bus transport. We use newly published bus event data
from a bus line in Boston and several algorithmic heuristics to create this
framework and demonstrate the capabilities and results. We find that this
approach yields a very promising routing infrastructure that is smarter and
more dynamic than the existing system.
| [
{
"version": "v1",
"created": "Sun, 29 Mar 2020 13:07:37 GMT"
}
] | 1,585,785,600,000 | [
[
"Musaelian",
"Movses",
""
],
[
"Boateng",
"Anane",
""
],
[
"Bhuiyan",
"Md Zakirul Alam",
""
]
] |
2004.00963 | Luc Libralesso | Luc Libralesso, Florian Fontan | An anytime tree search algorithm for the 2018 ROADEF/EURO challenge
glass cutting problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we present the anytime tree search algorithm we designed for
the 2018 ROADEF/EURO challenge glass cutting problem proposed by the French
company Saint-Gobain. The resulting program was ranked first among 64
participants. Its key components are: a new search algorithm called Memory
Bounded A* (MBA*) with guide functions, a symmetry breaking strategy, and a
pseudo-dominance rule. We perform a comprehensive study of these components
showing that each of them contributes to the algorithm global performances. In
addition, we designed a second tree search algorithm fully based on the
pseudo-dominance rule and dedicated to some of the challenge instances with
strong precedence constraints. On these instances, it finds the best-known
solutions very quickly.
| [
{
"version": "v1",
"created": "Thu, 2 Apr 2020 12:51:26 GMT"
}
] | 1,585,872,000,000 | [
[
"Libralesso",
"Luc",
""
],
[
"Fontan",
"Florian",
""
]
] |
2004.00980 | Anssi Kanervisto | Anssi Kanervisto, Christian Scheller, Ville Hautam\"aki | Action Space Shaping in Deep Reinforcement Learning | To appear in IEEE Conference on Games 2020. Experiment code is
available at https://github.com/Miffyli/rl-action-space-shaping | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning (RL) has been successful in training agents in various
learning environments, including video-games. However, such work modifies and
shrinks the action space from the game's original. This is to avoid trying
"pointless" actions and to ease the implementation. Currently, this is mostly
done based on intuition, with little systematic research supporting the design
decisions. In this work, we aim to gain insight on these action space
modifications by conducting extensive experiments in video-game environments.
Our results show how domain-specific removal of actions and discretization of
continuous actions can be crucial for successful learning. With these insights,
we hope to ease the use of RL in new environments, by clarifying what
action-spaces are easy to learn.
| [
{
"version": "v1",
"created": "Thu, 2 Apr 2020 13:25:55 GMT"
},
{
"version": "v2",
"created": "Tue, 26 May 2020 09:25:59 GMT"
}
] | 1,590,537,600,000 | [
[
"Kanervisto",
"Anssi",
""
],
[
"Scheller",
"Christian",
""
],
[
"Hautamäki",
"Ville",
""
]
] |
2004.00981 | Anssi Kanervisto | Anssi Kanervisto, Joonas Pussinen, Ville Hautam\"aki | Benchmarking End-to-End Behavioural Cloning on Video Games | To appear in IEEE Conference on Games 2020. Experiment code available
at https://github.com/joonaspu/video-game-behavioural-cloning and
https://github.com/joonaspu/ViControl | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Behavioural cloning, where a computer is taught to perform a task based on
demonstrations, has been successfully applied to various video games and
robotics tasks, with and without reinforcement learning. This also includes
end-to-end approaches, where a computer plays a video game like humans do: by
looking at the image displayed on the screen, and sending keystrokes to the
game. As a general approach to playing video games, this has many inviting
properties: no need for specialized modifications to the game, no lengthy
training sessions and the ability to re-use the same tools across different
games. However, related work includes game-specific engineering to achieve the
results. We take a step towards a general approach and study the general
applicability of behavioural cloning on twelve video games, including six
modern video games (published after 2010), by using human demonstrations as
training data. Our results show that these agents cannot match humans in raw
performance but do learn basic dynamics and rules. We also demonstrate how the
quality of the data matters, and how recording data from humans is subject to a
state-action mismatch, due to human reflexes.
| [
{
"version": "v1",
"created": "Thu, 2 Apr 2020 13:31:51 GMT"
},
{
"version": "v2",
"created": "Mon, 18 May 2020 13:50:11 GMT"
}
] | 1,589,846,400,000 | [
[
"Kanervisto",
"Anssi",
""
],
[
"Pussinen",
"Joonas",
""
],
[
"Hautamäki",
"Ville",
""
]
] |
2004.01223 | Ramtin Keramati | Ramtin Keramati, Emma Brunskill | Value Driven Representation for Human-in-the-Loop Reinforcement Learning | null | UMAP 2019, 27th ACM Conference on User Modeling, Adaptation and
Personalization | 10.1145/3320435.3320471 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interactive adaptive systems powered by Reinforcement Learning (RL) have many
potential applications, such as intelligent tutoring systems. In such systems
there is typically an external human system designer that is creating,
monitoring and modifying the interactive adaptive system, trying to improve its
performance on the target outcomes. In this paper we focus on algorithmic
foundation of how to help the system designer choose the set of sensors or
features to define the observation space used by reinforcement learning agent.
We present an algorithm, value driven representation (VDR), that can
iteratively and adaptively augment the observation space of a reinforcement
learning agent so that is sufficient to capture a (near) optimal policy. To do
so we introduce a new method to optimistically estimate the value of a policy
using offline simulated Monte Carlo rollouts. We evaluate the performance of
our approach on standard RL benchmarks with simulated humans and demonstrate
significant improvement over prior baselines.
| [
{
"version": "v1",
"created": "Thu, 2 Apr 2020 18:45:45 GMT"
}
] | 1,586,131,200,000 | [
[
"Keramati",
"Ramtin",
""
],
[
"Brunskill",
"Emma",
""
]
] |
2004.01431 | Zina Ibrahim | Zina Ibrahim, Honghan Wu, Richard Dobson | Modeling Rare Interactions in Time Series Data Through Qualitative
Change: Application to Outcome Prediction in Intensive Care Units | 8 pages, 3 figures. Accepted for publication in the European
Conference of Artificial Intelligence (ECAI 2020) | European Conference on Artificial Intelligence 325(2020) 1826-1833 | 10.3233/FAIA200298 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many areas of research are characterised by the deluge of large-scale
highly-dimensional time-series data. However, using the data available for
prediction and decision making is hampered by the current lag in our ability to
uncover and quantify true interactions that explain the outcomes.We are
interested in areas such as intensive care medicine, which are characterised by
i) continuous monitoring of multivariate variables and non-uniform sampling of
data streams, ii) the outcomes are generally governed by interactions between a
small set of rare events, iii) these interactions are not necessarily definable
by specific values (or value ranges) of a given group of variables, but rather,
by the deviations of these values from the normal state recorded over time, iv)
the need to explain the predictions made by the model. Here, while numerous
data mining models have been formulated for outcome prediction, they are unable
to explain their predictions.
We present a model for uncovering interactions with the highest likelihood of
generating the outcomes seen from highly-dimensional time series data.
Interactions among variables are represented by a relational graph structure,
which relies on qualitative abstractions to overcome non-uniform sampling and
to capture the semantics of the interactions corresponding to the changes and
deviations from normality of variables of interest over time. Using the
assumption that similar templates of small interactions are responsible for the
outcomes (as prevalent in the medical domains), we reformulate the discovery
task to retrieve the most-likely templates from the data.
| [
{
"version": "v1",
"created": "Fri, 3 Apr 2020 08:49:40 GMT"
}
] | 1,606,176,000,000 | [
[
"Ibrahim",
"Zina",
""
],
[
"Wu",
"Honghan",
""
],
[
"Dobson",
"Richard",
""
]
] |
2004.01768 | Michael Cook | Michael Cook | Generative Forensics: Procedural Generation and Information Games | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Procedural generation is used across game design to achieve a wide variety of
ends, and has led to the creation of several game subgenres by injecting
variance, surprise or unpredictability into otherwise static designs.
Information games are a type of mystery game in which the player is tasked with
gathering knowledge and developing an understanding of an event or system.
Their reliance on player knowledge leaves them vulnerable to spoilers and hard
to replay. In this paper we introduce the notion of generative forensics games,
a subgenre of information games that challenge the player to understand the
output of a generative system. We introduce information games, show how
generative forensics develops the idea, report on two prototype games we
created, and evaluate our work on generative forensics so far from a player and
a designer perspective.
| [
{
"version": "v1",
"created": "Fri, 3 Apr 2020 20:51:16 GMT"
}
] | 1,586,217,600,000 | [
[
"Cook",
"Michael",
""
]
] |
2004.02275 | Minh Ho\`ang H\`a | Minh Ho\`ang H\`a and Lam Vu and Duy Manh Vu | The two-echelon routing problem with truck and drones | 29 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study novel variants of the well-known two-echelon vehicle
routing problem in which a truck works on the first echelon to transport
parcels and a fleet of drones to intermediate depots while in the second
echelon, the drones are used to deliver parcels from intermediate depots to
customers. The objective is to minimize the completion time instead of the
transportation cost as in classical 2-echelon vehicle routing problems.
Depending on the context, a drone can be launched from the truck at an
intermediate depot once (single trip drone) or several times (multiple trip
drone). Mixed Integer Linear Programming (MILP) models are first proposed to
formulate mathematically the problems and solve to optimality small-size
instances. To handle larger instances, a metaheuristic based on the idea of
Greedy Randomized Adaptive Search Procedure (GRASP) is introduced. Experimental
results obtained on instances of different contexts are reported and analyzed.
| [
{
"version": "v1",
"created": "Sun, 5 Apr 2020 18:33:16 GMT"
}
] | 1,586,217,600,000 | [
[
"Hà",
"Minh Hoàng",
""
],
[
"Vu",
"Lam",
""
],
[
"Vu",
"Duy Manh",
""
]
] |
2004.02304 | Gordana Dodig Crnkovic | Gordana Dodig-Crnkovic | Morphological Computation and Learning to Learn In Natural Intelligent
Systems And AI | 5 pages, no figures, AISB 2020 conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | At present, artificial intelligence in the form of machine learning is making
impressive progress, especially the field of deep learning (DL) [1]. Deep
learning algorithms have been inspired from the beginning by nature,
specifically by the human brain, in spite of our incomplete knowledge about its
brain function. Learning from nature is a two-way process as discussed in
[2][3][4], computing is learning from neuroscience, while neuroscience is
quickly adopting information processing models. The question is, what can the
inspiration from computational nature at this stage of the development
contribute to deep learning and how much models and experiments in machine
learning can motivate, justify and lead research in neuroscience and cognitive
science and to practical applications of artificial intelligence.
| [
{
"version": "v1",
"created": "Sun, 5 Apr 2020 20:11:42 GMT"
}
] | 1,586,217,600,000 | [
[
"Dodig-Crnkovic",
"Gordana",
""
]
] |
2004.02600 | Ioannis Apostolopoulos | Ioannis Apostolopoulos, Peter Groumpos | Non-invasive modelling methodology for the diagnosis of Coronary Artery
Disease using Fuzzy Cognitive Maps | 13 pages, Manuscript Submitted for Peer-Review, Pre-Print | Journal Computer Methods in Biomechanics and Biomedical
Engineering. 2020. p. 1-9 | 10.1080/10255842.2020.1768534 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Cardiovascular Diseases (CVD) and strokes produce immense health and economic
burdens globally. Coronary Artery Disease (CAD) is the most common type of
cardiovascular disease. Coronary Angiography, which is an invasive treatment,
is also the standard procedure for diagnosing CAD. In this work, we illustrate
a Medical Decision Support System for the prediction of Coronary Artery Disease
(CAD) utilizing Fuzzy Cognitive Maps (FCMs). FCMs are a promising modeling
methodology, based on human knowledge, capable of dealing with ambiguity and
uncertainty, and learning how to adapt to the unknown or changing environment.
The newly proposed MDSS is developed using the basic notions of Fuzzy Logic and
Fuzzy Cognitive Maps, with some adjustments to improve the results. The
proposed model, tested on a labelled CAD dataset of 303 patients, obtains an
accuracy of 78.2% outmatching several state-of-the-art classification
algorithms.
| [
{
"version": "v1",
"created": "Thu, 2 Apr 2020 15:10:31 GMT"
}
] | 1,590,105,600,000 | [
[
"Apostolopoulos",
"Ioannis",
""
],
[
"Groumpos",
"Peter",
""
]
] |
2004.02603 | Florian Fontan | Florian Fontan, Luc Libralesso | An anytime tree search algorithm for two-dimensional two- and
three-staged guillotine packing problems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | [libralesso_anytime_2020] proposed an anytime tree search algorithm for the
2018 ROADEF/EURO challenge glass cutting problem
(https://www.roadef.org/challenge/2018/en/index.php). The resulting program was
ranked first among 64 participants. In this article, we generalize it and show
that it is not only effective for the specific problem it was originally
designed for, but is also very competitive and even returns state-of-the-art
solutions on a large variety of Cutting and Packing problems from the
literature. We adapted the algorithm for two-dimensional Bin Packing, Multiple
Knapsack, and Strip Packing Problems, with two- or three-staged exact or
non-exact guillotine cuts, the orientation of the first cut being imposed or
not, and with or without item rotation. The combination of efficiency, ability
to provide good solutions fast, simplicity and versatility makes it
particularly suited for industrial applications, which require quickly
developing algorithms implementing several business-specific constraints. The
algorithm is implemented in a new software package called PackingSolver.
| [
{
"version": "v1",
"created": "Thu, 2 Apr 2020 13:41:07 GMT"
},
{
"version": "v2",
"created": "Mon, 20 Apr 2020 15:49:14 GMT"
}
] | 1,587,427,200,000 | [
[
"Fontan",
"Florian",
""
],
[
"Libralesso",
"Luc",
""
]
] |
2004.02614 | Sam Vente | Sam Vente (1), Angelika Kimmig (1), Alun Preece (1), Federico Cerutti
(2) ((1) Cardiff University, (2) University of Brescia) | The current state of automated negotiation theory: a literature review | pre-print. New version fixes mistake in title | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Automated negotiation can be an efficient method for resolving conflict and
redistributing resources in a coalition setting. Automated negotiation has
already seen increased usage in fields such as e-commerce and power
distribution in smart girds, and recent advancements in opponent modelling have
proven to deliver better outcomes. However, significant barriers to more
widespread adoption remain, such as lack of predictable outcome over time and
user trust. Additionally, there have been many recent advancements in the field
of reasoning about uncertainty, which could help alleviate both those problems.
As there is no recent survey on these two fields, and specifically not on their
possible intersection we aim to provide such a survey here.
| [
{
"version": "v1",
"created": "Mon, 30 Mar 2020 20:27:20 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Apr 2020 10:17:46 GMT"
}
] | 1,588,032,000,000 | [
[
"Vente",
"Sam",
"",
"Cardiff University"
],
[
"Kimmig",
"Angelika",
"",
"Cardiff University"
],
[
"Preece",
"Alun",
"",
"Cardiff University"
],
[
"Cerutti",
"Federico",
"",
"University of Brescia"
]
] |
2004.02746 | Dongdong Wu | Dongdong Wu and Zijing Liu and Yongchuan Tang | A new approach for generation of generalized basic probability
assignment in the evidence theory | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The process of information fusion needs to deal with a large number of
uncertain information with multi-source, heterogeneity, inaccuracy,
unreliability, and incompleteness. In practical engineering applications,
Dempster-Shafer evidence theory is widely used in multi-source information
fusion owing to its effectiveness in data fusion. Information sources have an
important impact on multi-source information fusion in an environment of
complex, unstable, uncertain, and incomplete characteristics. To address
multi-source information fusion problem, this paper considers the situation of
uncertain information modeling from the closed world to the open world
assumption and studies the generation of basic probability assignment (BPA)
with incomplete information. In this paper, a new method is proposed to
generate generalized basic probability assignment (GBPA) based on the
triangular fuzzy number model under the open world assumption. The proposed
method can not only be used in different complex environments simply and
flexibly, but also have less information loss in information processing.
Finally, a series of comprehensive experiments basing on the UCI data sets are
used to verify the rationality and superiority of the proposed method.
| [
{
"version": "v1",
"created": "Mon, 6 Apr 2020 15:40:35 GMT"
}
] | 1,586,217,600,000 | [
[
"Wu",
"Dongdong",
""
],
[
"Liu",
"Zijing",
""
],
[
"Tang",
"Yongchuan",
""
]
] |
2004.04000 | Pablo Barros | Pablo Barros, Ana Tanevska, Alessandra Sciutti | Learning from Learners: Adapting Reinforcement Learning Agents to be
Competitive in a Card Game | Submitted to ICPR2020 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Learning how to adapt to complex and dynamic environments is one of the most
important factors that contribute to our intelligence. Endowing artificial
agents with this ability is not a simple task, particularly in competitive
scenarios. In this paper, we present a broad study on how popular reinforcement
learning algorithms can be adapted and implemented to learn and to play a
real-world implementation of a competitive multiplayer card game. We propose
specific training and validation routines for the learning agents, in order to
evaluate how the agents learn to be competitive and explain how they adapt to
each others' playing style. Finally, we pinpoint how the behavior of each agent
derives from their learning style and create a baseline for future research on
this scenario.
| [
{
"version": "v1",
"created": "Wed, 8 Apr 2020 14:11:05 GMT"
}
] | 1,586,390,400,000 | [
[
"Barros",
"Pablo",
""
],
[
"Tanevska",
"Ana",
""
],
[
"Sciutti",
"Alessandra",
""
]
] |
2004.04376 | Hongzhi Wang | Hongzhi Wang, Bozhou Chen, Yueyang Xu, Kaixin Zhang and Shengwen Zheng | ConsciousControlFlow(CCF): A Demonstration for conscious Artificial
Intelligence | 10 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this demo, we present ConsciousControlFlow(CCF), a prototype system to
demonstrate conscious Artificial Intelligence (AI). The system is based on the
computational model for consciousness and the hierarchy of needs. CCF supports
typical scenarios to show the behaviors and the mental activities of conscious
AI. We demonstrate that CCF provides a useful tool for effective machine
consciousness demonstration and human behavior study assistance.
| [
{
"version": "v1",
"created": "Thu, 9 Apr 2020 06:28:26 GMT"
},
{
"version": "v2",
"created": "Sat, 6 Feb 2021 02:43:24 GMT"
}
] | 1,612,828,800,000 | [
[
"Wang",
"Hongzhi",
""
],
[
"Chen",
"Bozhou",
""
],
[
"Xu",
"Yueyang",
""
],
[
"Zhang",
"Kaixin",
""
],
[
"Zheng",
"Shengwen",
""
]
] |
2004.05268 | Benjamin Goertzel | Ben Goertzel | Combinatorial Decision Dags: A Natural Computational Model for General
Intelligence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A novel computational model (CoDD) utilizing combinatory logic to create
higher-order decision trees is presented. A theoretical analysis of general
intelligence in terms of the formal theory of pattern recognition and pattern
formation is outlined, and shown to take especially natural form in the case
where patterns are expressed in CoDD language. Relationships between logical
entropy and algorithmic information, and Shannon entropy and runtime
complexity, are shown to be elucidated by this approach. Extension to the
quantum computing case is also briefly discussed.
| [
{
"version": "v1",
"created": "Sat, 11 Apr 2020 00:23:35 GMT"
}
] | 1,586,822,400,000 | [
[
"Goertzel",
"Ben",
""
]
] |
2004.05269 | Benjamin Goertzel | Ben Goertzel | Grounding Occam's Razor in a Formal Theory of Simplicity | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A formal theory of simplicity is introduced, in the context of a
"combinational" computation model that views computation as comprising the
iterated transformational and compositional activity of a population of agents
upon each other. Conventional measures of simplicity in terms of algorithmic
information etc. are shown to be special cases of a broader understanding of
the core "symmetry" properties constituting what is defined here as a
Compositional Simplicity Measure (CoSM).
This theory of CoSMs is extended to a theory of CoSMOS (Combinational
Simplicity Measure Operating Sets) which involve multiple simplicity measures
utilized together. Given a vector of simplicity measures, an entity is
associated not with an individual simplicity value but with a "simplicity
bundles" of Pareto-optimal simplicity-value vectors.
CoSMs and CoSMOS are then used as a foundation for a theory of pattern and
multipattern, and a theory of hierarchy and heterarchy in systems of patterns.
A formalization of the cognitive-systems notion of a "coherent dual network"
interweaving hierarchy and heterarchy in a consistent way is presented.
The high level end result of this investigation is to re-envision Occam's
Razor as something like: When in doubt, prefer hypotheses whose simplicity
bundles are Pareto optimal, partly because doing so both permits and benefits
from the construction of coherent dual networks comprising coordinated and
consistent multipattern hierarchies and heterarchies.
| [
{
"version": "v1",
"created": "Sat, 11 Apr 2020 00:26:56 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Sep 2020 01:34:38 GMT"
}
] | 1,599,177,600,000 | [
[
"Goertzel",
"Ben",
""
]
] |
2004.05499 | Julian Yarkony | Naveed Haghani, Claudio Contardo, Julian Yarkony | Relaxed Dual Optimal Inequalities for Relaxed Columns: with Application
to Vehicle Routing | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the problem of accelerating column generation for set cover
problems in which we relax the state space of the columns to do efficient
pricing. We achieve this by adapting the recently introduced smooth and
flexible dual optimal inequalities (DOI) for use with relaxed columns. Smooth
DOI exploit the observation that similar items are nearly fungible, and hence
should be associated with similarly valued dual variables. Flexible DOI exploit
the observation that the change in cost of a column induced by removing an item
can be bounded. We adapt these DOI to the problem of capacitated vehicle
routing in the context of ng-route relaxations. We demonstrate significant
speed ups on a benchmark data set, while provably not weakening the relaxation.
| [
{
"version": "v1",
"created": "Sat, 11 Apr 2020 22:28:32 GMT"
}
] | 1,586,822,400,000 | [
[
"Haghani",
"Naveed",
""
],
[
"Contardo",
"Claudio",
""
],
[
"Yarkony",
"Julian",
""
]
] |
2004.05920 | Tatiana Urazaeva | Tatiana Urazaeva | Game-theoretic applications of a relational risk model | 14 pages, 5 figures, 18 bibliographic links | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The report suggests the concept of risk, outlining two mathematical
structures necessary for risk genesis: the set of outcomes and, in a general
case, partial order of preference on it. It is shown that this minimum partial
order should constitute the structure of a semilattice. In some cases, there
should be a system of semilattices nested in a certain way. On this basis, the
classification of risk theory tasks is given in the context of specialization
of mathematical knowledge. In other words, we are talking about the development
of a new rela-tional risk theory. The problem of political decision making in
game-theoretic formulation in terms of having partial order of preference on
the set of outcomes for each par-ticipant of the game forming a certain system
of nested semilattices is consid-ered as an example of a relational risk
concept implementation. Solutions to the problem obtained through the use of
various optimality principles are investi-gated.
| [
{
"version": "v1",
"created": "Thu, 9 Apr 2020 18:54:58 GMT"
}
] | 1,586,822,400,000 | [
[
"Urazaeva",
"Tatiana",
""
]
] |
2004.06213 | Nibraas Khan | Nibraas Khan and Joshua Phillips | Combined Model for Partially-Observable and Non-Observable Task
Switching: Solving Hierarchical Reinforcement Learning Problems Statically
and Dynamically with Transfer Learning | substantial text overlap with arXiv:1911.10425 which is the same as
this paper. It was meant to be a revision of that paper, but I mistakenly
submitted it as a new paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An integral function of fully autonomous robots and humans is the ability to
focus attention on a few relevant percepts to reach a certain goal while
disregarding irrelevant percepts. Humans and animals rely on the interactions
between the Pre-Frontal Cortex (PFC) and the Basal Ganglia (BG) to achieve this
focus called Working Memory (WM). The Working Memory Toolkit (WMtk) was
developed based on a computational neuroscience model of this phenomenon with
Temporal Difference (TD) Learning for autonomous systems. Recent adaptations of
the toolkit either utilize Abstract Task Representations (ATRs) to solve
Non-Observable (NO) tasks or storage of past input features to solve
Partially-Observable (PO) tasks, but not both. We propose a new model,
PONOWMtk, which combines both approaches, ATRs and input storage, with a static
or dynamic number of ATRs. The results of our experiments show that PONOWMtk
performs effectively for tasks that exhibit PO, NO, or both properties.
| [
{
"version": "v1",
"created": "Mon, 13 Apr 2020 21:44:54 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Apr 2020 18:45:51 GMT"
}
] | 1,587,686,400,000 | [
[
"Khan",
"Nibraas",
""
],
[
"Phillips",
"Joshua",
""
]
] |
2004.07017 | Jonatas Chagas | Jonatas B. C. Chagas and Markus Wagner | Ants can orienteer a thief in their robbery | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Thief Orienteering Problem (ThOP) is a multi-component problem that
combines features of two classic combinatorial optimization problems:
Orienteering Problem and Knapsack Problem. The ThOP is challenging due to the
given time constraint and the interaction between its components. We propose an
Ant Colony Optimization algorithm together with a new packing heuristic to deal
individually and interactively with problem components. Our approach
outperforms existing work on more than 90% of the benchmarking instances, with
an average improvement of over 300%.
| [
{
"version": "v1",
"created": "Wed, 15 Apr 2020 11:30:37 GMT"
},
{
"version": "v2",
"created": "Fri, 29 May 2020 14:14:58 GMT"
},
{
"version": "v3",
"created": "Sat, 29 Aug 2020 11:43:24 GMT"
}
] | 1,598,918,400,000 | [
[
"Chagas",
"Jonatas B. C.",
""
],
[
"Wagner",
"Markus",
""
]
] |
2004.07027 | Francesco Fuggitti | Francesco Fuggitti | FOND Planning for LTLf and PLTLf Goals | Extract of MSc Thesis, 35 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this report, we will define a new approach to the problem of non
deterministic planning for extended temporal goals. In particular, we will give
a solution to this problem reducing it to a fully observable non deterministic
(FOND) planning problem and taking advantage of the LTLfToDFA tool. First of
all, we will introduce the main idea and motivations supporting our approach.
Then, we will give some preliminaries explaining the Planning Domain Definition
Language (PDDL) language and the FOND planning problem formally. After that, we
will illustrate our FOND4LTLfPLTLf (also available online) approach with the
encoding of temporal goals into a PDDL domain and problem. Finally, we will
present some of the results obtained through the application of the proposed
solution.
| [
{
"version": "v1",
"created": "Wed, 15 Apr 2020 12:04:02 GMT"
}
] | 1,586,995,200,000 | [
[
"Fuggitti",
"Francesco",
""
]
] |
2004.07822 | Mehrdad Zakershahrak | Mehrdad Zakershahrak, Shashank Rao Marpally, Akshay Sharma, Ze Gong
and Yu Zhang | Order Matters: Generating Progressive Explanations for Planning Tasks in
Human-Robot Teaming | arXiv admin note: text overlap with arXiv:1902.00604 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Prior work on generating explanations in a planning and decision-making
context has focused on providing the rationale behind an AI agent's decision
making. While these methods provide the right explanations from the explainer's
perspective, they fail to heed the cognitive requirement of understanding an
explanation from the explainee's (the human's) perspective. In this work, we
set out to address this issue by first considering the influence of information
order in an explanation, or the progressiveness of explanations. Intuitively,
progression builds later concepts on previous ones and is known to contribute
to better learning. In this work, we aim to investigate similar effects during
explanation generation when an explanation is broken into multiple parts that
are communicated sequentially. The challenge here lies in modeling the humans'
preferences for information order in receiving such explanations to assist
understanding. Given this sequential process, a formulation based on goal-based
MDP for generating progressive explanations is presented. The reward function
of this MDP is learned via inverse reinforcement learning based on explanations
that are retrieved via human subject studies. We first evaluated our approach
on a scavenger-hunt domain to demonstrate its effectively in capturing the
humans' preferences. Upon analyzing the results, it revealed something more
fundamental: the preferences arise strongly from both domain dependent and
independence features. The correlation with domain independent features pushed
us to verify this result further in an escape room domain. Results confirmed
our hypothesis that the process of understanding an explanation was a dynamic
process. The human preference that reflected this aspect corresponded exactly
to the progression for knowledge assimilation hidden deeper in our cognitive
process.
| [
{
"version": "v1",
"created": "Thu, 16 Apr 2020 00:17:02 GMT"
},
{
"version": "v2",
"created": "Sat, 17 Oct 2020 01:15:40 GMT"
}
] | 1,603,152,000,000 | [
[
"Zakershahrak",
"Mehrdad",
""
],
[
"Marpally",
"Shashank Rao",
""
],
[
"Sharma",
"Akshay",
""
],
[
"Gong",
"Ze",
""
],
[
"Zhang",
"Yu",
""
]
] |
2004.08128 | Beren Millidge Mr | Beren Millidge, Alexander Tschantz, Christopher L Buckley | Whence the Expected Free Energy? | 24 pages, 0 figures. Reuploaded to correct typos in the original.
Update 05-07-20 -- minor corrections. Update 28-09-20 -- Final version
accepted by Neural Computation | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Expected Free Energy (EFE) is a central quantity in the theory of active
inference. It is the quantity that all active inference agents are mandated to
minimize through action, and its decomposition into extrinsic and intrinsic
value terms is key to the balance of exploration and exploitation that active
inference agents evince. Despite its importance, the mathematical origins of
this quantity and its relation to the Variational Free Energy (VFE) remain
unclear. In this paper, we investigate the origins of the EFE in detail and
show that it is not simply "the free energy in the future". We present a
functional that we argue is the natural extension of the VFE, but which
actively discourages exploratory behaviour, thus demonstrating that exploration
does not directly follow from free energy minimization into the future. We then
develop a novel objective, the Free-Energy of the Expected Future (FEEF), which
possesses both the epistemic component of the EFE as well as an intuitive
mathematical grounding as the divergence between predicted and desired futures.
| [
{
"version": "v1",
"created": "Fri, 17 Apr 2020 09:06:56 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Apr 2020 10:19:53 GMT"
},
{
"version": "v3",
"created": "Sun, 10 May 2020 15:38:57 GMT"
},
{
"version": "v4",
"created": "Sun, 5 Jul 2020 18:41:09 GMT"
},
{
"version": "v5",
"created": "Mon, 28 Sep 2020 21:04:16 GMT"
}
] | 1,601,424,000,000 | [
[
"Millidge",
"Beren",
""
],
[
"Tschantz",
"Alexander",
""
],
[
"Buckley",
"Christopher L",
""
]
] |
2004.08672 | Shiqi Zhang | Shiqi Zhang, Piyush Khandelwal, Peter Stone | iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on
Robots | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robot sequential decision-making in the real world is a challenge because it
requires the robots to simultaneously reason about the current world state and
dynamics, while planning actions to accomplish complex tasks. On the one hand,
declarative languages and reasoning algorithms well support representing and
reasoning with commonsense knowledge. But these algorithms are not good at
planning actions toward maximizing cumulative reward over a long, unspecified
horizon. On the other hand, probabilistic planning frameworks, such as Markov
decision processes (MDPs) and partially observable MDPs (POMDPs), well support
planning to achieve long-term goals under uncertainty. But they are
ill-equipped to represent or reason about knowledge that is not directly
related to actions.
In this article, we present a novel algorithm, called iCORPP, to
simultaneously estimate the current world state, reason about world dynamics,
and construct task-oriented controllers. In this process, robot decision-making
problems are decomposed into two interdependent (smaller) subproblems that
focus on reasoning to "understand the world" and planning to "achieve the goal"
respectively. Contextual knowledge is represented in the reasoning component,
which makes the planning component epistemic and enables active information
gathering. The developed algorithm has been implemented and evaluated both in
simulation and on real robots using everyday service tasks, such as indoor
navigation, dialog management, and object delivery. Results show significant
improvements in scalability, efficiency, and adaptiveness, compared to
competitive baselines including handcrafted action policies.
| [
{
"version": "v1",
"created": "Sat, 18 Apr 2020 17:46:59 GMT"
},
{
"version": "v2",
"created": "Sun, 1 Oct 2023 00:56:27 GMT"
}
] | 1,696,291,200,000 | [
[
"Zhang",
"Shiqi",
""
],
[
"Khandelwal",
"Piyush",
""
],
[
"Stone",
"Peter",
""
]
] |
2004.09507 | Antonio Lieto | Laura Giordano, Valentina Gliozzi, Antonio Lieto, Nicola Olivetti,
Gian Luca Pozzato | Reasoning about Typicality and Probabilities in Preferential Description
Logics | 17 pages. arXiv admin note: text overlap with arXiv:1811.02366 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we describe preferential Description Logics of typicality, a
nonmonotonic extension of standard Description Logics by means of a typicality
operator T allowing to extend a knowledge base with inclusions of the form T(C)
v D, whose intuitive meaning is that normally/typically Cs are also Ds. This
extension is based on a minimal model semantics corresponding to a notion of
rational closure, built upon preferential models. We recall the basic concepts
underlying preferential Description Logics. We also present two extensions of
the preferential semantics: on the one hand, we consider probabilistic
extensions, based on a distributed semantics that is suitable for tackling the
problem of commonsense concept combination, on the other hand, we consider
other strengthening of the rational closure semantics and construction to avoid
the so-called blocking of property inheritance problem.
| [
{
"version": "v1",
"created": "Mon, 20 Apr 2020 14:50:31 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Apr 2020 16:15:31 GMT"
}
] | 1,587,686,400,000 | [
[
"Giordano",
"Laura",
""
],
[
"Gliozzi",
"Valentina",
""
],
[
"Lieto",
"Antonio",
""
],
[
"Olivetti",
"Nicola",
""
],
[
"Pozzato",
"Gian Luca",
""
]
] |
2004.10014 | Weizi Li | Weizi Li and Jan M. Allbeck | Imperatives for Virtual Humans | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Seemingly since the inception of virtual humans, there has been an effort to
make their behaviors more natural and human-like. In additions to improving
movement's visual quality, there has been considerable research focused on
creating more intelligent virtual characters. This paper presents a framework
inspired by natural language constructs that aims to author more reasonable
virtual human behaviors using structured English input. We focus mainly on
object types and properties, quantifiers, determiners, and spatial relations.
The framework provides a natural, flexible authoring system for simulating
human behaviors.
| [
{
"version": "v1",
"created": "Sun, 19 Apr 2020 12:47:15 GMT"
}
] | 1,587,513,600,000 | [
[
"Li",
"Weizi",
""
],
[
"Allbeck",
"Jan M.",
""
]
] |
2004.11858 | Amir Hosein Afshar Sedigh | Amir Hosein Afshar Sedigh, Martin K. Purvis, Bastin Tony Roy
Savarimuthu, Christopher K Frantz, and Maryam A. Purvis | Impact of different belief facets on agents' decision -- a refined
cognitive architecture to model the interaction between organisations'
institutional characteristics and agents' behaviour | Submitted to COINE 2020 workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a conceptual refinement of agent cognitive architecture
inspired from the beliefs-desires-intentions (BDI) and the theory of planned
behaviour (TPB) models, with an emphasis on different belief facets. This
enables us to investigate the impact of personality and the way that an agent
weights its internal beliefs and social sanctions on an agent's actions. The
study also uses the concept of cognitive dissonance associated with the
fairness of institutions to investigate the agents' behaviour. To showcase our
model, we simulate two historical long-distance trading societies, namely
Armenian merchants of New-Julfa and the English East India Company. The results
demonstrate the importance of internal beliefs of agents as a pivotal aspect
for following institutional rules.
| [
{
"version": "v1",
"created": "Fri, 24 Apr 2020 17:06:32 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Aug 2020 07:04:24 GMT"
}
] | 1,597,017,600,000 | [
[
"Sedigh",
"Amir Hosein Afshar",
""
],
[
"Purvis",
"Martin K.",
""
],
[
"Savarimuthu",
"Bastin Tony Roy",
""
],
[
"Frantz",
"Christopher K",
""
],
[
"Purvis",
"Maryam A.",
""
]
] |
2004.12193 | Chi Zhang | Wenhe Zhang, Chi Zhang, Yixin Zhu, Song-Chun Zhu | Machine Number Sense: A Dataset of Visual Arithmetic Problems for
Abstract and Relational Reasoning | AAAI 2020 Oral. Project page:
https://sites.google.com/view/number-sense/home Code:
https://github.com/zwh1999anne/Machine-Number-Sense-Dataset Dataset:
https://drive.google.com/file/d/17KuL8KOIDAeRL-lD418oiDEm8bE6TEFb/view | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As a comprehensive indicator of mathematical thinking and intelligence, the
number sense (Dehaene 2011) bridges the induction of symbolic concepts and the
competence of problem-solving. To endow such a crucial cognitive ability to
machine intelligence, we propose a dataset, Machine Number Sense (MNS),
consisting of visual arithmetic problems automatically generated using a
grammar model--And-Or Graph (AOG). These visual arithmetic problems are in the
form of geometric figures: each problem has a set of geometric shapes as its
context and embedded number symbols. Solving such problems is not trivial; the
machine not only has to recognize the number, but also to interpret the number
with its contexts, shapes, and relations (e.g., symmetry) together with proper
operations. We benchmark the MNS dataset using four predominant neural network
models as baselines in this visual reasoning task. Comprehensive experiments
show that current neural-network-based models still struggle to understand
number concepts and relational operations. We show that a simple brute-force
search algorithm could work out some of the problems without context
information. Crucially, taking geometric context into account by an additional
perception module would provide a sharp performance gain with fewer search
steps. Altogether, we call for attention in fusing the classic search-based
algorithms with modern neural networks to discover the essential number
concepts in future research.
| [
{
"version": "v1",
"created": "Sat, 25 Apr 2020 17:14:58 GMT"
}
] | 1,588,032,000,000 | [
[
"Zhang",
"Wenhe",
""
],
[
"Zhang",
"Chi",
""
],
[
"Zhu",
"Yixin",
""
],
[
"Zhu",
"Song-Chun",
""
]
] |
2004.12919 | Jeff Clune | Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley and
Jeff Clune | First return, then explore | 47 pages, 14 figures, 4 tables; reorganized sections and modified SI
text extensively; added reference to the published version, changed title to
published title; added reference to published unformatted pdf | Nature 590, 580-586 (2021) | 10.1038/s41586-020-03157-9 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The promise of reinforcement learning is to solve complex sequential decision
problems autonomously by specifying a high-level reward function only. However,
reinforcement learning algorithms struggle when, as is often the case, simple
and intuitive rewards provide sparse and deceptive feedback. Avoiding these
pitfalls requires thoroughly exploring the environment, but creating algorithms
that can do so remains one of the central challenges of the field. We
hypothesise that the main impediment to effective exploration originates from
algorithms forgetting how to reach previously visited states ("detachment") and
from failing to first return to a state before exploring from it
("derailment"). We introduce Go-Explore, a family of algorithms that addresses
these two challenges directly through the simple principles of explicitly
remembering promising states and first returning to such states before
intentionally exploring. Go-Explore solves all heretofore unsolved Atari games
and surpasses the state of the art on all hard-exploration games, with orders
of magnitude improvements on the grand challenges Montezuma's Revenge and
Pitfall. We also demonstrate the practical potential of Go-Explore on a
sparse-reward pick-and-place robotics task. Additionally, we show that adding a
goal-conditioned policy can further improve Go-Explore's exploration efficiency
and enable it to handle stochasticity throughout training. The substantial
performance gains from Go-Explore suggest that the simple principles of
remembering states, returning to them, and exploring from them are a powerful
and general approach to exploration, an insight that may prove critical to the
creation of truly intelligent learning agents.
| [
{
"version": "v1",
"created": "Mon, 27 Apr 2020 16:31:26 GMT"
},
{
"version": "v2",
"created": "Thu, 14 May 2020 18:40:39 GMT"
},
{
"version": "v3",
"created": "Fri, 26 Feb 2021 21:09:35 GMT"
},
{
"version": "v4",
"created": "Sat, 28 Aug 2021 22:59:10 GMT"
},
{
"version": "v5",
"created": "Tue, 31 Aug 2021 03:28:29 GMT"
},
{
"version": "v6",
"created": "Thu, 16 Sep 2021 17:50:10 GMT"
}
] | 1,631,836,800,000 | [
[
"Ecoffet",
"Adrien",
""
],
[
"Huizinga",
"Joost",
""
],
[
"Lehman",
"Joel",
""
],
[
"Stanley",
"Kenneth O.",
""
],
[
"Clune",
"Jeff",
""
]
] |
2004.13477 | Pavel Surynek | Pavel Surynek | Pushing the Envelope: From Discrete to Continuous Movements in
Multi-Agent Path Finding via Lazy Encodings | arXiv admin note: text overlap with arXiv:1903.09820 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-agent path finding in continuous space and time with geometric agents
MAPF$^\mathcal{R}$ is addressed in this paper. The task is to navigate agents
that move smoothly between predefined positions to their individual goals so
that they do not collide. We introduce a novel solving approach for obtaining
makespan optimal solutions called SMT-CBS$^\mathcal{R}$ based on {\em
satisfiability modulo theories} (SMT). The new algorithm combines collision
resolution known from conflict-based search (CBS) with previous generation of
incomplete SAT encodings on top of a novel scheme for selecting decision
variables in a potentially uncountable search space. We experimentally compare
SMT-CBS$^\mathcal{R}$ and previous CCBS algorithm for MAPF$^\mathcal{R}$.
| [
{
"version": "v1",
"created": "Sat, 25 Apr 2020 13:21:32 GMT"
}
] | 1,588,118,400,000 | [
[
"Surynek",
"Pavel",
""
]
] |
2004.13482 | Roger Granada | Roger Granada and Ramon Fraga Pereira and Juarez Monteiro and Leonardo
Amado and Rodrigo C. Barros and Duncan Ruiz and Felipe Meneguzzi | HAPRec: Hybrid Activity and Plan Recognizer | Demo paper of the AAAI 2020 Workshop on Plan, Activity, and Intent
Recognition | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computer-based assistants have recently attracted much interest due to its
applicability to ambient assisted living. Such assistants have to detect and
recognize the high-level activities and goals performed by the assisted human
beings. In this work, we demonstrate activity recognition in an indoor
environment in order to identify the goal towards which the subject of the
video is pursuing. Our hybrid approach combines an action recognition module
and a goal recognition algorithm to identify the ultimate goal of the subject
in the video.
| [
{
"version": "v1",
"created": "Tue, 28 Apr 2020 13:20:14 GMT"
}
] | 1,588,118,400,000 | [
[
"Granada",
"Roger",
""
],
[
"Pereira",
"Ramon Fraga",
""
],
[
"Monteiro",
"Juarez",
""
],
[
"Amado",
"Leonardo",
""
],
[
"Barros",
"Rodrigo C.",
""
],
[
"Ruiz",
"Duncan",
""
],
[
"Meneguzzi",
"Felipe",
""
]
] |
2004.13529 | Juarez Monteiro | Juarez Monteiro, Nathan Gavenski, Roger Granada, Felipe Meneguzzi and
Rodrigo Barros | Augmented Behavioral Cloning from Observation | This paper has been accepted in the International Joint Conference on
Neural Networks 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Imitation from observation is a computational technique that teaches an agent
on how to mimic the behavior of an expert by observing only the sequence of
states from the expert demonstrations. Recent approaches learn the inverse
dynamics of the environment and an imitation policy by interleaving epochs of
both models while changing the demonstration data. However, such approaches
often get stuck into sub-optimal solutions that are distant from the expert,
limiting their imitation effectiveness. We address this problem with a novel
approach that overcomes the problem of reaching bad local minima by exploring:
(I) a self-attention mechanism that better captures global features of the
states; and (ii) a sampling strategy that regulates the observations that are
used for learning. We show empirically that our approach outperforms the
state-of-the-art approaches in four different environments by a large margin.
| [
{
"version": "v1",
"created": "Tue, 28 Apr 2020 13:56:36 GMT"
}
] | 1,588,118,400,000 | [
[
"Monteiro",
"Juarez",
""
],
[
"Gavenski",
"Nathan",
""
],
[
"Granada",
"Roger",
""
],
[
"Meneguzzi",
"Felipe",
""
],
[
"Barros",
"Rodrigo",
""
]
] |
2004.13654 | Stuart Armstrong | Stuart Armstrong and Jan Leike and Laurent Orseau and Shane Legg | Pitfalls of learning a reward function online | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In some agent designs like inverse reinforcement learning an agent needs to
learn its own reward function. Learning the reward function and optimising for
it are typically two different processes, usually performed at different
stages. We consider a continual (``one life'') learning approach where the
agent both learns the reward function and optimises for it at the same time. We
show that this comes with a number of pitfalls, such as deliberately
manipulating the learning process in one direction, refusing to learn,
``learning'' facts already known to the agent, and making decisions that are
strictly dominated (for all relevant reward functions). We formally introduce
two desirable properties: the first is `unriggability', which prevents the
agent from steering the learning process in the direction of a reward function
that is easier to optimise. The second is `uninfluenceability', whereby the
reward-function learning process operates by learning facts about the
environment. We show that an uninfluenceable process is automatically
unriggable, and if the set of possible environments is sufficiently rich, the
converse is true too.
| [
{
"version": "v1",
"created": "Tue, 28 Apr 2020 16:58:58 GMT"
}
] | 1,588,118,400,000 | [
[
"Armstrong",
"Stuart",
""
],
[
"Leike",
"Jan",
""
],
[
"Orseau",
"Laurent",
""
],
[
"Legg",
"Shane",
""
]
] |
2004.13836 | Heerok Banerjee | Heerok Banerjee, V. Ganapathy and V. M. Shenbagaraman | Uncertainty Modelling in Risk-averse Supply Chain Systems Using
Multi-objective Pareto Optimization | 15 pages, 6 Figures, 2 Tables, research article | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the arduous tasks in supply chain modelling is to build robust models
against irregular variations. During the proliferation of time-series analyses
and machine learning models, several modifications were proposed such as
acceleration of the classical levenberg-marquardt algorithm, weight decaying
and normalization, which introduced an algorithmic optimization approach to
this problem. In this paper, we have introduced a novel methodology namely,
Pareto Optimization to handle uncertainties and bound the entropy of such
uncertainties by explicitly modelling them under some apriori assumptions. We
have implemented Pareto Optimization using a genetic approach and compared the
results with classical genetic algorithms and Mixed-Integer Linear Programming
(MILP) models. Our results yields empirical evidence suggesting that Pareto
Optimization can elude such non-deterministic errors and is a formal approach
towards producing robust and reactive supply chain models.
| [
{
"version": "v1",
"created": "Fri, 24 Apr 2020 21:04:25 GMT"
}
] | 1,588,204,800,000 | [
[
"Banerjee",
"Heerok",
""
],
[
"Ganapathy",
"V.",
""
],
[
"Shenbagaraman",
"V. M.",
""
]
] |
2004.14892 | Pranab K. Muhuri Dr. | Prashant K Gupta and Pranab K. Muhuri | An empirical study of computing with words approaches for multi-person
and single-person systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computing with words (CWW) has emerged as a powerful tool for processing the
linguistic information, especially the one generated by human beings. Various
CWW approaches have emerged since the inception of CWW, such as perceptual
computing, extension principle based CWW approach, symbolic method based CWW
approach, and 2-tuple based CWW approach. Furthermore, perceptual computing can
use interval approach (IA), enhanced interval approach (EIA), or Hao-Mendel
approach (HMA), for data processing. There have been numerous works in which
HMA was shown to be better at word modelling than EIA, and EIA better than IA.
But, a deeper study of these works reveals that HMA captures lesser fuzziness
than the EIA or IA. Thus, we feel that EIA is more suited for word modelling in
multi-person systems and HMA for single-person systems (as EIA is an
improvement over IA). Furthermore, another set of works, compared the
performances perceptual computing to the other above said CWW approaches. In
all these works, perceptual computing was shown to be better than other CWW
approaches. However, none of the works tried to investigate the reason behind
this observed better performance of perceptual computing. Also, no comparison
has been performed for scenarios where the inputs are differentially weighted.
Thus, the aim of this work is to empirically establish that EIA is suitable for
multi-person systems and HMA for single-person systems. Another dimension of
this work is also to empirically prove that perceptual computing gives better
performance than other CWW approaches based on extension principle, symbolic
method and 2-tuple especially in scenarios where inputs are differentially
weighted.
| [
{
"version": "v1",
"created": "Thu, 30 Apr 2020 15:45:38 GMT"
}
] | 1,588,291,200,000 | [
[
"Gupta",
"Prashant K",
""
],
[
"Muhuri",
"Pranab K.",
""
]
] |
2004.14933 | Pranab K. Muhuri Dr. | Prashant K Gupta and Pranab K. Muhuri | Perceptual reasoning based solution methodology for linguistic
optimization problems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision making in real-life scenarios may often be modeled as an
optimization problem. It requires the consideration of various attributes like
human preferences and thinking, which constrain achieving the optimal value of
the problem objectives. The value of the objectives may be maximized or
minimized, depending on the situation. Numerous times, the values of these
problem parameters are in linguistic form, as human beings naturally understand
and express themselves using words. These problems are therefore termed as
linguistic optimization problems (LOPs), and are of two types, namely single
objective linguistic optimization problems (SOLOPs) and multi-objective
linguistic optimization problems (MOLOPs). In these LOPs, the value of the
objective function(s) may not be known at all points of the decision space, and
therefore, the objective function(s) as well as problem constraints are linked
by the if-then rules. Tsukamoto inference method has been used to solve these
LOPs; however, it suffers from drawbacks. As, the use of linguistic information
inevitably calls for the utilization of computing with words (CWW), and
therefore, 2-tuple linguistic model based solution methodologies were proposed
for LOPs. However, we found that 2-tuple linguistic model based solution
methodologies represent the semantics of the linguistic information using a
combination of type-1 fuzzy sets and ordinal term sets. As, the semantics of
linguistic information are best modeled using the interval type-2 fuzzy sets,
hence we propose solution methodologies for LOPs based on CWW approach of
perceptual computing, in this paper. The perceptual computing based solution
methodologies use a novel design of CWW engine, called the perceptual reasoning
(PR). PR in the current form is suitable for solving SOLOPs and, hence, we have
also extended it to the MOLOPs.
| [
{
"version": "v1",
"created": "Thu, 30 Apr 2020 16:35:01 GMT"
}
] | 1,588,291,200,000 | [
[
"Gupta",
"Prashant K",
""
],
[
"Muhuri",
"Pranab K.",
""
]
] |
2004.14955 | Pranab K. Muhuri Dr. | Prashant K Gupta and Pranab K. Muhuri | Parallel processor scheduling: formulation as multi-objective linguistic
optimization and solution using Perceptual Reasoning based methodology | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the era of Industry 4.0, the focus is on the minimization of human element
and maximizing the automation in almost all the industrial and manufacturing
establishments. These establishments contain numerous processing systems, which
can execute a number of tasks, in parallel with minimum number of human beings.
This parallel execution of tasks is done in accordance to a scheduling policy.
However, the minimization of human element beyond a certain point is difficult.
In fact, the expertise and experience of a group of humans, called the experts,
becomes imminent to design a fruitful scheduling policy. The aim of the
scheduling policy is to achieve the optimal value of an objective, like
production time, cost, etc. In real-life situations, there are more often than
not, multiple objectives in any parallel processing scenario. Furthermore, the
experts generally provide their opinions, about various scheduling criteria
(pertaining to the scheduling policies) in linguistic terms or words. Word
semantics are best modeled using fuzzy sets (FSs). Thus, all these factors have
motivated us to model the parallel processing scenario as a multi-objective
linguistic optimization problem (MOLOP) and use the novel perceptual reasoning
(PR) based methodology for solving it. We have also compared the results of the
PR based solution methodology with those obtained from the 2-tuple based
solution methodology. PR based solution methodology offers three main
advantages viz., it generates unique recommendations, here the linguistic
recommendations match a codebook word, and also the word model comes before the
word. 2-tuple based solution methodology fails to give all these advantages.
Thus, we feel that our work is novel and will provide directions for the future
research.
| [
{
"version": "v1",
"created": "Thu, 30 Apr 2020 17:04:49 GMT"
}
] | 1,588,291,200,000 | [
[
"Gupta",
"Prashant K",
""
],
[
"Muhuri",
"Pranab K.",
""
]
] |
2005.00868 | Pranab K. Muhuri Dr. | Prashant K Gupta, and Pranab K. Muhuri | Computing With Words for Student Strategy Evaluation in an Examination | null | Granular Computing 4, no. 2 (2019): 167-184 | 10.1007/s41066-018-0109-2 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the framework of Granular Computing (GC), Interval type 2 Fuzzy Sets (IT2
FSs) play a prominent role by facilitating a better representation of uncertain
linguistic information. Perceptual Computing (Per C), a well known computing
with words (CWW) approach, and its various applications have nicely exploited
this advantage. This paper reports a novel Per C based approach for student
strategy evaluation. Examinations are generally oriented to test the subject
knowledge of students. The number of questions that they are able to solve
accurately judges success rates of students in the examinations. However, we
feel that not only the solutions of questions, but also the strategy adopted
for finding those solutions are equally important. More marks should be awarded
to a student, who solves a question with a better strategy compared to a
student, whose strategy is relatively not that good. Furthermore, the students
strategy can be taken as a measure of his or her learning outcome as perceived
by a faculty member. This can help to identify students, whose learning
outcomes are not good, and, thus, can be provided with any relevant help, for
improvement. The main contribution of this paper is to illustrate the use of
CWW for student strategy evaluation and present a comparison of the
recommendations generated by different CWW approaches. CWW provides us with two
major advantages. First, it generates a numeric score for the overall
evaluation of strategy adopted by a student in the examination. This enables
comparison and ranking of the students based on their performances. Second, a
linguistic evaluation describing the student strategy is also obtained from the
system. Both these numeric score and linguistic recommendation are together
used to assess the quality of a students strategy. We found that Per-C
generates unique recommendations in all cases and outperforms other CWW
approaches.
| [
{
"version": "v1",
"created": "Sat, 2 May 2020 15:57:54 GMT"
}
] | 1,593,302,400,000 | [
[
"Gupta",
"Prashant K",
""
],
[
"Muhuri",
"Pranab K.",
""
]
] |
2005.01633 | Veronique Ventos | V\'eronique Ventos, Daniel Braun, Colin Deheeger, Jean Pierre
Desmoulins, Jean Baptiste Fantun, Swann Legras, Alexis Rimbaud, C\'eline
Rouveirol, Henry Soldano and Sol\`ene Th\'epaut | Construction and Elicitation of a Black Box Model in the Game of Bridge | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the problem of building a decision model for a specific bidding
situation in the game of Bridge. We propose the following multi-step
methodology i) Build a set of examples for the decision problem and use
simulations to associate a decision to each example ii) Use supervised
relational learning to build an accurate and readable model iii) Perform a
joint analysis between domain experts and data scientists to improve the
learning language, including the production by experts of a handmade model iv)
Build a better, more readable and accurate model.
| [
{
"version": "v1",
"created": "Mon, 4 May 2020 16:44:45 GMT"
},
{
"version": "v2",
"created": "Mon, 4 Apr 2022 06:34:54 GMT"
}
] | 1,649,116,800,000 | [
[
"Ventos",
"Véronique",
""
],
[
"Braun",
"Daniel",
""
],
[
"Deheeger",
"Colin",
""
],
[
"Desmoulins",
"Jean Pierre",
""
],
[
"Fantun",
"Jean Baptiste",
""
],
[
"Legras",
"Swann",
""
],
[
"Rimbaud",
"Alexis",
""
],
[
"Rouveirol",
"Céline",
""
],
[
"Soldano",
"Henry",
""
],
[
"Thépaut",
"Solène",
""
]
] |
|
2005.02659 | Samira Babalou | Samira Babalou, Birgitta K\"onig-Ries | Towards Building Knowledge by Merging Multiple Ontologies with CoMerger:
A Partitioning-based Approach | A further improved version of this paper will be submitted to the
International Semantic Web Conference (ISWC) 2020 conference. The paper has
23 pages including appendix and 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ontologies are the prime way of organizing data in the Semantic Web. Often,
it is necessary to combine several, independently developed ontologies to
obtain a knowledge graph fully representing a domain of interest. The
complementarity of existing ontologies can be leveraged by merging them.
Existing approaches for ontology merging mostly implement a binary merge.
However, with the growing number and size of relevant ontologies across
domains, scalability becomes a central challenge. A multi-ontology merging
technique offers a potential solution to this problem. We present CoMerger, a
scalable multiple ontologies merging method. For efficient processing, rather
than successively merging complete ontologies pairwise, we group related
concepts across ontologies into partitions and merge first within and then
across those partitions. The experimental results on well-known datasets
confirm the feasibility of our approach and demonstrate its superiority over
binary strategies. A prototypical implementation is freely accessible through a
live web portal.
| [
{
"version": "v1",
"created": "Wed, 6 May 2020 08:45:00 GMT"
}
] | 1,588,809,600,000 | [
[
"Babalou",
"Samira",
""
],
[
"König-Ries",
"Birgitta",
""
]
] |
2005.02863 | Loris Nanni | Lorenzo Mantovan and Loris Nanni | The computerization of archaeology: survey on AI techniques | null | SN Computer Science, 2020 | 10.1007/s42979-020-00286-w | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyses the application of artificial intelligence techniques to
various areas of archaeology and more specifically: a) The use of software
tools as a creative stimulus for the organization of exhibitions; the use of
humanoid robots and holographic displays as guides that interact and involve
museum visitors; b) The analysis of methods for the classification of fragments
found in archaeological excavations and for the reconstruction of ceramics,
with the recomposition of the parts of text missing from historical documents
and epigraphs; c) The cataloguing and study of human remains to understand the
social and historical context of belonging with the demonstration of the
effectiveness of the AI techniques used; d) The detection of particularly
difficult terrestrial archaeological sites with the analysis of the
architectures of the Artificial Neural Networks most suitable for solving the
problems presented by the site; the design of a study for the exploration of
marine archaeological sites, located at depths that cannot be reached by man,
through the construction of a freely explorable 3D version.
| [
{
"version": "v1",
"created": "Tue, 5 May 2020 17:09:48 GMT"
},
{
"version": "v2",
"created": "Tue, 30 Jun 2020 23:50:14 GMT"
}
] | 1,601,337,600,000 | [
[
"Mantovan",
"Lorenzo",
""
],
[
"Nanni",
"Loris",
""
]
] |
2005.02880 | Jessica Hamrick | Eliza Kosoy, Jasmine Collins, David M. Chan, Sandy Huang, Deepak
Pathak, Pulkit Agrawal, John Canny, Alison Gopnik, Jessica B. Hamrick | Exploring Exploration: Comparing Children with RL Agents in Unified
Environments | Published as a workshop paper at "Bridging AI and Cognitive Science"
(ICLR 2020) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Research in developmental psychology consistently shows that children explore
the world thoroughly and efficiently and that this exploration allows them to
learn. In turn, this early learning supports more robust generalization and
intelligent behavior later in life. While much work has gone into developing
methods for exploration in machine learning, artificial agents have not yet
reached the high standard set by their human counterparts. In this work we
propose using DeepMind Lab (Beattie et al., 2016) as a platform to directly
compare child and agent behaviors and to develop new exploration techniques. We
outline two ongoing experiments to demonstrate the effectiveness of a direct
comparison, and outline a number of open research questions that we believe can
be tested using this methodology.
| [
{
"version": "v1",
"created": "Wed, 6 May 2020 14:54:31 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jul 2020 09:26:18 GMT"
}
] | 1,593,648,000,000 | [
[
"Kosoy",
"Eliza",
""
],
[
"Collins",
"Jasmine",
""
],
[
"Chan",
"David M.",
""
],
[
"Huang",
"Sandy",
""
],
[
"Pathak",
"Deepak",
""
],
[
"Agrawal",
"Pulkit",
""
],
[
"Canny",
"John",
""
],
[
"Gopnik",
"Alison",
""
],
[
"Hamrick",
"Jessica B.",
""
]
] |
2005.02963 | Maayan Shvo | Maayan Shvo, Toryn Q. Klassen, Sheila A. McIlraith | Towards the Role of Theory of Mind in Explanation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Theory of Mind is commonly defined as the ability to attribute mental states
(e.g., beliefs, goals) to oneself, and to others. A large body of previous work
- from the social sciences to artificial intelligence - has observed that
Theory of Mind capabilities are central to providing an explanation to another
agent or when explaining that agent's behaviour. In this paper, we build and
expand upon previous work by providing an account of explanation in terms of
the beliefs of agents and the mechanism by which agents revise their beliefs
given possible explanations. We further identify a set of desiderata for
explanations that utilize Theory of Mind. These desiderata inform our
belief-based account of explanation.
| [
{
"version": "v1",
"created": "Wed, 6 May 2020 17:13:46 GMT"
}
] | 1,588,809,600,000 | [
[
"Shvo",
"Maayan",
""
],
[
"Klassen",
"Toryn Q.",
""
],
[
"McIlraith",
"Sheila A.",
""
]
] |
2005.02986 | Ramon Fraga Pereira | Kin Max Piamolini Gusm\~ao, Ramon Fraga Pereira, Felipe Meneguzzi | The More the Merrier?! Evaluating the Effect of Landmark Extraction
Algorithms on Landmark-Based Goal Recognition | This paper has been published at the AAAI 2020 workshop on Plan,
Activity, and Intent Recognition (PAIR) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent approaches to goal and plan recognition using classical planning
domains have achieved state of the art results in terms of both recognition
time and accuracy by using heuristics based on planning landmarks. To achieve
such fast recognition time these approaches use efficient, but incomplete,
algorithms to extract only a subset of landmarks for planning domains and
problems, at the cost of some accuracy. In this paper, we investigate the
impact and effect of using various landmark extraction algorithms capable of
extracting a larger proportion of the landmarks for each given planning
problem, up to exhaustive landmark extraction. We perform an extensive
empirical evaluation of various landmark-based heuristics when using different
percentages of the full set of landmarks. Results show that having more
landmarks does not necessarily mean achieving higher accuracy and lower spread,
as the additional extracted landmarks may not necessarily increase be helpful
towards the goal recognition task.
| [
{
"version": "v1",
"created": "Wed, 6 May 2020 17:41:19 GMT"
}
] | 1,588,809,600,000 | [
[
"Gusmão",
"Kin Max Piamolini",
""
],
[
"Pereira",
"Ramon Fraga",
""
],
[
"Meneguzzi",
"Felipe",
""
]
] |
2005.03098 | Arne Decadt | Arne Decadt, Jasper De Bock, Gert de Cooman | Inference with Choice Functions Made Practical | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study how to infer new choices from previous choices in a conservative
manner. To make such inferences, we use the theory of choice functions: a
unifying mathematical framework for conservative decision making that allows
one to impose axioms directly on the represented decisions. We here adopt the
coherence axioms of De Bock and De Cooman (2019). We show how to naturally
extend any given choice assessment to such a coherent choice function, whenever
possible, and use this natural extension to make new choices. We present a
practical algorithm to compute this natural extension and provide several
methods that can be used to improve its scalability.
| [
{
"version": "v1",
"created": "Thu, 7 May 2020 12:58:05 GMT"
},
{
"version": "v2",
"created": "Sun, 10 May 2020 14:34:25 GMT"
},
{
"version": "v3",
"created": "Wed, 15 Jul 2020 14:08:00 GMT"
}
] | 1,594,857,600,000 | [
[
"Decadt",
"Arne",
""
],
[
"De Bock",
"Jasper",
""
],
[
"de Cooman",
"Gert",
""
]
] |
2005.03182 | Thomas Da Silva Paula | David Murphy and Thomas S. Paula and Wagston Staehler and Juliano
Vacaro and Gabriel Paz and Guilherme Marques and Bruna Oliveira | A Proposal for Intelligent Agents with Episodic Memory | 7 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the future we can expect that artificial intelligent agents, once
deployed, will be required to learn continually from their experience during
their operational lifetime. Such agents will also need to communicate with
humans and other agents regarding the content of their experience, in the
context of passing along their learnings, for the purpose of explaining their
actions in specific circumstances or simply to relate more naturally to humans
concerning experiences the agent acquires that are not necessarily related to
their assigned tasks. We argue that to support these goals, an agent would
benefit from an episodic memory; that is, a memory that encodes the agent's
experience in such a way that the agent can relive the experience, communicate
about it and use its past experience, inclusive of the agents own past actions,
to learn more effective models and policies. In this short paper, we propose
one potential approach to provide an AI agent with such capabilities. We draw
upon the ever-growing body of work examining the function and operation of the
Medial Temporal Lobe (MTL) in mammals to guide us in adding an episodic memory
capability to an AI agent composed of artificial neural networks (ANNs). Based
on that, we highlight important aspects to be considered in the memory
organization and we propose an architecture combining ANNs and standard
Computer Science techniques for supporting storage and retrieval of episodic
memories. Despite being initial work, we hope this short paper can spark
discussions around the creation of intelligent agents with memory or, at least,
provide a different point of view on the subject.
| [
{
"version": "v1",
"created": "Thu, 7 May 2020 00:26:42 GMT"
}
] | 1,633,046,400,000 | [
[
"Murphy",
"David",
""
],
[
"Paula",
"Thomas S.",
""
],
[
"Staehler",
"Wagston",
""
],
[
"Vacaro",
"Juliano",
""
],
[
"Paz",
"Gabriel",
""
],
[
"Marques",
"Guilherme",
""
],
[
"Oliveira",
"Bruna",
""
]
] |
2005.04016 | Abderrahmane Maaradji | Abderrahmane Maaradji, Marlon Dumas, Marcello La Rosa, and Alireza
Ostovar | Detecting sudden and gradual drifts in business processes from execution
traces | null | IEEE Transactions on Knowledge and Data Engineering 29, no. 10
(2017) | 10.1109/TKDE.2017.2720601 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Business processes are prone to unexpected changes, as process workers may
suddenly or gradually start executing a process differently in order to adjust
to changes in workload, season, or other external factors. Early detection of
business process changes enables managers to identify and act upon changes that
may otherwise affect process performance. Business process drift detection
refers to a family of methods to detect changes in a business process by
analyzing event logs extracted from the systems that support the execution of
the process. Existing methods for business process drift detection are based on
an explorative analysis of a potentially large feature space and in some cases
they require users to manually identify specific features that characterize the
drift. Depending on the explored feature space, these methods miss various
types of changes. Moreover, they are either designed to detect sudden drifts or
gradual drifts but not both. This paper proposes an automated and statistically
grounded method for detecting sudden and gradual business process drifts under
a unified framework. An empirical evaluation shows that the method detects
typical change patterns with significantly higher accuracy and lower detection
delay than existing methods, while accurately distinguishing between sudden and
gradual drifts.
| [
{
"version": "v1",
"created": "Thu, 7 May 2020 16:22:11 GMT"
}
] | 1,589,155,200,000 | [
[
"Maaradji",
"Abderrahmane",
""
],
[
"Dumas",
"Marlon",
""
],
[
"La Rosa",
"Marcello",
""
],
[
"Ostovar",
"Alireza",
""
]
] |
2005.04306 | Peter Clark | Peter Clark, John Thompson, Bruce Porter | Knowledge Patterns | Published in the Handbook of Ontologies, 2004, pp 191-207. (This is
an updated and extended version of the paper by the same name in Proc.
KR'2000) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a new technique, called "knowledge patterns", for
helping construct axiom-rich, formal ontologies, based on identifying and
explicitly representing recurring patterns of knowledge (theory schemata) in
the ontology, and then stating how those patterns map onto domain-specific
concepts in the ontology. From a modeling perspective, knowledge patterns
provide an important insight into the structure of a formal ontology: rather
than viewing a formal ontology simply as a list of terms and axioms, knowledge
patterns views it as a collection of abstract, modular theories (the "knowledge
patterns") plus a collection of modeling decisions stating how different
aspects of the world can be modeled using those theories. Knowledge patterns
make both those abstract theories and their mappings to the domain of interest
explicit, thus making modeling decisions clear, and avoiding some of the
ontological confusion that can otherwise arise. In addition, from a
computational perspective, knowledge patterns provide a simple and
computationally efficient mechanism for facilitating knowledge reuse. We
describe the technique and an application built using them, and then critique
its strengths and weaknesses. We conclude that this technique enables us to
better explicate both the structure and modeling decisions made when
constructing a formal axiom-rich ontology.
| [
{
"version": "v1",
"created": "Fri, 8 May 2020 22:33:30 GMT"
}
] | 1,589,241,600,000 | [
[
"Clark",
"Peter",
""
],
[
"Thompson",
"John",
""
],
[
"Porter",
"Bruce",
""
]
] |
2005.04466 | Romain Wallon | Daniel Le Berre, Pierre Marquis, Romain Wallon | On Weakening Strategies for PB Solvers | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current pseudo-Boolean solvers implement different variants of the cutting
planes proof system to infer new constraints during conflict analysis. One of
these variants is generalized resolution, which allows to infer strong
constraints, but suffers from the growth of coefficients it generates while
combining pseudo-Boolean constraints. Another variant consists in using
weakening and division, which is more efficient in practice but may infer
weaker constraints. In both cases, weakening is mandatory to derive conflicting
constraints. However, its impact on the performance of pseudo-Boolean solvers
has not been assessed so far. In this paper, new application strategies for
this rule are studied, aiming to infer strong constraints with small
coefficients. We implemented them in Sat4j and observed that each of them
improves the runtime of the solver. While none of them performs better than the
others on all benchmarks, applying weakening on the conflict side has
surprising good performance, whereas applying partial weakening and division on
both the conflict and the reason sides provides the best results overall.
| [
{
"version": "v1",
"created": "Sat, 9 May 2020 15:40:55 GMT"
}
] | 1,589,241,600,000 | [
[
"Berre",
"Daniel Le",
""
],
[
"Marquis",
"Pierre",
""
],
[
"Wallon",
"Romain",
""
]
] |
2005.04589 | Benjamin Goertzel | Ben Goertzel | Maximal Algorithmic Caliber and Algorithmic Causal Network Inference:
General Principles of Real-World General Intelligence? | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ideas and formalisms from far-from-equilibrium thermodynamics are ported to
the context of stochastic computational processes, via following and extending
Tadaki's algorithmic thermodynamics. A Principle of Maximum Algorithmic Caliber
is proposed, providing guidance as to what computational processes one should
hypothesize if one is provided constraints to work within. It is conjectured
that, under suitable assumptions, computational processes obeying algorithmic
Markov conditions will maximize algorithmic caliber. It is proposed that in
accordance with this, real-world cognitive systems may operate in substantial
part by modeling their environments and choosing their actions to be
(approximate and compactly represented) algorithmic Markov networks. These
ideas are suggested as potential early steps toward a general theory of the
operation of pragmatic generally intelligent systems.
| [
{
"version": "v1",
"created": "Sun, 10 May 2020 06:14:59 GMT"
}
] | 1,589,241,600,000 | [
[
"Goertzel",
"Ben",
""
]
] |
2005.05131 | Kuruge Darshana Abeyrathna | K. Darshana Abeyrathna, Ole-Christoffer Granmo, Morten Goodwin | Extending the Tsetlin Machine With Integer-Weighted Clauses for
Increased Interpretability | 20 pages, 10 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite significant effort, building models that are both interpretable and
accurate is an unresolved challenge for many pattern recognition problems. In
general, rule-based and linear models lack accuracy, while deep learning
interpretability is based on rough approximations of the underlying inference.
Using a linear combination of conjunctive clauses in propositional logic,
Tsetlin Machines (TMs) have shown competitive performance on diverse
benchmarks. However, to do so, many clauses are needed, which impacts
interpretability. Here, we address the accuracy-interpretability challenge in
machine learning by equipping the TM clauses with integer weights. The
resulting Integer Weighted TM (IWTM) deals with the problem of learning which
clauses are inaccurate and thus must team up to obtain high accuracy as a team
(low weight clauses), and which clauses are sufficiently accurate to operate
more independently (high weight clauses). Since each TM clause is formed
adaptively by a team of Tsetlin Automata, identifying effective weights becomes
a challenging online learning problem. We address this problem by extending
each team of Tsetlin Automata with a stochastic searching on the line (SSL)
automaton. In our novel scheme, the SSL automaton learns the weight of its
clause in interaction with the corresponding Tsetlin Automata team, which, in
turn, adapts the composition of the clause by the adjusting weight. We evaluate
IWTM empirically using five datasets, including a study of interpetability. On
average, IWTM uses 6.5 times fewer literals than the vanilla TM and 120 times
fewer literals than a TM with real-valued weights. Furthermore, in terms of
average F1-Score, IWTM outperforms simple Multi-Layered Artificial Neural
Networks, Decision Trees, Support Vector Machines, K-Nearest Neighbor, Random
Forest, XGBoost, Explainable Boosting Machines, and standard and real-value
weighted TMs.
| [
{
"version": "v1",
"created": "Mon, 11 May 2020 14:18:09 GMT"
}
] | 1,589,241,600,000 | [
[
"Abeyrathna",
"K. Darshana",
""
],
[
"Granmo",
"Ole-Christoffer",
""
],
[
"Goodwin",
"Morten",
""
]
] |
2005.05137 | Kieran Greer Dr | Kieran Greer | New Ideas for Brain Modelling 6 | null | AIMS Biophysics, Vol. 7, Issue 4, pp. 308-322 (2020) | 10.3934/biophy.2020022. | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes implementation details for a 3-level cognitive model,
described in the paper series. The whole architecture is now modular, with
different levels using different types of information. The ensemble-hierarchy
relationship is maintained and placed in the bottom optimising and middle
aggregating levels, to store memory objects and their relations. The top-level
cognitive layer has been re-designed to model the Cognitive Process Language
(CPL) of an earlier paper, by refactoring it into a network structure with a
light scheduler. The cortex brain region is thought to be hierarchical -
clustering from simple to more complex features. The refactored network might
therefore challenge conventional thinking on that brain region. It is also
argued that the function and structure in particular, of the new top level, is
similar to the psychology theory of chunking. The model is still only a
framework and does not have enough information for real intelligence. But a
framework is now implemented over the whole design and so can give a more
complete picture about the potential for results.
| [
{
"version": "v1",
"created": "Mon, 11 May 2020 14:28:34 GMT"
}
] | 1,596,153,600,000 | [
[
"Greer",
"Kieran",
""
]
] |
2005.05538 | Nicholas Kluge Corr\^ea | Nythamar de Oliveira and Nicholas Kluge Corr\^ea | Dynamic Cognition Applied to Value Learning in Artificial Intelligence | null | Aoristo - International Journal of Phenomenology, Hermeneutics and
Metaphysics (2021) | 10.6394/aoristo.v2i4.27982 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Experts in Artificial Intelligence (AI) development predict that advances in
the development of intelligent systems and agents will reshape vital areas in
our society. Nevertheless, if such an advance isn't done with prudence, it can
result in negative outcomes for humanity. For this reason, several researchers
in the area are trying to develop a robust, beneficial, and safe concept of
artificial intelligence. Currently, several of the open problems in the field
of AI research arise from the difficulty of avoiding unwanted behaviors of
intelligent agents, and at the same time specifying what we want such systems
to do. It is of utmost importance that artificial intelligent agents have their
values aligned with human values, given the fact that we cannot expect an AI to
develop our moral preferences simply because of its intelligence, as discussed
in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are
addressing the problem of expressing objectives, values, and ends, using
representational cognitive methods. A solution to this problem would be the
dynamic cognitive approach proposed by Dreyfus, whose phenomenological
philosophy defends that the human experience of being-in-the-world cannot be
represented by the symbolic or connectionist cognitive methods. A possible
approach to this problem would be to use theoretical models such as SED
(situated embodied dynamics) to address the values learning problem in AI.
| [
{
"version": "v1",
"created": "Tue, 12 May 2020 03:58:52 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Aug 2020 01:51:35 GMT"
},
{
"version": "v3",
"created": "Thu, 27 Aug 2020 19:41:56 GMT"
},
{
"version": "v4",
"created": "Sun, 25 Oct 2020 20:49:17 GMT"
},
{
"version": "v5",
"created": "Sun, 22 Aug 2021 20:25:31 GMT"
},
{
"version": "v6",
"created": "Tue, 24 Aug 2021 01:15:40 GMT"
}
] | 1,629,849,600,000 | [
[
"de Oliveira",
"Nythamar",
""
],
[
"Corrêa",
"Nicholas Kluge",
""
]
] |
2005.05712 | Ramon Fraga Pereira | Ramon Fraga Pereira | Goal Recognition over Imperfect Domain Models | Ph. D. Thesis defended in February of 2020, PUCRS, Porto Alegre,
Brazil | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Goal recognition is the problem of recognizing the intended goal of
autonomous agents or humans by observing their behavior in an environment. Over
the past years, most existing approaches to goal and plan recognition have been
ignoring the need to deal with imperfections regarding the domain model that
formalizes the environment where autonomous agents behave. In this thesis, we
introduce the problem of goal recognition over imperfect domain models, and
develop solution approaches that explicitly deal with two distinct types of
imperfect domains models: (1) incomplete discrete domain models that have
possible, rather than known, preconditions and effects in action descriptions;
and (2) approximate continuous domain models, where the transition function is
approximated from past observations and not well-defined. We develop novel goal
recognition approaches over imperfect domains models by leveraging and adapting
existing recognition approaches from the literature. Experiments and evaluation
over these two types of imperfect domains models show that our novel goal
recognition approaches are accurate in comparison to baseline approaches from
the literature, at several levels of observability and imperfections.
| [
{
"version": "v1",
"created": "Tue, 12 May 2020 12:11:53 GMT"
}
] | 1,589,328,000,000 | [
[
"Pereira",
"Ramon Fraga",
""
]
] |
2005.05721 | Quratul-Ain Mahesar | Quratul-ain Mahesar, Nir Oren and Wamberto W. Vasconcelos | Preference Elicitation in Assumption-Based Argumentation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Various structured argumentation frameworks utilize preferences as part of
their standard inference procedure to enable reasoning with preferences. In
this paper, we consider an inverse of the standard reasoning problem, seeking
to identify what preferences over assumptions could lead to a given set of
conclusions being drawn. We ground our work in the Assumption-Based
Argumentation (ABA) framework, and present an algorithm which computes and
enumerates all possible sets of preferences over the assumptions in the system
from which a desired conflict free set of conclusions can be obtained under a
given semantic. After describing our algorithm, we establish its soundness,
completeness and complexity.
| [
{
"version": "v1",
"created": "Tue, 12 May 2020 12:31:27 GMT"
}
] | 1,589,328,000,000 | [
[
"Mahesar",
"Quratul-ain",
""
],
[
"Oren",
"Nir",
""
],
[
"Vasconcelos",
"Wamberto W.",
""
]
] |
2005.05849 | Quratul-Ain Mahesar | Quratul-ain Mahesar and Simon Parsons | Argument Schemes for Explainable Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) is being increasingly used to develop systems
that produce intelligent solutions. However, there is a major concern that
whether the systems built will be trusted by humans. In order to establish
trust in AI systems, there is a need for the user to understand the reasoning
behind their solutions and therefore, the system should be able to explain and
justify its output. In this paper, we use argumentation to provide explanations
in the domain of AI planning. We present argument schemes to create arguments
that explain a plan and its components; 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. Finally, we present some
properties of the plan arguments.
| [
{
"version": "v1",
"created": "Tue, 12 May 2020 15:09:50 GMT"
}
] | 1,589,328,000,000 | [
[
"Mahesar",
"Quratul-ain",
""
],
[
"Parsons",
"Simon",
""
]
] |
2005.05906 | Brent Mittelstadt | Sandra Wachter, Brent Mittelstadt, Chris Russell | Why Fairness Cannot Be Automated: Bridging the Gap Between EU
Non-Discrimination Law and AI | null | null | 10.2139/ssrn.3547922 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article identifies a critical incompatibility between European notions
of discrimination and existing statistical measures of fairness. First, we
review the evidential requirements to bring a claim under EU non-discrimination
law. Due to the disparate nature of algorithmic and human discrimination, the
EU's current requirements are too contextual, reliant on intuition, and open to
judicial interpretation to be automated. Second, we show how the legal
protection offered by non-discrimination law is challenged when AI, not humans,
discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes,
prejudice) and unintentional biases (e.g. organisational practices or
internalised stereotypes) which can act as a signal to victims that
discrimination has occurred. Finally, we examine how existing work on fairness
in machine learning lines up with procedures for assessing cases under EU
non-discrimination law. We propose "conditional demographic disparity" (CDD) as
a standard baseline statistical measurement that aligns with the European Court
of Justice's "gold standard." Establishing a standard set of statistical
evidence for automated discrimination cases can help ensure consistent
procedures for assessment, but not judicial interpretation, of cases involving
AI and automated systems. Through this proposal for procedural regularity in
the identification and assessment of automated discrimination, we clarify how
to build considerations of fairness into automated systems as far as possible
while still respecting and enabling the contextual approach to judicial
interpretation practiced under EU non-discrimination law.
N.B. Abridged abstract
| [
{
"version": "v1",
"created": "Tue, 12 May 2020 16:30:12 GMT"
}
] | 1,589,328,000,000 | [
[
"Wachter",
"Sandra",
""
],
[
"Mittelstadt",
"Brent",
""
],
[
"Russell",
"Chris",
""
]
] |
2005.06885 | Bing Huang | Bing Huang, Athman Bouguettaya, Hai Dong | Enabling Edge Cloud Intelligence for Activity Learning in Smart Home | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel activity learning framework based on Edge Cloud
architecture for the purpose of recognizing and predicting human activities.
Although activity recognition has been vastly studied by many researchers, the
temporal features that constitute an activity, which can provide useful
insights for activity models, have not been exploited to their full potentials
by mining algorithms. In this paper, we utilize temporal features for activity
recognition and prediction in a single smart home setting. We discover activity
patterns and temporal relations such as the order of activities from real data
to develop a prompting system. Analysis of real data collected from smart homes
was used to validate the proposed method.
| [
{
"version": "v1",
"created": "Thu, 14 May 2020 11:43:20 GMT"
}
] | 1,589,500,800,000 | [
[
"Huang",
"Bing",
""
],
[
"Bouguettaya",
"Athman",
""
],
[
"Dong",
"Hai",
""
]
] |
2005.06914 | Bing Huang | Bing Huang, Athman Bouguettaya, Azadeh Ghari Neiat | Cognitive Amplifier for Internet of Things | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a Cognitive Amplifier framework to augment things part of an IoT,
with cognitive capabilities for the purpose of improving life convenience.
Specifically, the Cognitive Amplifier consists of knowledge discovery and
prediction components. The knowledge discovery component focuses on finding
natural activity patterns considering their regularity, variations, and
transitions in real life setting. The prediction component takes the discovered
knowledge as the base for inferring what, when, and where the next activity
will happen. Experimental results on real-life data validate the feasibility
and applicability of the proposed approach.
| [
{
"version": "v1",
"created": "Thu, 14 May 2020 12:30:42 GMT"
}
] | 1,589,500,800,000 | [
[
"Huang",
"Bing",
""
],
[
"Bouguettaya",
"Athman",
""
],
[
"Neiat",
"Azadeh Ghari",
""
]
] |
2005.07073 | Edoardo Bacci | Edoardo Bacci and David Parker | Probabilistic Guarantees for Safe Deep Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep reinforcement learning has been successfully applied to many control
tasks, but the application of such agents in safety-critical scenarios has been
limited due to safety concerns. Rigorous testing of these controllers is
challenging, particularly when they operate in probabilistic environments due
to, for example, hardware faults or noisy sensors. We propose MOSAIC, an
algorithm for measuring the safety of deep reinforcement learning agents in
stochastic settings. Our approach is based on the iterative construction of a
formal abstraction of a controller's execution in an environment, and leverages
probabilistic model checking of Markov decision processes to produce
probabilistic guarantees on safe behaviour over a finite time horizon. It
produces bounds on the probability of safe operation of the controller for
different initial configurations and identifies regions where correct behaviour
can be guaranteed. We implement and evaluate our approach on agents trained for
several benchmark control problems.
| [
{
"version": "v1",
"created": "Thu, 14 May 2020 15:42:19 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Jul 2020 09:55:04 GMT"
}
] | 1,594,252,800,000 | [
[
"Bacci",
"Edoardo",
""
],
[
"Parker",
"David",
""
]
] |
2005.07870 | Diego Fernando Gomez Noriega | Diego Gomez, Nicanor Quijano, Luis Felipe Giraldo | Learning Transferable Concepts in Deep Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While humans and animals learn incrementally during their lifetimes and
exploit their experience to solve new tasks, standard deep reinforcement
learning methods specialize to solve only one task at a time. As a result, the
information they acquire is hardly reusable in new situations. Here, we
introduce a new perspective on the problem of leveraging prior knowledge to
solve future tasks. We show that learning discrete representations of sensory
inputs can provide a high-level abstraction that is common across multiple
tasks, thus facilitating the transference of information. In particular, we
show that it is possible to learn such representations by self-supervision,
following an information theoretic approach. Our method is able to learn
concepts in locomotive and optimal control tasks that increase the sample
efficiency in both known and unknown tasks, opening a new path to endow
artificial agents with generalization abilities.
| [
{
"version": "v1",
"created": "Sat, 16 May 2020 04:45:51 GMT"
},
{
"version": "v2",
"created": "Tue, 19 May 2020 15:05:31 GMT"
},
{
"version": "v3",
"created": "Thu, 25 Feb 2021 01:33:54 GMT"
},
{
"version": "v4",
"created": "Tue, 22 Feb 2022 08:08:51 GMT"
}
] | 1,645,574,400,000 | [
[
"Gomez",
"Diego",
""
],
[
"Quijano",
"Nicanor",
""
],
[
"Giraldo",
"Luis Felipe",
""
]
] |
2005.08078 | David Limbaugh | David Limbaugh, Jobst Landgrebe, David Kasmier, Ronald Rudnicki, James
Llinas, Barry Smith | Ontology and Cognitive Outcomes | 19 pages, 6 figures | Journal of Knowledge Structures & Systems, 1(1), 3-22 (2020) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here we understand 'intelligence' as referring to items of knowledge
collected for the sake of assessing and maintaining national security. The
intelligence community (IC) of the United States (US) is a community of
organizations that collaborate in collecting and processing intelligence for
the US. The IC relies on human-machine-based analytic strategies that 1) access
and integrate vast amounts of information from disparate sources, 2)
continuously process this information, so that, 3) a maximally comprehensive
understanding of world actors and their behaviors can be developed and updated.
Herein we describe an approach to utilizing outcomes-based learning (OBL) to
support these efforts that is based on an ontology of the cognitive processes
performed by intelligence analysts. Of particular importance to the Cognitive
Process Ontology is the class Representation that is Warranted. Such a
representation is descriptive in nature and deserving of trust in its
veridicality. The latter is because a Representation that is Warranted is
always produced by a process that was vetted (or successfully designed) to
reliably produce veridical representations. As such, Representations that are
Warranted are what in other contexts we might refer to as 'items of knowledge'.
| [
{
"version": "v1",
"created": "Sat, 16 May 2020 19:50:26 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Aug 2020 17:18:40 GMT"
},
{
"version": "v3",
"created": "Fri, 8 Jan 2021 14:49:58 GMT"
}
] | 1,610,323,200,000 | [
[
"Limbaugh",
"David",
""
],
[
"Landgrebe",
"Jobst",
""
],
[
"Kasmier",
"David",
""
],
[
"Rudnicki",
"Ronald",
""
],
[
"Llinas",
"James",
""
],
[
"Smith",
"Barry",
""
]
] |
2005.08517 | Joel Colloc | Jo\"el Colloc (IDEES), Danielle Boulanger | Automatic Knowledge Acquisition for Object-Oriented Expert Systems | null | AVIGNON'93 Thirteenth International Conference Artificial
Intelligence, Expert Systems, Natural Language, May 1993, Avignon, France | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe an Object Oriented Model for building Expert Systems. This model
and the detection of similarities allow to implement reasoning modes as
induction, deduction and simulation. We specially focus on similarity and its
use in induction. We propose original algorithms which deal with total and
partial structural similitude of objects to facilitate knowledge acquisition.
| [
{
"version": "v1",
"created": "Mon, 18 May 2020 08:16:48 GMT"
}
] | 1,589,846,400,000 | [
[
"Colloc",
"Joël",
"",
"IDEES"
],
[
"Boulanger",
"Danielle",
""
]
] |
2005.08954 | Toby Walsh | Toby Walsh | On the Complexity of Breaking Symmetry | Appears in Proceedings of the 11th International Workshop on Symmetry
in Constraint Satisfaction Problems (SymCon'11). Held alongside the
International Conference on the Principles and Practice of Constraint
Programming (CP 2011). arXiv admin note: text overlap with arXiv:1306.5053 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We can break symmetry by eliminating solutions within a symmetry class that
are not least in the lexicographical ordering. This is often referred to as the
lex-leader method. Unfortunately, as symmetry groups can be large, the
lexleader method is not tractable in general. We prove that using other total
orderings besides the usual lexicographical ordering will not reduce the
computational complexity of breaking symmetry in general. It follows that
breaking symmetry with other orderings like the Gray code ordering or the
Snake-Lex ordering is intractable in general.
| [
{
"version": "v1",
"created": "Sat, 16 May 2020 23:54:06 GMT"
}
] | 1,589,932,800,000 | [
[
"Walsh",
"Toby",
""
]
] |
2005.09280 | Anton Kolonin Dr. | Anton Kolonin | Controlled Language and Baby Turing Test for General Conversational
Intelligence | 10 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | General conversational intelligence appears to be an important part of
artificial general intelligence. Respectively, it requires accessible measures
of the intelligence quality and controllable ways of its achievement, ideally -
having the linguistic and semantic models represented in a reasonable way. Our
work is suggesting to use Baby Turing Test approach to extend the classic
Turing Test for conversational intelligence and controlled language based on
semantic graph representation extensible for arbitrary subject domain. We
describe how the two can be used together to build a general-purpose
conversational system such as an intelligent assistant for online media and
social network data processing.
| [
{
"version": "v1",
"created": "Tue, 19 May 2020 08:27:26 GMT"
}
] | 1,589,932,800,000 | [
[
"Kolonin",
"Anton",
""
]
] |
2005.09331 | Athina Georgara | Athina Georgara, Carles Sierra, Juan A. Rodr\'iguez-Aguilar | TAIP: an anytime algorithm for allocating student teams to internship
programs | 10 pages, 7 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In scenarios that require teamwork, we usually have at hand a variety of
specific tasks, for which we need to form a team in order to carry out each
one. Here we target the problem of matching teams with tasks within the context
of education, and specifically in the context of forming teams of students and
allocating them to internship programs. First we provide a formalization of the
Team Allocation for Internship Programs Problem, and show the computational
hardness of solving it optimally. Thereafter, we propose TAIP, a heuristic
algorithm that generates an initial team allocation which later on attempts to
improve in an iterative process. Moreover, we conduct a systematic evaluation
to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.
| [
{
"version": "v1",
"created": "Tue, 19 May 2020 09:50:38 GMT"
}
] | 1,589,932,800,000 | [
[
"Georgara",
"Athina",
""
],
[
"Sierra",
"Carles",
""
],
[
"Rodríguez-Aguilar",
"Juan A.",
""
]
] |
2005.09645 | Thomas Moerland | Thomas M Moerland, Joost Broekens, Aske Plaat, Catholijn M Jonker | The Second Type of Uncertainty in Monte Carlo Tree Search | arXiv admin note: text overlap with arXiv:1805.09218 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Monte Carlo Tree Search (MCTS) efficiently balances exploration and
exploitation in tree search based on count-derived uncertainty. However, these
local visit counts ignore a second type of uncertainty induced by the size of
the subtree below an action. We first show how, due to the lack of this second
uncertainty type, MCTS may completely fail in well-known sparse exploration
problems, known from the reinforcement learning community. We then introduce a
new algorithm, which estimates the size of the subtree below an action, and
leverages this information in the UCB formula to better direct exploration.
Subsequently, we generalize these ideas by showing that loops, i.e., the
repeated occurrence of (approximately) the same state in the same trace, are
actually a special case of subtree depth variation. Testing on a variety of
tasks shows that our algorithms increase sample efficiency, especially when the
planning budget per timestep is small.
| [
{
"version": "v1",
"created": "Tue, 19 May 2020 09:10:51 GMT"
}
] | 1,590,019,200,000 | [
[
"Moerland",
"Thomas M",
""
],
[
"Broekens",
"Joost",
""
],
[
"Plaat",
"Aske",
""
],
[
"Jonker",
"Catholijn M",
""
]
] |
2005.09755 | Rachel Freedman | Rachel Freedman, Jana Schaich Borg, Walter Sinnott-Armstrong, John P.
Dickerson, Vincent Conitzer | Adapting a Kidney Exchange Algorithm to Align with Human Values | null | Artificial Intelligence 283 (2020) 103261 | 10.1016/j.artint.2020.103261 10.1145/3278721.3278727 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The efficient and fair allocation of limited resources is a classical problem
in economics and computer science. In kidney exchanges, a central market maker
allocates living kidney donors to patients in need of an organ. Patients and
donors in kidney exchanges are prioritized using ad-hoc weights decided on by
committee and then fed into an allocation algorithm that determines who gets
what--and who does not. In this paper, we provide an end-to-end methodology for
estimating weights of individual participant profiles in a kidney exchange. We
first elicit from human subjects a list of patient attributes they consider
acceptable for the purpose of prioritizing patients (e.g., medical
characteristics, lifestyle choices, and so on). Then, we ask subjects
comparison queries between patient profiles and estimate weights in a
principled way from their responses. We show how to use these weights in kidney
exchange market clearing algorithms. We then evaluate the impact of the weights
in simulations and find that the precise numerical values of the weights we
computed matter little, other than the ordering of profiles that they imply.
However, compared to not prioritizing patients at all, there is a significant
effect, with certain classes of patients being (de)prioritized based on the
human-elicited value judgments.
| [
{
"version": "v1",
"created": "Tue, 19 May 2020 21:00:29 GMT"
}
] | 1,590,019,200,000 | [
[
"Freedman",
"Rachel",
""
],
[
"Borg",
"Jana Schaich",
""
],
[
"Sinnott-Armstrong",
"Walter",
""
],
[
"Dickerson",
"John P.",
""
],
[
"Conitzer",
"Vincent",
""
]
] |
2005.09833 | Shiqi Zhang | Keting Lu, Shiqi Zhang, Peter Stone, Xiaoping Chen | Learning and Reasoning for Robot Dialog and Navigation Tasks | arXiv admin note: substantial text overlap with arXiv:1809.11074 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning and probabilistic reasoning algorithms aim at learning
from interaction experiences and reasoning with probabilistic contextual
knowledge respectively. In this research, we develop algorithms for robot task
completions, while looking into the complementary strengths of reinforcement
learning and probabilistic reasoning techniques. The robots learn from
trial-and-error experiences to augment their declarative knowledge base, and
the augmented knowledge can be used for speeding up the learning process in
potentially different tasks. We have implemented and evaluated the developed
algorithms using mobile robots conducting dialog and navigation tasks. From the
results, we see that our robot's performance can be improved by both reasoning
with human knowledge and learning from task-completion experience. More
interestingly, the robot was able to learn from navigation tasks to improve its
dialog strategies.
| [
{
"version": "v1",
"created": "Wed, 20 May 2020 03:20:42 GMT"
},
{
"version": "v2",
"created": "Mon, 31 Aug 2020 02:07:43 GMT"
}
] | 1,599,004,800,000 | [
[
"Lu",
"Keting",
""
],
[
"Zhang",
"Shiqi",
""
],
[
"Stone",
"Peter",
""
],
[
"Chen",
"Xiaoping",
""
]
] |
2005.09961 | Tristan Cazenave | Tristan Cazenave and Thomas Fournier | Monte Carlo Inverse Folding | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The RNA Inverse Folding problem comes from computational biology. The goal is
to find a molecule that has a given folding. It is important for scientific
fields such as bioengineering, pharmaceutical research, biochemistry, synthetic
biology and RNA nanostructures. Nested Monte Carlo Search has given excellent
results for this problem. We propose to adapt and evaluate different Monte
Carlo Search algorithms for the RNA Inverse Folding problem.
| [
{
"version": "v1",
"created": "Wed, 20 May 2020 11:07:20 GMT"
}
] | 1,590,019,200,000 | [
[
"Cazenave",
"Tristan",
""
],
[
"Fournier",
"Thomas",
""
]
] |
2005.10131 | Joseph Y. Halpern | Meir Friedenberg and Joseph Y. Halpern | Combining the Causal Judgments of Experts with Possibly Different Focus
Areas | Appear in the Proceedings of the Sixteenth International Conference
on Principles of Knowledge Representation and Reasoning (KR2018}, 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many real-world settings, a decision-maker must combine information
provided by different experts in order to decide on an effective policy.
Alrajeh, Chockler, and Halpern [2018] showed how to combine causal models that
are compatible in the sense that, for variables that appear in both models, the
experts agree on the causal structure. In this work we show how causal models
can be combined in cases where the experts might disagree on the causal
structure for variables that appear in both models due to having different
focus areas. We provide a new formal definition of compatibility of models in
this setting and show how compatible models can be combined. We also consider
the complexity of determining whether models are compatible. We believe that
the notions defined in this work are of direct relevance to many practical
decision making scenarios that come up in natural, social, and medical science
settings.
| [
{
"version": "v1",
"created": "Wed, 20 May 2020 15:28:08 GMT"
}
] | 1,590,019,200,000 | [
[
"Friedenberg",
"Meir",
""
],
[
"Halpern",
"Joseph Y.",
""
]
] |
2005.10383 | Joseph Y. Halpern | Matvey Soloviev, Joseph Y. Halpern | Information Acquisition Under Resource Limitations in a Noisy
Environment | A preliminary version of the paper appeared in \emph{Proceedings of
the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)}, 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a theoretical model of information acquisition under resource
limitations in a noisy environment. An agent must guess the truth value of a
given Boolean formula $\varphi$ after performing a bounded number of noisy
tests of the truth values of variables in the formula. We observe that, in
general, the problem of finding an optimal testing strategy for $\phi$ is hard,
but we suggest a useful heuristic. The techniques we use also give insight into
two apparently unrelated, but well-studied problems: (1) \emph{rational
inattention}, that is, when it is rational to ignore pertinent information (the
optimal strategy may involve hardly ever testing variables that are clearly
relevant to $\phi$), and (2) what makes a formula hard to learn/remember.
| [
{
"version": "v1",
"created": "Wed, 20 May 2020 22:49:48 GMT"
}
] | 1,590,105,600,000 | [
[
"Soloviev",
"Matvey",
""
],
[
"Halpern",
"Joseph Y.",
""
]
] |
2005.11016 | Anis Najar | Anis Najar and Mohamed Chetouani | Reinforcement learning with human advice: a survey | Under review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we provide an overview of the existing methods for integrating
human advice into a Reinforcement Learning process. We first propose a taxonomy
of the different forms of advice that can be provided to a learning agent. We
then describe the methods that can be used for interpreting advice when its
meaning is not determined beforehand. Finally, we review different approaches
for integrating advice into the learning process.
| [
{
"version": "v1",
"created": "Fri, 22 May 2020 06:00:13 GMT"
},
{
"version": "v2",
"created": "Tue, 24 Nov 2020 09:02:59 GMT"
}
] | 1,606,262,400,000 | [
[
"Najar",
"Anis",
""
],
[
"Chetouani",
"Mohamed",
""
]
] |
2005.11019 | Florian Richoux | Florian Richoux | microPhantom: Playing microRTS under uncertainty and chaos | null | null | 10.1109/CoG47356.2020.9231653 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This competition paper presents microPhantom, a bot playing microRTS and
participating in the 2020 microRTS AI competition. microPhantom is based on our
previous bot POAdaptive which won the partially observable track of the 2018
and 2019 microRTS AI competitions. In this paper, we focus on decision-making
under uncertainty, by tackling the Unit Production Problem with a method based
on a combination of Constraint Programming and decision theory. We show that
using our method to decide which units to train improves significantly the win
rate against the second-best microRTS bot from the partially observable track.
We also show that our method is resilient in chaotic environments, with a very
small loss of efficiency only. To allow replicability and to facilitate further
research, the source code of microPhantom is available, as well as the
Constraint Programming toolkit it uses.
| [
{
"version": "v1",
"created": "Fri, 22 May 2020 06:05:46 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Jun 2020 03:05:00 GMT"
}
] | 1,653,350,400,000 | [
[
"Richoux",
"Florian",
""
]
] |
2005.11247 | Martin Balla | Martin Balla and Simon M. Lucas and Diego Perez-Liebana | Evaluating Generalisation in General Video Game Playing | accepted for publication in IEEE Conference on Games (CoG) 2020 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The General Video Game Artificial Intelligence (GVGAI) competition has been
running for several years with various tracks. This paper focuses on the
challenge of the GVGAI learning track in which 3 games are selected and 2
levels are given for training, while 3 hidden levels are left for evaluation.
This setup poses a difficult challenge for current Reinforcement Learning (RL)
algorithms, as they typically require much more data. This work investigates 3
versions of the Advantage Actor-Critic (A2C) algorithm trained on a maximum of
2 levels from the available 5 from the GVGAI framework and compares their
performance on all levels. The selected sub-set of games have different
characteristics, like stochasticity, reward distribution and objectives. We
found that stochasticity improves the generalisation, but too much can cause
the algorithms to fail to learn the training levels. The quality of the
training levels also matters, different sets of training levels can boost
generalisation over all levels. In the GVGAI competition agents are scored
based on their win rates and then their scores achieved in the games. We found
that solely using the rewards provided by the game might not encourage winning.
| [
{
"version": "v1",
"created": "Fri, 22 May 2020 15:57:52 GMT"
}
] | 1,590,364,800,000 | [
[
"Balla",
"Martin",
""
],
[
"Lucas",
"Simon M.",
""
],
[
"Perez-Liebana",
"Diego",
""
]
] |
2005.11963 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek | Non-Destructive Sample Generation From Conditional Belief Functions | null | [in:]: Z. Bubnicki, A. Grzech eds: Proc. 13th International
Conference on Systems Science. September 15-18, 1998, Wroc{\l}aw. Oficyna
Wydawnicza Politechniki Wroc{\l}awskiej, Wroc{\l}aw 1998, Vol. I, pp. 115-120 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new approach to generate samples from conditional
belief functions for a restricted but non trivial subset of conditional belief
functions. It assumes the factorization (decomposition) of a belief function
along a bayesian network structure. It applies general conditional belief
functions.
| [
{
"version": "v1",
"created": "Mon, 25 May 2020 08:18:45 GMT"
}
] | 1,590,451,200,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
]
] |
2005.11979 | Mieczys{\l}aw K{\l}opotek | Mieczyslaw A. Klopotek and Andrzej Matuszewski | On Irrelevance of Attributes in Flexible Prediction | null | Proc. 2nd Int. Conf. on New Techniques and Technologies for
Statistics (NTTS'95), Bonn, 19-22 Nov., 1995, Publisher: GMD Sankt Augustin,
pp. 282-293 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyses properties of conceptual hierarchy obtained via
incremental concept formation method called "flexible prediction" in order to
determine what kind of "relevance" of participating attributes may be requested
for meaningful conceptual hierarchy. The impact of selection of simple and
combined attributes, of scaling and of distribution of individual attributes
and of correlation strengths among them is investigated. Paradoxically, both:
attributes weakly and strongly related with other attributes have deteriorating
impact onto the overall classification. Proper construction of derived
attributes as well as selection of scaling of individual attributes strongly
influences the obtained concept hierarchy. Attribute density of distribution
seems to influence the classification weakly
It seems also, that concept hierarchies (taxonomies) reflect a compromise
between the data and our interests in some objective truth about the data. To
obtain classifications more suitable for one's purposes, breaking the symmetry
among attributes (by dividing them into dependent and independent and applying
differing evaluation formulas for their contribution) is suggested. Both
continuous and discrete variables are considered. Some methodologies for the
former are considered.
| [
{
"version": "v1",
"created": "Mon, 25 May 2020 08:41:48 GMT"
}
] | 1,590,451,200,000 | [
[
"Klopotek",
"Mieczyslaw A.",
""
],
[
"Matuszewski",
"Andrzej",
""
]
] |
2005.12360 | Jalal Etesami | Jalal Etesami, Christoph-Nikolas Straehle | Non-cooperative Multi-agent Systems with Exploring Agents | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-agent learning is a challenging problem in machine learning that has
applications in different domains such as distributed control, robotics, and
economics. We develop a prescriptive model of multi-agent behavior using Markov
games. Since in many multi-agent systems, agents do not necessary select their
optimum strategies against other agents (e.g., multi-pedestrian interaction),
we focus on models in which the agents play "exploration but near optimum
strategies". We model such policies using the Boltzmann-Gibbs distribution.
This leads to a set of coupled Bellman equations that describes the behavior of
the agents. We introduce a set of conditions under which the set of equations
admit a unique solution and propose two algorithms that provably provide the
solution in finite and infinite time horizon scenarios. We also study a
practical setting in which the interactions can be described using the
occupancy measures and propose a simplified Markov game with less complexity.
Furthermore, we establish the connection between the Markov games with
exploration strategies and the principle of maximum causal entropy for
multi-agent systems. Finally, we evaluate the performance of our algorithms via
several well-known games from the literature and some games that are designed
based on real world applications.
| [
{
"version": "v1",
"created": "Mon, 25 May 2020 19:34:29 GMT"
}
] | 1,590,537,600,000 | [
[
"Etesami",
"Jalal",
""
],
[
"Straehle",
"Christoph-Nikolas",
""
]
] |
2005.12697 | Colin Bellinger | Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn | Active Measure Reinforcement Learning for Observation Cost Minimization | Under review at NeurIPS 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Standard reinforcement learning (RL) algorithms assume that the observation
of the next state comes instantaneously and at no cost. In a wide variety of
sequential decision making tasks ranging from medical treatment to scientific
discovery, however, multiple classes of state observations are possible, each
of which has an associated cost. We propose the active measure RL framework
(Amrl) as an initial solution to this problem where the agent learns to
maximize the costed return, which we define as the discounted sum of rewards
minus the sum of observation costs. Our empirical evaluation demonstrates that
Amrl-Q agents are able to learn a policy and state estimator in parallel during
online training. During training the agent naturally shifts from its reliance
on costly measurements of the environment to its state estimator in order to
increase its reward. It does this without harm to the learned policy. Our
results show that the Amrl-Q agent learns at a rate similar to standard
Q-learning and Dyna-Q. Critically, by utilizing an active strategy, Amrl-Q
achieves a higher costed return.
| [
{
"version": "v1",
"created": "Tue, 26 May 2020 13:18:09 GMT"
}
] | 1,590,537,600,000 | [
[
"Bellinger",
"Colin",
""
],
[
"Coles",
"Rory",
""
],
[
"Crowley",
"Mark",
""
],
[
"Tamblyn",
"Isaac",
""
]
] |
2005.12713 | Christoph Beierle | Christian Komo and Christoph Beierle | Nonmonotonic Inferences with Qualitative Conditionals based on Preferred
Structures on Worlds | 14 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A conditional knowledge base R is a set of conditionals of the form "If A,
the usually B". Using structural information derived from the conditionals in
R, we introduce the preferred structure relation on worlds. The preferred
structure relation is the core ingredient of a new inference relation called
system W inference that inductively completes the knowledge given explicitly in
R. We show that system W exhibits desirable inference properties like
satisfying system P and avoiding, in contrast to e.g. system Z, the drowning
problem. It fully captures and strictly extends both system Z and skeptical
c-inference. In contrast to skeptical c-inference, it does not require to solve
a complex constraint satisfaction problem, but is as tractable as system Z.
| [
{
"version": "v1",
"created": "Tue, 26 May 2020 13:32:00 GMT"
}
] | 1,590,537,600,000 | [
[
"Komo",
"Christian",
""
],
[
"Beierle",
"Christoph",
""
]
] |
2005.13129 | Lizi Liao Ms | Lizi Liao, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua | Rethinking Dialogue State Tracking with Reasoning | further modification needed | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tracking dialogue states to better interpret user goals and feed downstream
policy learning is a bottleneck in dialogue management. Common practice has
been to treat it as a problem of classifying dialogue content into a set of
pre-defined slot-value pairs, or generating values for different slots given
the dialogue history. Both have limitations on considering dependencies that
occur on dialogues, and are lacking of reasoning capabilities. This paper
proposes to track dialogue states gradually with reasoning over dialogue turns
with the help of the back-end data. Empirical results demonstrate that our
method significantly outperforms the state-of-the-art methods by 38.6% in terms
of joint belief accuracy for MultiWOZ 2.1, a large-scale human-human dialogue
dataset across multiple domains.
| [
{
"version": "v1",
"created": "Wed, 27 May 2020 02:05:33 GMT"
},
{
"version": "v2",
"created": "Wed, 3 Jun 2020 15:12:56 GMT"
}
] | 1,591,228,800,000 | [
[
"Liao",
"Lizi",
""
],
[
"Ma",
"Yunshan",
""
],
[
"Lei",
"Wenqiang",
""
],
[
"Chua",
"Tat-Seng",
""
]
] |
2005.13289 | Pascal Kerschke | Jakob Bossek and Pascal Kerschke and Heike Trautmann | Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated
Algorithm Selection | This version has been accepted for publication at the IEEE Congress
on Evolutionary Computation (IEEE CEC) 2020, which is part of the IEEE World
Congress on Computational Intelligence (IEEE WCCI) 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known
NP-hard combinatorial optimization problems. The two sophisticated heuristic
solvers LKH and EAX and respective (restart) variants manage to calculate
close-to optimal or even optimal solutions, also for large instances with
several thousand nodes in reasonable time. In this work we extend existing
benchmarking studies by addressing anytime behaviour of inexact TSP solvers
based on empirical runtime distributions leading to an increased understanding
of solver behaviour and the respective relation to problem hardness. It turns
out that performance ranking of solvers is highly dependent on the focused
approximation quality. Insights on intersection points of performances offer
huge potential for the construction of hybridized solvers depending on instance
features. Moreover, instance features tailored to anytime performance and
corresponding performance indicators will highly improve automated algorithm
selection models by including comprehensive information on solver quality.
| [
{
"version": "v1",
"created": "Wed, 27 May 2020 11:36:53 GMT"
}
] | 1,590,624,000,000 | [
[
"Bossek",
"Jakob",
""
],
[
"Kerschke",
"Pascal",
""
],
[
"Trautmann",
"Heike",
""
]
] |
2005.13406 | Sebastian Jaszczur | Sebastian Jaszczur, Micha{\l} {\L}uszczyk, Henryk Michalewski | Neural heuristics for SAT solving | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We use neural graph networks with a message-passing architecture and an
attention mechanism to enhance the branching heuristic in two SAT-solving
algorithms. We report improvements of learned neural heuristics compared with
two standard human-designed heuristics.
| [
{
"version": "v1",
"created": "Wed, 27 May 2020 15:05:22 GMT"
}
] | 1,590,624,000,000 | [
[
"Jaszczur",
"Sebastian",
""
],
[
"Łuszczyk",
"Michał",
""
],
[
"Michalewski",
"Henryk",
""
]
] |
2005.13601 | Eric Veith | Eric MSP Veith, Nils Wenninghoff, and Emilie Frost | The Adversarial Resilience Learning Architecture for AI-based Modelling,
Exploration, and Operation of Complex Cyber-Physical Systems | Submitted to NIPS 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern algorithms in the domain of Deep Reinforcement Learning (DRL)
demonstrated remarkable successes; most widely known are those in game-based
scenarios, from ATARI video games to Go and the StarCraft~\textsc{II} real-time
strategy game. However, applications in the domain of modern Cyber-Physical
Systems (CPS) that take advantage a vast variety of DRL algorithms are few. We
assume that the benefits would be considerable: Modern CPS have become
increasingly complex and evolved beyond traditional methods of modelling and
analysis. At the same time, these CPS are confronted with an increasing amount
of stochastic inputs, from volatile energy sources in power grids to broad user
participation stemming from markets. Approaches of system modelling that use
techniques from the domain of Artificial Intelligence (AI) do not focus on
analysis and operation. In this paper, we describe the concept of Adversarial
Resilience Learning (ARL) that formulates a new approach to complex environment
checking and resilient operation: It defines two agent classes, attacker and
defender agents. The quintessence of ARL lies in both agents exploring the
system and training each other without any domain knowledge. Here, we introduce
the ARL software architecture that allows to use a wide range of model-free as
well as model-based DRL-based algorithms, and document results of concrete
experiment runs on a complex power grid.
| [
{
"version": "v1",
"created": "Wed, 27 May 2020 19:19:57 GMT"
}
] | 1,590,710,400,000 | [
[
"Veith",
"Eric MSP",
""
],
[
"Wenninghoff",
"Nils",
""
],
[
"Frost",
"Emilie",
""
]
] |
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