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2111.01366 | Liao Qu | Liao Qu, Shuaiqi Huang, Yunsong Jia, Xiang Li | Improved Loss Function-Based Prediction Method of Extreme Temperatures
in Greenhouses | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The prediction of extreme greenhouse temperatures to which crops are
susceptible is essential in the field of greenhouse planting. It can help avoid
heat or freezing damage and economic losses. Therefore, it's important to
develop models that can predict them accurately. Due to the lack of extreme
temperature data in datasets, it is challenging for models to accurately
predict it. In this paper, we propose an improved loss function, which is
suitable for a variety of machine learning models. By increasing the weight of
extreme temperature samples and reducing the possibility of misjudging extreme
temperature as normal, the proposed loss function can enhance the prediction
results in extreme situations. To verify the effectiveness of the proposed
method, we implement the improved loss function in LightGBM, long short-term
memory, and artificial neural network and conduct experiments on a real-world
greenhouse dataset. The results show that the performance of models with the
improved loss function is enhanced compared to the original models in extreme
cases. The improved models can be used to guarantee the timely judgment of
extreme temperatures in agricultural greenhouses, thereby preventing
unnecessary losses caused by incorrect predictions.
| [
{
"version": "v1",
"created": "Tue, 2 Nov 2021 04:33:15 GMT"
}
] | 1,635,897,600,000 | [
[
"Qu",
"Liao",
""
],
[
"Huang",
"Shuaiqi",
""
],
[
"Jia",
"Yunsong",
""
],
[
"Li",
"Xiang",
""
]
] |
2111.01371 | Yongming Li | Fan Li, Xiaoheng Zhang, Pin Wang, Yongming Li | Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means
Clustering and Minimum Interlayer discrepancy | 21 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Imbalanced learning is important and challenging since the problem of the
classification of imbalanced datasets is prevalent in machine learning and data
mining fields. Sampling approaches are proposed to address this issue, and
cluster-based oversampling methods have shown great potential as they aim to
simultaneously tackle between-class and within-class imbalance issues. However,
all existing clustering methods are based on a one-time approach. Due to the
lack of a priori knowledge, improper setting of the number of clusters often
exists, which leads to poor clustering performance. Besides, the existing
methods are likely to generate noisy instances. To solve these problems, this
paper proposes a deep instance envelope network-based imbalanced learning
algorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayer
discrepancy mechanism based on the maximum mean discrepancy (MIDMD). This
algorithm can guarantee high quality balanced instances using a deep instance
envelope network in the absence of prior knowledge. In the experimental
section, thirty-three popular public datasets are used for verification, and
over ten representative algorithms are used for comparison. The experimental
results show that the proposed approach significantly outperforms other popular
methods.
| [
{
"version": "v1",
"created": "Tue, 2 Nov 2021 04:59:57 GMT"
}
] | 1,635,897,600,000 | [
[
"Li",
"Fan",
""
],
[
"Zhang",
"Xiaoheng",
""
],
[
"Wang",
"Pin",
""
],
[
"Li",
"Yongming",
""
]
] |
2111.01431 | Seokjun Kim | Seokjun Kim, Jaeeun Jang, Hyeoncheol Kim | Deductive Association Networks | A simple experiment was conducted as a series of artificial
association networks | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | we introduce deductive association networks(DANs), a network that performs
deductive reasoning. To have high-dimensional thinking, combining various
axioms and putting the results back into another axiom is necessary to produce
new relationships and results. For example, it would be given two propositions:
"Socrates is a man." and "All men are mortals." and two propositions could be
used to infer the new proposition, "Therefore Socrates is mortal.". To
evaluate, we used MNIST Dataset, a handwritten numerical image dataset, to
apply it to the group theory and show the results of performing deductive
learning.
| [
{
"version": "v1",
"created": "Tue, 2 Nov 2021 08:47:04 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Nov 2021 16:54:10 GMT"
},
{
"version": "v3",
"created": "Mon, 27 Dec 2021 17:41:53 GMT"
}
] | 1,640,649,600,000 | [
[
"Kim",
"Seokjun",
""
],
[
"Jang",
"Jaeeun",
""
],
[
"Kim",
"Hyeoncheol",
""
]
] |
2111.01726 | Nicholas Kantack | Nicholas Kantack, Nina Cohen, Nathan Bos, Corey Lowman, James Everett,
and Timothy Endres | Instructive artificial intelligence (AI) for human training, assistance,
and explainability | 10 pages, 6 figures, to be published in SPIE Defense & Commercial
Sensing (Artificial Intelligence and Machine Learning for Multi-Domain
Operations Applications IV) proceedings (April 2022) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose a novel approach to explainable AI (XAI) based on the concept of
"instruction" from neural networks. In this case study, we demonstrate how a
superhuman neural network might instruct human trainees as an alternative to
traditional approaches to XAI. Specifically, an AI examines human actions and
calculates variations on the human strategy that lead to better performance.
Experiments with a JHU/APL-developed AI player for the cooperative card game
Hanabi suggest this technique makes unique contributions to explainability
while improving human performance. One area of focus for Instructive AI is in
the significant discrepancies that can arise between a human's actual strategy
and the strategy they profess to use. This inaccurate self-assessment presents
a barrier for XAI, since explanations of an AI's strategy may not be properly
understood or implemented by human recipients. We have developed and are
testing a novel, Instructive AI approach that estimates human strategy by
observing human actions. With neural networks, this allows a direct calculation
of the changes in weights needed to improve the human strategy to better
emulate a more successful AI. Subjected to constraints (e.g. sparsity) these
weight changes can be interpreted as recommended changes to human strategy
(e.g. "value A more, and value B less"). Instruction from AI such as this
functions both to help humans perform better at tasks, but also to better
understand, anticipate, and correct the actions of an AI. Results will be
presented on AI instruction's ability to improve human decision-making and
human-AI teaming in Hanabi.
| [
{
"version": "v1",
"created": "Tue, 2 Nov 2021 16:46:46 GMT"
}
] | 1,635,897,600,000 | [
[
"Kantack",
"Nicholas",
""
],
[
"Cohen",
"Nina",
""
],
[
"Bos",
"Nathan",
""
],
[
"Lowman",
"Corey",
""
],
[
"Everett",
"James",
""
],
[
"Endres",
"Timothy",
""
]
] |
2111.01856 | Alexandre Ichida | Alexandre Yukio Ichida and Felipe Meneguzzi | Detecting Logical Relation In Contract Clauses | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Contracts underlie most modern commercial transactions defining define the
duties and obligations of the related parties in an agreement. Ensuring such
agreements are error free is crucial for modern society and their analysis of a
contract requires understanding the logical relations between clauses and
identifying potential contradictions. This analysis depends on error-prone
human effort to understand each contract clause. In this work, we develop an
approach to automate the extraction of logical relations between clauses in a
contract. We address this problem as a Natural Language Inference task to
detect the entailment type between two clauses in a contract. The resulting
approach should help contract authors detecting potential logical conflicts
between clauses.
| [
{
"version": "v1",
"created": "Tue, 2 Nov 2021 19:26:32 GMT"
}
] | 1,635,984,000,000 | [
[
"Ichida",
"Alexandre Yukio",
""
],
[
"Meneguzzi",
"Felipe",
""
]
] |
2111.02123 | Bruno Sartini | Bruno Sartini, Marieke van Erp, Aldo Gangemi | Marriage is a Peach and a Chalice: Modelling Cultural Symbolism on the
SemanticWeb | 8 pages, 5 figures | null | 10.1145/3460210.3493552 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this work, we fill the gap in the Semantic Web in the context of Cultural
Symbolism. Building upon earlier work in, we introduce the Simulation Ontology,
an ontology that models the background knowledge of symbolic meanings,
developed by combining the concepts taken from the authoritative theory of
Simulacra and Simulations of Jean Baudrillard with symbolic structures and
content taken from "Symbolism: a Comprehensive Dictionary" by Steven Olderr. We
re-engineered the symbolic knowledge already present in heterogeneous resources
by converting it into our ontology schema to create HyperReal, the first
knowledge graph completely dedicated to cultural symbolism. A first experiment
run on the knowledge graph is presented to show the potential of quantitative
research on symbolism.
| [
{
"version": "v1",
"created": "Wed, 3 Nov 2021 10:40:50 GMT"
}
] | 1,635,984,000,000 | [
[
"Sartini",
"Bruno",
""
],
[
"van Erp",
"Marieke",
""
],
[
"Gangemi",
"Aldo",
""
]
] |
2111.02244 | Ouren Kuiper | Ouren Kuiper, Martin van den Berg, Joost van der Burgt, Stefan Leijnen | Exploring Explainable AI in the Financial Sector: Perspectives of Banks
and Supervisory Authorities | BNAIC/BeneLearn 2021 conference paper | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Explainable artificial intelligence (xAI) is seen as a solution to making AI
systems less of a black box. It is essential to ensure transparency, fairness,
and accountability, which are especially paramount in the financial sector. The
aim of this study was a preliminary investigation of the perspectives of
supervisory authorities and regulated entities regarding the application of xAI
in the fi-nancial sector. Three use cases (consumer credit, credit risk, and
anti-money laundering) were examined using semi-structured interviews at three
banks and two supervisory authorities in the Netherlands. We found that for the
investigated use cases a disparity exists between supervisory authorities and
banks regarding the desired scope of explainability of AI systems. We argue
that the financial sector could benefit from clear differentiation between
technical AI (model) ex-plainability requirements and explainability
requirements of the broader AI system in relation to applicable laws and
regulations.
| [
{
"version": "v1",
"created": "Wed, 3 Nov 2021 14:11:37 GMT"
}
] | 1,636,329,600,000 | [
[
"Kuiper",
"Ouren",
""
],
[
"Berg",
"Martin van den",
""
],
[
"van der Burgt",
"Joost",
""
],
[
"Leijnen",
"Stefan",
""
]
] |
2111.02353 | Seokjun Kim | Seokjun Kim, Jaeeun Jang, Yeonju Jang, Seongyune Choi, Hyeoncheol Kim | Memory Association Networks | This study is part of a series and is a memory device in artificial
association neural networks | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce memory association networks(MANs) that memorize and remember any
data. This neural network has two memories. One consists of a queue-structured
short-term memory to solve the class imbalance problem and long-term memory to
store the distribution of objects, introducing the contents of storing and
generating various datasets.
| [
{
"version": "v1",
"created": "Wed, 3 Nov 2021 17:08:40 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Nov 2021 16:58:18 GMT"
},
{
"version": "v3",
"created": "Fri, 19 Nov 2021 13:16:03 GMT"
},
{
"version": "v4",
"created": "Mon, 27 Dec 2021 17:44:27 GMT"
}
] | 1,640,649,600,000 | [
[
"Kim",
"Seokjun",
""
],
[
"Jang",
"Jaeeun",
""
],
[
"Jang",
"Yeonju",
""
],
[
"Choi",
"Seongyune",
""
],
[
"Kim",
"Hyeoncheol",
""
]
] |
2111.02839 | Dennis Soemers | Dennis J. N. J. Soemers and \'Eric Piette and Matthew Stephenson and
Cameron Browne | Optimised Playout Implementations for the Ludii General Game System | Advances in Computer Games (ACG) 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes three different optimised implementations of playouts,
as commonly used by game-playing algorithms such as Monte-Carlo Tree Search.
Each of the optimised implementations is applicable only to specific sets of
games, based on their rules. The Ludii general game system can automatically
infer, based on a game's description in its general game description language,
whether any optimised implementations are applicable. An empirical evaluation
demonstrates major speedups over a standard implementation, with a median
result of running playouts 5.08 times as fast, over 145 different games in
Ludii for which one of the optimised implementations is applicable.
| [
{
"version": "v1",
"created": "Thu, 4 Nov 2021 12:59:53 GMT"
}
] | 1,636,070,400,000 | [
[
"Soemers",
"Dennis J. N. J.",
""
],
[
"Piette",
"Éric",
""
],
[
"Stephenson",
"Matthew",
""
],
[
"Browne",
"Cameron",
""
]
] |
2111.02859 | Aaron Baughman | Aaron Baughman, Daniel Bohm, Micah Forster, Eduardo Morales, Jeff
Powell, Shaun McPartlin, Raja Hebbar, Kavitha Yogaraj, Yoshika Chhabra,
Sudeep Ghosh, Rukhsan Ul Haq, Arjun Kashyap | Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football
Player Trades | 16 pages, 6 figures, 30 equations | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Even skilled fantasy football managers can be disappointed by their
mid-season rosters as some players inevitably fall short of draft day
expectations. Team managers can quickly discover that their team has a low
score ceiling even if they start their best active players. A novel and diverse
combinatorial optimization system proposes high volume and unique player trades
between complementary teams to balance trade fairness. Several algorithms
create the valuation of each fantasy football player with an ensemble of
computing models: Quantum Support Vector Classifier with Permutation Importance
(QSVC-PI), Quantum Support Vector Classifier with Accumulated Local Effects
(QSVC-ALE), Variational Quantum Circuit with Permutation Importance (VQC-PI),
Hybrid Quantum Neural Network with Permutation Importance (HQNN-PI), eXtreme
Gradient Boosting Classifier (XGB), and Subject Matter Expert (SME) rules. The
valuation of each player is personalized based on league rules, roster, and
selections. The cost of trading away a player is related to a team's roster,
such as the depth at a position, slot count, and position importance. Teams are
paired together for trading based on a cosine dissimilarity score so that teams
can offset their strengths and weaknesses. A knapsack 0-1 algorithm computes
outgoing players for each team. Postprocessors apply analytics and deep
learning models to measure 6 different objective measures about each trade.
Over the 2020 and 2021 National Football League (NFL) seasons, a group of 24
experts from IBM and ESPN evaluated trade quality through 10 Football Error
Analysis Tool (FEAT) sessions. Our system started with 76.9% of high-quality
trades and was deployed for the 2021 season with 97.3% of high-quality trades.
To increase trade quantity, our quantum, classical, and rules-based computing
have 100% trade uniqueness. We use Qiskit's quantum simulators throughout our
work.
| [
{
"version": "v1",
"created": "Thu, 4 Nov 2021 13:39:40 GMT"
},
{
"version": "v2",
"created": "Fri, 5 Nov 2021 01:00:57 GMT"
},
{
"version": "v3",
"created": "Tue, 19 Apr 2022 03:51:34 GMT"
}
] | 1,650,412,800,000 | [
[
"Baughman",
"Aaron",
""
],
[
"Bohm",
"Daniel",
""
],
[
"Forster",
"Micah",
""
],
[
"Morales",
"Eduardo",
""
],
[
"Powell",
"Jeff",
""
],
[
"McPartlin",
"Shaun",
""
],
[
"Hebbar",
"Raja",
""
],
[
"Yogaraj",
"Kavitha",
""
],
[
"Chhabra",
"Yoshika",
""
],
[
"Ghosh",
"Sudeep",
""
],
[
"Haq",
"Rukhsan Ul",
""
],
[
"Kashyap",
"Arjun",
""
]
] |
2111.03048 | Seokjun Kim | Seokjun Kim, Jaeeun Jang, Hyeoncheol Kim | Imagine Networks | This paper is the part of the artificial association neural networks
series we are studying | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce an imagine network that can simulate itself
through artificial association networks. Association, deduction, and memory
networks are learned, and a network is created by combining the discriminator
and reinforcement learning models. This model can learn various datasets or
data samples generated in environments and generate new data samples.
| [
{
"version": "v1",
"created": "Thu, 4 Nov 2021 17:51:13 GMT"
},
{
"version": "v2",
"created": "Fri, 5 Nov 2021 07:40:29 GMT"
},
{
"version": "v3",
"created": "Wed, 17 Nov 2021 17:04:21 GMT"
},
{
"version": "v4",
"created": "Mon, 27 Dec 2021 17:40:34 GMT"
},
{
"version": "v5",
"created": "Thu, 30 Dec 2021 03:58:38 GMT"
}
] | 1,641,168,000,000 | [
[
"Kim",
"Seokjun",
""
],
[
"Jang",
"Jaeeun",
""
],
[
"Kim",
"Hyeoncheol",
""
]
] |
2111.03059 | Joao P. A. Dantas | Joao P. A. Dantas, Andre N. Costa, Diego Geraldo, Marcos R. O. A.
Maximo and Takashi Yoneyama | Engagement Decision Support for Beyond Visual Range Air Combat | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This work aims to provide an engagement decision support tool for Beyond
Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA)
missions. In BVR air combat, engagement decision refers to the choice of the
moment the pilot engages a target by assuming an offensive stance and executing
corresponding maneuvers. To model this decision, we use the Brazilian Air
Force's Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao
Aeroespacial - ASA in Portuguese), which generated 3,729 constructive
simulations lasting 12 minutes each and a total of 10,316 engagements. We
analyzed all samples by an operational metric called the DCA index, which
represents, based on the experience of subject matter experts, the degree of
success in this type of mission. This metric considers the distances of the
aircraft of the same team and the opposite team, the point of Combat Air
Patrol, and the number of missiles used. By defining the engagement status
right before it starts and the average of the DCA index throughout the
engagement, we create a supervised learning model to determine the quality of a
new engagement. An algorithm based on decision trees, working with the XGBoost
library, provides a regression model to predict the DCA index with a
coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05
that can furnish parameters to the BVR pilot to decide whether or not to
engage. Thus, using data obtained through simulations, this work contributes by
building a decision support system based on machine learning for BVR air
combat.
| [
{
"version": "v1",
"created": "Thu, 4 Nov 2021 17:59:45 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Nov 2021 20:17:32 GMT"
}
] | 1,637,280,000,000 | [
[
"Dantas",
"Joao P. A.",
""
],
[
"Costa",
"Andre N.",
""
],
[
"Geraldo",
"Diego",
""
],
[
"Maximo",
"Marcos R. O. A.",
""
],
[
"Yoneyama",
"Takashi",
""
]
] |
2111.03204 | Enpeng Yuan | Enpeng Yuan, Pascal Van Hentenryck | Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle
Relocation and Pricing Decisions | arXiv admin note: text overlap with arXiv:2105.13461 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large-scale ride-hailing systems often combine real-time routing at the
individual request level with a macroscopic Model Predictive Control (MPC)
optimization for dynamic pricing and vehicle relocation. The MPC relies on a
demand forecast and optimizes over a longer time horizon to compensate for the
myopic nature of the routing optimization. However, the longer horizon
increases computational complexity and forces the MPC to operate at coarser
spatial-temporal granularity, degrading the quality of its decisions. This
paper addresses these computational challenges by learning the MPC
optimization. The resulting machine-learning model then serves as the
optimization proxy and predicts its optimal solutions. This makes it possible
to use the MPC at higher spatial-temporal fidelity, since the optimizations can
be solved and learned offline. Experimental results show that the proposed
approach improves quality of service on challenging instances from the New York
City dataset.
| [
{
"version": "v1",
"created": "Fri, 5 Nov 2021 00:52:15 GMT"
}
] | 1,636,329,600,000 | [
[
"Yuan",
"Enpeng",
""
],
[
"Van Hentenryck",
"Pascal",
""
]
] |
2111.03647 | Nicky Lenaers | Nicky Lenaers and Martijn van Otterlo | Regular Decision Processes for Grid Worlds | 21 pages, 10 figures, accepted for oral presentation at the AI & ML
conference for Belgium, Netherlands & Luxemburg (BNAIC/BeneLearn 2021), 10-12
November, Luxembourg | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Markov decision processes are typically used for sequential decision making
under uncertainty. For many aspects however, ranging from constrained or safe
specifications to various kinds of temporal (non-Markovian) dependencies in
task and reward structures, extensions are needed. To that end, in recent years
interest has grown into combinations of reinforcement learning and temporal
logic, that is, combinations of flexible behavior learning methods with robust
verification and guarantees. In this paper we describe an experimental
investigation of the recently introduced regular decision processes that
support both non-Markovian reward functions as well as transition functions. In
particular, we provide a tool chain for regular decision processes, algorithmic
extensions relating to online, incremental learning, an empirical evaluation of
model-free and model-based solution algorithms, and applications in regular,
but non-Markovian, grid worlds.
| [
{
"version": "v1",
"created": "Fri, 5 Nov 2021 17:54:43 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Nov 2021 08:55:21 GMT"
}
] | 1,636,502,400,000 | [
[
"Lenaers",
"Nicky",
""
],
[
"van Otterlo",
"Martijn",
""
]
] |
2111.03728 | Mihai Boicu | Gheorghe Tecuci, Dorin Marcu, Louis Kaiser and Mihai Boicu | Shared Model of Sense-making for Human-Machine Collaboration | Presented at AAAI FSS-21: Artificial Intelligence in Government and
Public Sector, Washington, DC, USA | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We present a model of sense-making that greatly facilitates the collaboration
between an intelligent analyst and a knowledge-based agent. It is a general
model grounded in the science of evidence and the scientific method of
hypothesis generation and testing, where sense-making hypotheses that explain
an observation are generated, relevant evidence is then discovered, and the
hypotheses are tested based on the discovered evidence. We illustrate how the
model enables an analyst to directly instruct the agent to understand
situations involving the possible production of weapons (e.g., chemical warfare
agents) and how the agent becomes increasingly more competent in understanding
other situations from that domain (e.g., possible production of
centrifuge-enriched uranium or of stealth fighter aircraft).
| [
{
"version": "v1",
"created": "Fri, 5 Nov 2021 21:08:54 GMT"
}
] | 1,636,416,000,000 | [
[
"Tecuci",
"Gheorghe",
""
],
[
"Marcu",
"Dorin",
""
],
[
"Kaiser",
"Louis",
""
],
[
"Boicu",
"Mihai",
""
]
] |
2111.03796 | Donsuk Lee | Donsuk Lee, Samantha M. W. Wood, Justin N. Wood | Development of collective behavior in newborn artificial agents | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Collective behavior is widespread across the animal kingdom. To date,
however, the developmental and mechanistic foundations of collective behavior
have not been formally established. What learning mechanisms drive the
development of collective behavior in newborn animals? Here, we used deep
reinforcement learning and curiosity-driven learning -- two learning mechanisms
deeply rooted in psychological and neuroscientific research -- to build newborn
artificial agents that develop collective behavior. Like newborn animals, our
agents learn collective behavior from raw sensory inputs in naturalistic
environments. Our agents also learn collective behavior without external
rewards, using only intrinsic motivation (curiosity) to drive learning.
Specifically, when we raise our artificial agents in natural visual
environments with groupmates, the agents spontaneously develop ego-motion,
object recognition, and a preference for groupmates, rapidly learning all of
the core skills required for collective behavior. This work bridges the divide
between high-dimensional sensory inputs and collective action, resulting in a
pixels-to-actions model of collective animal behavior. More generally, we show
that two generic learning mechanisms -- deep reinforcement learning and
curiosity-driven learning -- are sufficient to learn collective behavior from
unsupervised natural experience.
| [
{
"version": "v1",
"created": "Sat, 6 Nov 2021 03:46:31 GMT"
}
] | 1,636,416,000,000 | [
[
"Lee",
"Donsuk",
""
],
[
"Wood",
"Samantha M. W.",
""
],
[
"Wood",
"Justin N.",
""
]
] |
2111.04051 | Zf Wu | Zifan Wu, Chao Yu, Deheng Ye, Junge Zhang, Haiyin Piao, Hankz Hankui
Zhuo | Coordinated Proximal Policy Optimization | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present Coordinated Proximal Policy Optimization (CoPPO), an algorithm
that extends the original Proximal Policy Optimization (PPO) to the multi-agent
setting. The key idea lies in the coordinated adaptation of step size during
the policy update process among multiple agents. We prove the monotonicity of
policy improvement when optimizing a theoretically-grounded joint objective,
and derive a simplified optimization objective based on a set of
approximations. We then interpret that such an objective in CoPPO can achieve
dynamic credit assignment among agents, thereby alleviating the high variance
issue during the concurrent update of agent policies. Finally, we demonstrate
that CoPPO outperforms several strong baselines and is competitive with the
latest multi-agent PPO method (i.e. MAPPO) under typical multi-agent settings,
including cooperative matrix games and the StarCraft II micromanagement tasks.
| [
{
"version": "v1",
"created": "Sun, 7 Nov 2021 11:14:19 GMT"
}
] | 1,636,416,000,000 | [
[
"Wu",
"Zifan",
""
],
[
"Yu",
"Chao",
""
],
[
"Ye",
"Deheng",
""
],
[
"Zhang",
"Junge",
""
],
[
"Piao",
"Haiyin",
""
],
[
"Zhuo",
"Hankz Hankui",
""
]
] |
2111.04997 | Jos\'e \'A. Segura-Muros | Jos\'e \'A. Segura-Muros and Juan Fern\'andez-Olivares and Ra\'ul
P\'erez | Learning Numerical Action Models from Noisy Input Data | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper presents the PlanMiner-N algorithm, a domain learning technique
based on the PlanMiner domain learning algorithm. The algorithm presented here
improves the learning capabilities of PlanMiner when using noisy data as input.
The PlanMiner algorithm is able to infer arithmetic and logical expressions to
learn numerical planning domains from the input data, but it was designed to
work under situations of incompleteness making it unreliable when facing noisy
input data. In this paper, we propose a series of enhancements to the learning
process of PlanMiner to expand its capabilities to learn from noisy data. These
methods preprocess the input data by detecting noise and filtering it and study
the learned action models learned to find erroneous preconditions/effects in
them. The methods proposed in this paper were tested using a set of domains
from the International Planning Competition (IPC). The results obtained
indicate that PlanMiner-N improves the performance of PlanMiner greatly when
facing noisy input data.
| [
{
"version": "v1",
"created": "Tue, 9 Nov 2021 08:36:23 GMT"
}
] | 1,636,502,400,000 | [
[
"Segura-Muros",
"José Á.",
""
],
[
"Fernández-Olivares",
"Juan",
""
],
[
"Pérez",
"Raúl",
""
]
] |
2111.05157 | Stefania Costantini | Stefania Costantini | Self-checking Logical Agents | Proceedings currently not available on the web | Proceedings of the Eighth Latin American Workshop on
Logic/Languages, Algorithms and New Methods of Reasoning 2012, CEUR Workshop
Proceedings 911, pp. 3-30, Invited paper | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a comprehensive framework for run-time self-checking of
logical agents, by means of temporal axioms to be dynamically checked. These
axioms are specified by using an agent-oriented interval temporal logic defined
to this purpose. We define syntax, semantics and pragmatics for this new logic,
specifically tailored for application to agents. In the resulting framework, we
encompass and extend our past work.
| [
{
"version": "v1",
"created": "Tue, 9 Nov 2021 14:13:41 GMT"
}
] | 1,636,502,400,000 | [
[
"Costantini",
"Stefania",
""
]
] |
2111.05514 | Dohae Lee | Dohae Lee, Young Jin Oh, and In-Kwon Lee | Discovering Latent Representations of Relations for Interacting Systems | Accepted by IEEE Access on Oct. 25, 2021 | null | 10.1109/ACCESS.2021.3125335 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Systems whose entities interact with each other are common. In many
interacting systems, it is difficult to observe the relations between entities
which is the key information for analyzing the system. In recent years, there
has been increasing interest in discovering the relationships between entities
using graph neural networks. However, existing approaches are difficult to
apply if the number of relations is unknown or if the relations are complex. We
propose the DiScovering Latent Relation (DSLR) model, which is flexibly
applicable even if the number of relations is unknown or many types of
relations exist. The flexibility of our DSLR model comes from the design
concept of our encoder that represents the relation between entities in a
latent space rather than a discrete variable and a decoder that can handle many
types of relations. We performed the experiments on synthetic and real-world
graph data with various relationships between entities, and compared the
qualitative and quantitative results with other approaches. The experiments
show that the proposed method is suitable for analyzing dynamic graphs with an
unknown number of complex relations.
| [
{
"version": "v1",
"created": "Wed, 10 Nov 2021 03:32:09 GMT"
}
] | 1,636,588,800,000 | [
[
"Lee",
"Dohae",
""
],
[
"Oh",
"Young Jin",
""
],
[
"Lee",
"In-Kwon",
""
]
] |
2111.05527 | Yizhou Zhao | Yizhou Zhao, Kaixiang Lin, Zhiwei Jia, Qiaozi Gao, Govind Thattai,
Jesse Thomason, Gaurav S.Sukhatme | LUMINOUS: Indoor Scene Generation for Embodied AI Challenges | 2021 paper, Amazon | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning-based methods for training embodied agents typically require a large
number of high-quality scenes that contain realistic layouts and support
meaningful interactions. However, current simulators for Embodied AI (EAI)
challenges only provide simulated indoor scenes with a limited number of
layouts. This paper presents Luminous, the first research framework that
employs state-of-the-art indoor scene synthesis algorithms to generate
large-scale simulated scenes for Embodied AI challenges. Further, we
automatically and quantitatively evaluate the quality of generated indoor
scenes via their ability to support complex household tasks. Luminous
incorporates a novel scene generation algorithm (Constrained Stochastic Scene
Generation (CSSG)), which achieves competitive performance with human-designed
scenes. Within Luminous, the EAI task executor, task instruction generation
module, and video rendering toolkit can collectively generate a massive
multimodal dataset of new scenes for the training and evaluation of Embodied AI
agents. Extensive experimental results demonstrate the effectiveness of the
data generated by Luminous, enabling the comprehensive assessment of embodied
agents on generalization and robustness.
| [
{
"version": "v1",
"created": "Wed, 10 Nov 2021 04:43:42 GMT"
}
] | 1,636,588,800,000 | [
[
"Zhao",
"Yizhou",
""
],
[
"Lin",
"Kaixiang",
""
],
[
"Jia",
"Zhiwei",
""
],
[
"Gao",
"Qiaozi",
""
],
[
"Thattai",
"Govind",
""
],
[
"Thomason",
"Jesse",
""
],
[
"Sukhatme",
"Gaurav S.",
""
]
] |
2111.05819 | Yunkun Xu | Yunkun Xu, Zhenyu Liu, Guifang Duan, Jiangcheng Zhu, Xiaolong Bai,
Jianrong Tan | Look Before You Leap: Safe Model-Based Reinforcement Learning with Human
Intervention | CoRL 2021 accepted | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Safety has become one of the main challenges of applying deep reinforcement
learning to real world systems. Currently, the incorporation of external
knowledge such as human oversight is the only means to prevent the agent from
visiting the catastrophic state. In this paper, we propose MBHI, a novel
framework for safe model-based reinforcement learning, which ensures safety in
the state-level and can effectively avoid both "local" and "non-local"
catastrophes. An ensemble of supervised learners are trained in MBHI to imitate
human blocking decisions. Similar to human decision-making process, MBHI will
roll out an imagined trajectory in the dynamics model before executing actions
to the environment, and estimate its safety. When the imagination encounters a
catastrophe, MBHI will block the current action and use an efficient MPC method
to output a safety policy. We evaluate our method on several safety tasks, and
the results show that MBHI achieved better performance in terms of sample
efficiency and number of catastrophes compared to the baselines.
| [
{
"version": "v1",
"created": "Wed, 10 Nov 2021 17:25:37 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Nov 2021 12:43:05 GMT"
}
] | 1,637,107,200,000 | [
[
"Xu",
"Yunkun",
""
],
[
"Liu",
"Zhenyu",
""
],
[
"Duan",
"Guifang",
""
],
[
"Zhu",
"Jiangcheng",
""
],
[
"Bai",
"Xiaolong",
""
],
[
"Tan",
"Jianrong",
""
]
] |
2111.05884 | Martin Schmid | Martin Schmid | Search in Imperfect Information Games | doctoral thesis | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | From the very dawn of the field, search with value functions was a
fundamental concept of computer games research. Turing's chess algorithm from
1950 was able to think two moves ahead, and Shannon's work on chess from $1950$
includes an extensive section on evaluation functions to be used within a
search. Samuel's checkers program from 1959 already combines search and value
functions that are learned through self-play and bootstrapping. TD-Gammon
improves upon those ideas and uses neural networks to learn those complex value
functions -- only to be again used within search. The combination of
decision-time search and value functions has been present in the remarkable
milestones where computers bested their human counterparts in long standing
challenging games -- DeepBlue for Chess and AlphaGo for Go. Until recently,
this powerful framework of search aided with (learned) value functions has been
limited to perfect information games. As many interesting problems do not
provide the agent perfect information of the environment, this was an
unfortunate limitation. This thesis introduces the reader to sound search for
imperfect information games.
| [
{
"version": "v1",
"created": "Wed, 10 Nov 2021 19:06:15 GMT"
}
] | 1,636,675,200,000 | [
[
"Schmid",
"Martin",
""
]
] |
2111.06366 | Seemran Mishra | Jorge Fandinno, Seemran Mishra, Javier Romero, Torsten Schaub | Answer Set Programming Made Easy | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We take up an idea from the folklore of Answer Set Programming, namely that
choices, integrity constraints along with a restricted rule format is
sufficient for Answer Set Programming. We elaborate upon the foundations of
this idea in the context of the logic of Here-and-There and show how it can be
derived from the logical principle of extension by definition. We then provide
an austere form of logic programs that may serve as a normalform for logic
programs similar to conjunctive normalform in classical logic. Finally, we take
the key ideas and propose a modeling methodology for ASP beginners and
illustrate how it can be used.
| [
{
"version": "v1",
"created": "Thu, 11 Nov 2021 18:27:09 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Nov 2021 15:00:19 GMT"
}
] | 1,637,798,400,000 | [
[
"Fandinno",
"Jorge",
""
],
[
"Mishra",
"Seemran",
""
],
[
"Romero",
"Javier",
""
],
[
"Schaub",
"Torsten",
""
]
] |
2111.06803 | Christopher Gagne | Chris Gagne and Peter Dayan | Two steps to risk sensitivity | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distributional reinforcement learning (RL) -- in which agents learn about all
the possible long-term consequences of their actions, and not just the expected
value -- is of great recent interest. One of the most important affordances of
a distributional view is facilitating a modern, measured, approach to risk when
outcomes are not completely certain. By contrast, psychological and
neuroscientific investigations into decision making under risk have utilized a
variety of more venerable theoretical models such as prospect theory that lack
axiomatically desirable properties such as coherence. Here, we consider a
particularly relevant risk measure for modeling human and animal planning,
called conditional value-at-risk (CVaR), which quantifies worst-case outcomes
(e.g., vehicle accidents or predation). We first adopt a conventional
distributional approach to CVaR in a sequential setting and reanalyze the
choices of human decision-makers in the well-known two-step task, revealing
substantial risk aversion that had been lurking under stickiness and
perseveration. We then consider a further critical property of risk
sensitivity, namely time consistency, showing alternatives to this form of CVaR
that enjoy this desirable characteristic. We use simulations to examine
settings in which the various forms differ in ways that have implications for
human and animal planning and behavior.
| [
{
"version": "v1",
"created": "Fri, 12 Nov 2021 16:27:47 GMT"
}
] | 1,636,934,400,000 | [
[
"Gagne",
"Chris",
""
],
[
"Dayan",
"Peter",
""
]
] |
2111.06804 | Christopher Gagne | Chris Gagne and Peter Dayan | Catastrophe, Compounding & Consistency in Choice | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conditional value-at-risk (CVaR) precisely characterizes the influence that
rare, catastrophic events can exert over decisions. Such characterizations are
important for both normal decision-making and for psychiatric conditions such
as anxiety disorders -- especially for sequences of decisions that might
ultimately lead to disaster. CVaR, like other well-founded risk measures,
compounds in complex ways over such sequences -- and we recently formalized
three structurally different forms in which risk either averages out or
multiplies. Unfortunately, existing cognitive tasks fail to discriminate these
approaches well; here, we provide examples that highlight their unique
characteristics, and make formal links to temporal discounting for the two of
the approaches that are time consistent. These examples can ground future
experiments with the broader aim of characterizing risk attitudes, especially
for longer horizon problems and in psychopathological populations.
| [
{
"version": "v1",
"created": "Fri, 12 Nov 2021 16:33:06 GMT"
}
] | 1,636,934,400,000 | [
[
"Gagne",
"Chris",
""
],
[
"Dayan",
"Peter",
""
]
] |
2111.06854 | Ling Cai | Ling Cai, Krzysztof Janowic, Bo Yan, Rui Zhu and Gengchen Mai | Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes | null | null | 10.1145/3460210.3493566 | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Almost all statements in knowledge bases have a temporal scope during which
they are valid. Hence, knowledge base completion (KBC) on temporal knowledge
bases (TKB), where each statement \textit{may} be associated with a temporal
scope, has attracted growing attention. Prior works assume that each statement
in a TKB \textit{must} be associated with a temporal scope. This ignores the
fact that the scoping information is commonly missing in a KB. Thus prior work
is typically incapable of handling generic use cases where a TKB is composed of
temporal statements with/without a known temporal scope. In order to address
this issue, we establish a new knowledge base embedding framework, called
TIME2BOX, that can deal with atemporal and temporal statements of different
types simultaneously. Our main insight is that answers to a temporal query
always belong to a subset of answers to a time-agnostic counterpart. Put
differently, time is a filter that helps pick out answers to be correct during
certain periods. We introduce boxes to represent a set of answer entities to a
time-agnostic query. The filtering functionality of time is modeled by
intersections over these boxes. In addition, we generalize current evaluation
protocols on time interval prediction. We describe experiments on two datasets
and show that the proposed method outperforms state-of-the-art (SOTA) methods
on both link prediction and time prediction.
| [
{
"version": "v1",
"created": "Fri, 12 Nov 2021 18:17:07 GMT"
}
] | 1,636,934,400,000 | [
[
"Cai",
"Ling",
""
],
[
"Janowic",
"Krzysztof",
""
],
[
"Yan",
"Bo",
""
],
[
"Zhu",
"Rui",
""
],
[
"Mai",
"Gengchen",
""
]
] |
2111.06908 | Yanou Ramon | Yanou Ramon, Sandra C. Matz, R.A. Farrokhnia, David Martens | Explainable AI for Psychological Profiling from Digital Footprints: A
Case Study of Big Five Personality Predictions from Spending Data | 24 pages, 12 figures, 6 tables | null | 10.3390/info12120518 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Every step we take in the digital world leaves behind a record of our
behavior; a digital footprint. Research has suggested that algorithms can
translate these digital footprints into accurate estimates of psychological
characteristics, including personality traits, mental health or intelligence.
The mechanisms by which AI generates these insights, however, often remain
opaque. In this paper, we show how Explainable AI (XAI) can help domain experts
and data subjects validate, question, and improve models that classify
psychological traits from digital footprints. We elaborate on two popular XAI
methods (rule extraction and counterfactual explanations) in the context of Big
Five personality predictions (traits and facets) from financial transactions
data (N = 6,408). First, we demonstrate how global rule extraction sheds light
on the spending patterns identified by the model as most predictive for
personality, and discuss how these rules can be used to explain, validate, and
improve the model. Second, we implement local rule extraction to show that
individuals are assigned to personality classes because of their unique
financial behavior, and that there exists a positive link between the model's
prediction confidence and the number of features that contributed to the
prediction. Our experiments highlight the importance of both global and local
XAI methods. By better understanding how predictive models work in general as
well as how they derive an outcome for a particular person, XAI promotes
accountability in a world in which AI impacts the lives of billions of people
around the world.
| [
{
"version": "v1",
"created": "Fri, 12 Nov 2021 19:28:56 GMT"
}
] | 1,639,699,200,000 | [
[
"Ramon",
"Yanou",
""
],
[
"Matz",
"Sandra C.",
""
],
[
"Farrokhnia",
"R. A.",
""
],
[
"Martens",
"David",
""
]
] |
2111.06928 | Tristan Cazenave | Julien Sentuc and Tristan Cazenave and Jean-Yves Lucas | Generalized Nested Rollout Policy Adaptation with Dynamic Bias for
Vehicle Routing | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper we present an extension of the Nested Rollout Policy Adaptation
algorithm (NRPA), namely the Generalized Nested Rollout Policy Adaptation
(GNRPA), as well as its use for solving some instances of the Vehicle Routing
Problem. We detail some results obtained on the Solomon instances set which is
a conventional benchmark for the Vehicle Routing Problem (VRP). We show that on
all instances, GNRPA performs better than NRPA. On some instances, it performs
better than the Google OR Tool module dedicated to VRP.
| [
{
"version": "v1",
"created": "Fri, 12 Nov 2021 20:30:12 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Dec 2021 18:29:14 GMT"
}
] | 1,640,822,400,000 | [
[
"Sentuc",
"Julien",
""
],
[
"Cazenave",
"Tristan",
""
],
[
"Lucas",
"Jean-Yves",
""
]
] |
2111.07263 | Tenzin Jinpa | Tenzin Jinpa and Yong Gao | Code Representation Learning with Pr\"ufer Sequences | Paper has been accepted in AAAI-22 Student Abstract and Poster
Program (SA-22) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An effective and efficient encoding of the source code of a computer program
is critical to the success of sequence-to-sequence deep neural network models
for tasks in computer program comprehension, such as automated code
summarization and documentation. A significant challenge is to find a
sequential representation that captures the structural/syntactic information in
a computer program and facilitates the training of the learning models.
In this paper, we propose to use the Pr\"ufer sequence of the Abstract Syntax
Tree (AST) of a computer program to design a sequential representation scheme
that preserves the structural information in an AST. Our representation makes
it possible to develop deep-learning models in which signals carried by lexical
tokens in the training examples can be exploited automatically and selectively
based on their syntactic role and importance. Unlike other recently-proposed
approaches, our representation is concise and lossless in terms of the
structural information of the AST. Empirical studies on real-world benchmark
datasets, using a sequence-to-sequence learning model we designed for code
summarization, show that our Pr\"ufer-sequence-based representation is indeed
highly effective and efficient, outperforming significantly all the
recently-proposed deep-learning models we used as the baseline models.
| [
{
"version": "v1",
"created": "Sun, 14 Nov 2021 07:27:38 GMT"
}
] | 1,637,020,800,000 | [
[
"Jinpa",
"Tenzin",
""
],
[
"Gao",
"Yong",
""
]
] |
2111.07568 | Minghao Liu | Minghao Liu, Fuqi Jia, Pei Huang, Fan Zhang, Yuchen Sun, Shaowei Cai,
Feifei Ma, Jian Zhang | Can Graph Neural Networks Learn to Solve MaxSAT Problem? | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With the rapid development of deep learning techniques, various recent work
has tried to apply graph neural networks (GNNs) to solve NP-hard problems such
as Boolean Satisfiability (SAT), which shows the potential in bridging the gap
between machine learning and symbolic reasoning. However, the quality of
solutions predicted by GNNs has not been well investigated in the literature.
In this paper, we study the capability of GNNs in learning to solve Maximum
Satisfiability (MaxSAT) problem, both from theoretical and practical
perspectives. We build two kinds of GNN models to learn the solution of MaxSAT
instances from benchmarks, and show that GNNs have attractive potential to
solve MaxSAT problem through experimental evaluation. We also present a
theoretical explanation of the effect that GNNs can learn to solve MaxSAT
problem to some extent for the first time, based on the algorithmic alignment
theory.
| [
{
"version": "v1",
"created": "Mon, 15 Nov 2021 07:33:33 GMT"
}
] | 1,637,020,800,000 | [
[
"Liu",
"Minghao",
""
],
[
"Jia",
"Fuqi",
""
],
[
"Huang",
"Pei",
""
],
[
"Zhang",
"Fan",
""
],
[
"Sun",
"Yuchen",
""
],
[
"Cai",
"Shaowei",
""
],
[
"Ma",
"Feifei",
""
],
[
"Zhang",
"Jian",
""
]
] |
2111.07631 | Qiyue Yin | Qiyue Yin, Jun Yang, Kaiqi Huang, Meijing Zhao, Wancheng Ni, Bin
Liang, Yan Huang, Shu Wu, Liang Wang | AI in Human-computer Gaming: Techniques, Challenges and Opportunities | null | Machine Intelligence Research, 2023
(https://link.springer.com/article/10.1007/s11633-022-1384-6) | 10.1007/s11633-022-1384-6 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With breakthrough of the AlphaGo, human-computer gaming AI has ushered in a
big explosion, attracting more and more researchers all around the world. As a
recognized standard for testing artificial intelligence, various human-computer
gaming AI systems (AIs) have been developed such as the Libratus, OpenAI Five
and AlphaStar, beating professional human players. The rapid development of
human-computer gaming AIs indicate a big step of decision making intelligence,
and it seems that current techniques can handle very complex human-computer
games. So, one natural question raises: what are the possible challenges of
current techniques in human-computer gaming, and what are the future trends? To
answer the above question, in this paper, we survey recent successful game AIs,
covering board game AIs, card game AIs, first-person shooting game AIs and real
time strategy game AIs. Through this survey, we 1) compare the main
difficulties among different kinds of games and the corresponding techniques
utilized for achieving professional human level AIs; 2) summarize the
mainstream frameworks and techniques that can be properly relied on for
developing AIs for complex human-computer gaming; 3) raise the challenges or
drawbacks of current techniques in the successful AIs; and 4) try to point out
future trends in human-computer gaming AIs. Finally, we hope this brief review
can provide an introduction for beginners, and inspire insights for researchers
in the field of AI in human-computer gaming.
| [
{
"version": "v1",
"created": "Mon, 15 Nov 2021 09:35:53 GMT"
},
{
"version": "v2",
"created": "Thu, 18 Aug 2022 01:56:59 GMT"
}
] | 1,711,929,600,000 | [
[
"Yin",
"Qiyue",
""
],
[
"Yang",
"Jun",
""
],
[
"Huang",
"Kaiqi",
""
],
[
"Zhao",
"Meijing",
""
],
[
"Ni",
"Wancheng",
""
],
[
"Liang",
"Bin",
""
],
[
"Huang",
"Yan",
""
],
[
"Wu",
"Shu",
""
],
[
"Wang",
"Liang",
""
]
] |
2111.07648 | Gonzalo Imaz | Gonzalo E. Imaz | The Possibilistic Horn Non-Clausal Knowledge Bases | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Posibilistic logic is the most extended approach to handle uncertain and
partially inconsistent information. Regarding normal forms, advances in
possibilistic reasoning are mostly focused on clausal form. Yet, the encoding
of real-world problems usually results in a non-clausal (NC) formula and
NC-to-clausal translators produce severe drawbacks that heavily limit the
practical performance of clausal reasoning. Thus, by computing formulas in its
original NC form, we propose several contributions showing that notable
advances are also possible in possibilistic non-clausal reasoning.
{\em Firstly,} we define the class of {\em Possibilistic Horn Non-Clausal
Knowledge Bases,} or $\mathcal{\overline{H}}_\Sigma$, which subsumes the
classes: possibilistic Horn and propositional Horn-NC.
$\mathcal{\overline{H}}_\Sigma $ is shown to be a kind of NC analogous of the
standard Horn class.
{\em Secondly}, we define {\em Possibilistic Non-Clausal Unit-Resolution,} or
$ \mathcal{UR}_\Sigma $, and prove that $ \mathcal{UR}_\Sigma $ correctly
computes the inconsistency degree of $\mathcal{\overline{H}}_\Sigma $members.
$\mathcal{UR}_\Sigma $ had not been proposed before and is formulated in a
clausal-like manner, which eases its understanding, formal proofs and future
extension towards non-clausal resolution.
{\em Thirdly}, we prove that computing the inconsistency degree of
$\mathcal{\overline{H}}_\Sigma $ members takes polynomial time. Although there
already exist tractable classes in possibilistic logic, all of them are
clausal, and thus, $\mathcal{\overline{H}}_\Sigma $ turns out to be the first
characterized polynomial non-clausal class within possibilistic reasoning.
| [
{
"version": "v1",
"created": "Mon, 15 Nov 2021 10:18:49 GMT"
}
] | 1,637,020,800,000 | [
[
"Imaz",
"Gonzalo E.",
""
]
] |
2111.07734 | Soeren Hougaard Mulvad | Nguyen Van Hoang and Soeren Hougaard Mulvad and Dexter Neo Yuan Rong
and Yang Yue | Zero-Shot Learning in Named-Entity Recognition with External Knowledge | 4 main pages, 5 including broader impact and references. 4 figures. 2
equations. 2 tables. For code, see https://github.com/shmulvad/zero-for-ner | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A significant shortcoming of current state-of-the-art (SOTA) named-entity
recognition (NER) systems is their lack of generalization to unseen domains,
which poses a major problem since obtaining labeled data for NER in a new
domain is expensive and time-consuming. We propose ZERO, a model that performs
zero-shot and few-shot learning in NER to generalize to unseen domains by
incorporating pre-existing knowledge in the form of semantic word embeddings.
ZERO first obtains contextualized word representations of input sentences using
the model LUKE, reduces their dimensionality, and compares them directly with
the embeddings of the external knowledge, allowing ZERO to be trained to
recognize unseen output entities. We find that ZERO performs well on unseen NER
domains with an average macro F1 score of 0.23, outperforms LUKE in few-shot
learning, and even achieves competitive scores on an in-domain comparison. The
performance across source-target domain pairs is shown to be inversely
correlated with the pairs' KL divergence.
| [
{
"version": "v1",
"created": "Mon, 15 Nov 2021 13:28:27 GMT"
}
] | 1,637,020,800,000 | [
[
"Van Hoang",
"Nguyen",
""
],
[
"Mulvad",
"Soeren Hougaard",
""
],
[
"Rong",
"Dexter Neo Yuan",
""
],
[
"Yue",
"Yang",
""
]
] |
2111.07765 | Jobst Landgrebe | Jobst Landgrebe, Barry Smith | An argument for the impossibility of machine intelligence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Since the noun phrase `artificial intelligence' (AI) was coined, it has been
debated whether humans are able to create intelligence using technology. We
shed new light on this question from the point of view of themodynamics and
mathematics. First, we define what it is to be an agent (device) that could be
the bearer of AI. Then we show that the mainstream definitions of
`intelligence' proposed by Hutter and others and still accepted by the AI
community are too weak even to capture what is involved when we ascribe
intelligence to an insect. We then summarise the highly useful definition of
basic (arthropod) intelligence proposed by Rodney Brooks, and we identify the
properties that an AI agent would need to possess in order to be the bearer of
intelligence by this definition. Finally, we show that, from the perspective of
the disciplines needed to create such an agent, namely mathematics and physics,
these properties are realisable by neither implicit nor explicit mathematical
design nor by setting up an environment in which an AI could evolve
spontaneously.
| [
{
"version": "v1",
"created": "Wed, 20 Oct 2021 08:54:48 GMT"
}
] | 1,637,020,800,000 | [
[
"Landgrebe",
"Jobst",
""
],
[
"Smith",
"Barry",
""
]
] |
2111.07779 | Sekou Remy | Sekou L Remy, Aisha Walcott-Bryant, Nelson K Bore, Charles M Wachira,
Julian Kuenhert | Overcoming Digital Gravity when using AI in Public Health Decisions | Presented at AAAI FSS-21: Artificial Intelligence in Government and
Public Sector, Washington, DC, USA | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In popular usage, Data Gravity refers to the ability of a body of data to
attract applications, services and other data. In this work we introduce a
broader concept, "Digital Gravity" which includes not just data, but other
elements of the AI/ML workflow. This concept is born out of our recent
experiences in developing and deploying an AI-based decision support platform
intended for use in a public health context. In addition to data, examples of
additional considerations are compute (infrastructure and software), DevSecOps
(personnel and practices), algorithms/programs, control planes, middleware
(considered separately from programs), and even companies/service providers. We
discuss the impact of Digital Gravity on the pathway to adoption and suggest
preliminary approaches to conceptualize and mitigate the friction caused by it.
| [
{
"version": "v1",
"created": "Fri, 5 Nov 2021 01:33:38 GMT"
}
] | 1,637,020,800,000 | [
[
"Remy",
"Sekou L",
""
],
[
"Walcott-Bryant",
"Aisha",
""
],
[
"Bore",
"Nelson K",
""
],
[
"Wachira",
"Charles M",
""
],
[
"Kuenhert",
"Julian",
""
]
] |
2111.07876 | Mugurel-Ionut Andreica | Mugurel-Ionut Andreica | Winning Solution of the AIcrowd SBB Flatland Challenge 2019-2020 | Presented at the Flatland Challenge workshop at AMLD 2020
(https://appliedmldays.org/events/amld-epfl-2020/challenges/flatland-challenge) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This report describes the main ideas of the solution which won the AIcrowd
SBB Flatland Challenge 2019-2020, with a score of 99% (meaning that, on
average, 99% of the agents were routed to their destinations within the
allotted time steps). The details of the task can be found on the competition's
website. The solution consists of 2 major components: 1) A component which
(re-)generates paths over a time-expanded graph for each agent 2) A component
which updates the agent paths after a malfunction occurs, in order to try to
preserve the same agent ordering of entering each cell as before the
malfunction. The goal of this component is twofold: a) to (try to) avoid
deadlocks b) to bring the system back to a consistent state (where each agent
has a feasible path over the time-expanded graph). I am discussing both of
these components, as well as a series of potentially promising, but unexplored
ideas, below.
| [
{
"version": "v1",
"created": "Thu, 11 Nov 2021 22:55:43 GMT"
}
] | 1,637,020,800,000 | [
[
"Andreica",
"Mugurel-Ionut",
""
]
] |
2111.08156 | Haofeng Liu | Haofeng Liu, Yiwen Chen, Jiayi Tan, Marcelo H Ang Jr | Improving Learning from Demonstrations by Learning from Experience | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | How to make imitation learning more general when demonstrations are
relatively limited has been a persistent problem in reinforcement learning
(RL). Poor demonstrations lead to narrow and biased date distribution,
non-Markovian human expert demonstration makes it difficult for the agent to
learn, and over-reliance on sub-optimal trajectories can make it hard for the
agent to improve its performance. To solve these problems we propose a new
algorithm named TD3fG that can smoothly transition from learning from experts
to learning from experience. Our algorithm achieves good performance in the
MUJOCO environment with limited and sub-optimal demonstrations. We use behavior
cloning to train the network as a reference action generator and utilize it in
terms of both loss function and exploration noise. This innovation can help
agents extract a priori knowledge from demonstrations while reducing the
detrimental effects of the poor Markovian properties of the demonstrations. It
has a better performance compared to the BC+ fine-tuning and DDPGfD approach,
especially when the demonstrations are relatively limited. We call our method
TD3fG meaning TD3 from a generator.
| [
{
"version": "v1",
"created": "Tue, 16 Nov 2021 00:40:31 GMT"
}
] | 1,637,107,200,000 | [
[
"Liu",
"Haofeng",
""
],
[
"Chen",
"Yiwen",
""
],
[
"Tan",
"Jiayi",
""
],
[
"Ang",
"Marcelo H",
"Jr"
]
] |
2111.08246 | Wushuang Wang | Shuyun Luo, Wushuang Wang, Mengyuan Fang, and Weiqiang Xu | Self-encoding Barnacle Mating Optimizer Algorithm for Manpower
Scheduling in Flow Shop | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Flow Shop Scheduling (FSS) has been widely researched due to its application
in many types of fields, while the human participant brings great challenges to
this problem. Manpower scheduling captures attention for assigning workers with
diverse proficiency to the appropriate stages, which is of great significance
to production efficiency.
In this paper, we present a novel algorithm called Self-encoding Barnacle
Mating Optimizer (SBMO), which solves the FSS problem considering worker
proficiency, defined as a new problem, Flow Shop Manpower Scheduling Problem
(FSMSP). The highlight of the SBMO algorithm is the combination with the
encoding method, crossover and mutation operators. Moreover, in order to solve
the local optimum problem, we design a neighborhood search scheme. Finally, the
extensive comparison simulations are conducted to demonstrate the superiority
of the proposed SBMO. The results indicate the effectiveness of SBMO in
approximate ratio, powerful stability, and execution time, compared with the
classic and popular counterparts.
| [
{
"version": "v1",
"created": "Tue, 16 Nov 2021 06:06:34 GMT"
}
] | 1,637,107,200,000 | [
[
"Luo",
"Shuyun",
""
],
[
"Wang",
"Wushuang",
""
],
[
"Fang",
"Mengyuan",
""
],
[
"Xu",
"Weiqiang",
""
]
] |
2111.08322 | Tongwen Huang | Tongwen Huang and Xihua Li | An Empirical Study of Finding Similar Exercises | 35th Conference on Neural Information Processing Systems (NeurIPS
2021) Workshop on Math AI for Education(MATHAI4ED) | 35th Conference on Neural Information Processing Systems (NeurIPS
2021) Workshop on Math AI for Education (MATHAI4ED) | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Education artificial intelligence aims to profit tasks in the education
domain such as intelligent test paper generation and consolidation exercises
where the main technique behind is how to match the exercises, known as the
finding similar exercises(FSE) problem. Most of these approaches emphasized
their model abilities to represent the exercise, unfortunately there are still
many challenges such as the scarcity of data, insufficient understanding of
exercises and high label noises. We release a Chinese education pre-trained
language model BERT$_{Edu}$ for the label-scarce dataset and introduce the
exercise normalization to overcome the diversity of mathematical formulas and
terms in exercise. We discover new auxiliary tasks in an innovative way depends
on problem-solving ideas and propose a very effective MoE enhanced multi-task
model for FSE task to attain better understanding of exercises. In addition,
confidence learning was utilized to prune train-set and overcome high noises in
labeling data. Experiments show that these methods proposed in this paper are
very effective.
| [
{
"version": "v1",
"created": "Tue, 16 Nov 2021 09:39:14 GMT"
}
] | 1,637,193,600,000 | [
[
"Huang",
"Tongwen",
""
],
[
"Li",
"Xihua",
""
]
] |
2111.08361 | Aviral Chharia | Aviral Chharia, Shivu Chauhan, Rahul Upadhyay, Vinay Kumar | From Convolutions towards Spikes: The Environmental Metric that the
Community currently Misses | NeurIPS 2021 Human-Centered AI Workshop | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Today, the AI community is obsessed with 'state-of-the-art' scores (80%
papers in NeurIPS) as the major performance metrics, due to which an important
parameter, i.e., the environmental metric, remains unreported. Computational
capabilities were a limiting factor a decade ago; however, in foreseeable
future circumstances, the challenge will be to develop environment-friendly and
power-efficient algorithms. The human brain, which has been optimizing itself
for almost a million years, consumes the same amount of power as a typical
laptop. Therefore, developing nature-inspired algorithms is one solution to it.
In this study, we show that currently used ANNs are not what we find in nature,
and why, although having lower performance, spiking neural networks, which
mirror the mammalian visual cortex, have attracted much interest. We further
highlight the hardware gaps restricting the researchers from using spike-based
computation for developing neuromorphic energy-efficient microchips on a large
scale. Using neuromorphic processors instead of traditional GPUs might be more
environment friendly and efficient. These processors will turn SNNs into an
ideal solution for the problem. This paper presents in-depth attention
highlighting the current gaps, the lack of comparative research, while
proposing new research directions at the intersection of two fields --
neuroscience and deep learning. Further, we define a new evaluation metric
'NATURE' for reporting the carbon footprint of AI models.
| [
{
"version": "v1",
"created": "Tue, 16 Nov 2021 11:04:42 GMT"
}
] | 1,637,107,200,000 | [
[
"Chharia",
"Aviral",
""
],
[
"Chauhan",
"Shivu",
""
],
[
"Upadhyay",
"Rahul",
""
],
[
"Kumar",
"Vinay",
""
]
] |
2111.08486 | N'dah Jean Kouagou | N'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga
Ngomo | Neural Class Expression Synthesis | 11 pages, 4 figures, 7 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Most existing approaches for class expression learning in description logics
are search algorithms. As the search space of these approaches is infinite,
they often fail to scale to large learning problems. Our main intuition is that
class expression learning can be regarded as a translation problem. Based
thereupon, we propose a new family of class expression learning approaches
which we dub neural class expression synthesis. Instances of this new family
circumvent the high search costs entailed by current algorithms by translating
training examples into class expressions in a fashion akin to machine
translation solutions. Consequently, they are not subject to the runtime
limitations of search-based approaches post training. We study three instances
of this novel family of approaches to synthesize class expressions from sets of
positive and negative examples. An evaluation of our approach on four benchmark
datasets suggests that it can effectively synthesize high-quality class
expressions with respect to the input examples in approximately one second on
average. Moreover, a comparison to other state-of-the-art approaches suggests
that we achieve better F-measures on large datasets. For reproducibility
purposes, we provide our implementation as well as pretrained models in our
public GitHub repository at https://github.com/fosterreproducibleresearch/NCES
| [
{
"version": "v1",
"created": "Tue, 16 Nov 2021 14:05:24 GMT"
},
{
"version": "v2",
"created": "Thu, 18 Nov 2021 09:31:47 GMT"
},
{
"version": "v3",
"created": "Mon, 19 Dec 2022 13:11:22 GMT"
}
] | 1,671,494,400,000 | [
[
"Kouagou",
"N'Dah Jean",
""
],
[
"Heindorf",
"Stefan",
""
],
[
"Demir",
"Caglar",
""
],
[
"Ngomo",
"Axel-Cyrille Ngonga",
""
]
] |
2111.08587 | Miguel Suau | Miguel Suau, Alexandros Agapitos, David Lynch, Derek Farrell, Mingqi
Zhou, Aleksandar Milenovic | Offline Contextual Bandits for Wireless Network Optimization | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The explosion in mobile data traffic together with the ever-increasing
expectations for higher quality of service call for the development of AI
algorithms for wireless network optimization. In this paper, we investigate how
to learn policies that can automatically adjust the configuration parameters of
every cell in the network in response to the changes in the user demand. Our
solution combines existent methods for offline learning and adapts them in a
principled way to overcome crucial challenges arising in this context.
Empirical results suggest that our proposed method will achieve important
performance gains when deployed in the real network while satisfying practical
constrains on computational efficiency.
| [
{
"version": "v1",
"created": "Thu, 11 Nov 2021 11:31:20 GMT"
}
] | 1,637,107,200,000 | [
[
"Suau",
"Miguel",
""
],
[
"Agapitos",
"Alexandros",
""
],
[
"Lynch",
"David",
""
],
[
"Farrell",
"Derek",
""
],
[
"Zhou",
"Mingqi",
""
],
[
"Milenovic",
"Aleksandar",
""
]
] |
2111.08625 | Yuansheng Zhu | Yuansheng Zhu, Weishi Shi, Deep Shankar Pandey, Yang Liu, Xiaofan Que,
Daniel E. Krutz, and Qi Yu | Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time
Series Data | Accepted to IEEE BigData 2021 as short paper; Revised in 11/20/20121 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel framework to classify large-scale time series data with
long duration. Long time seriesclassification (L-TSC) is a challenging problem
because the dataoften contains a large amount of irrelevant information to
theclassification target. The irrelevant period degrades the classifica-tion
performance while the relevance is unknown to the system.This paper proposes an
uncertainty-aware multiple instancelearning (MIL) framework to identify the
most relevant periodautomatically. The predictive uncertainty enables designing
anattention mechanism that forces the MIL model to learn from thepossibly
discriminant period. Moreover, the predicted uncertaintyyields a principled
estimator to identify whether a prediction istrustworthy or not. We further
incorporate another modality toaccommodate unreliable predictions by training a
separate modelbased on its availability and conduct uncertainty aware fusion
toproduce the final prediction. Systematic evaluation is conductedon the
Automatic Identification System (AIS) data, which is col-lected to identify and
track real-world vessels. Empirical resultsdemonstrate that the proposed method
can effectively detect thetypes of vessels based on the trajectory and the
uncertainty-awarefusion with other available data modality
(Synthetic-ApertureRadar or SAR imagery is used in our experiments) can
furtherimprove the detection accuracy.
| [
{
"version": "v1",
"created": "Tue, 16 Nov 2021 17:09:02 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Nov 2021 19:11:47 GMT"
},
{
"version": "v3",
"created": "Sun, 21 Nov 2021 02:30:21 GMT"
}
] | 1,637,625,600,000 | [
[
"Zhu",
"Yuansheng",
""
],
[
"Shi",
"Weishi",
""
],
[
"Pandey",
"Deep Shankar",
""
],
[
"Liu",
"Yang",
""
],
[
"Que",
"Xiaofan",
""
],
[
"Krutz",
"Daniel E.",
""
],
[
"Yu",
"Qi",
""
]
] |
2111.08817 | Hung Nguyen | Hung Nguyen, Minh Nguyen, Long Pham, Jennifer Adorno Nieves | Compressive Features in Offline Reinforcement Learning for Recommender
Systems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we develop a recommender system for a game that suggests
potential items to players based on their interactive behaviors to maximize
revenue for the game provider. Our approach is built on a reinforcement
learning-based technique and is trained on an offline data set that is publicly
available on an IEEE Big Data Cup challenge. The limitation of the offline data
set and the curse of high dimensionality pose significant obstacles to solving
this problem. Our proposed method focuses on improving the total rewards and
performance by tackling these main difficulties. More specifically, we utilized
sparse PCA to extract important features of user behaviors. Our
Q-learning-based system is then trained from the processed offline data set. To
exploit all possible information from the provided data set, we cluster user
features to different groups and build an independent Q-table for each group.
Furthermore, to tackle the challenge of unknown formula for evaluation metrics,
we design a metric to self-evaluate our system's performance based on the
potential value the game provider might achieve and a small collection of
actual evaluation metrics that we obtain from the live scoring environment. Our
experiments show that our proposed metric is consistent with the results
published by the challenge organizers. We have implemented the proposed
training pipeline, and the results show that our method outperforms current
state-of-the-art methods in terms of both total rewards and training speed. By
addressing the main challenges and leveraging the state-of-the-art techniques,
we have achieved the best public leaderboard result in the challenge.
Furthermore, our proposed method achieved an estimated score of approximately
20% better and can be trained faster by 30 times than the best of the current
state-of-the-art methods.
| [
{
"version": "v1",
"created": "Tue, 16 Nov 2021 22:43:16 GMT"
}
] | 1,637,193,600,000 | [
[
"Nguyen",
"Hung",
""
],
[
"Nguyen",
"Minh",
""
],
[
"Pham",
"Long",
""
],
[
"Nieves",
"Jennifer Adorno",
""
]
] |
2111.08951 | Hengyao Bao | Hengyao Bao, Xihua Li, Xuemin Zhao, Yunbo Cao | Exploring Student Representation For Neural Cognitive Diagnosis | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Cognitive diagnosis, the goal of which is to obtain the proficiency level of
students on specific knowledge concepts, is an fundamental task in smart
educational systems. Previous works usually represent each student as a
trainable knowledge proficiency vector, which cannot capture the relations of
concepts and the basic profile(e.g. memory or comprehension) of students. In
this paper, we propose a method of student representation with the exploration
of the hierarchical relations of knowledge concepts and student embedding.
Specifically, since the proficiency on parent knowledge concepts reflects the
correlation between knowledge concepts, we get the first knowledge proficiency
with a parent-child concepts projection layer. In addition, a low-dimension
dense vector is adopted as the embedding of each student, and obtain the second
knowledge proficiency with a full connection layer. Then, we combine the two
proficiency vector above to get the final representation of students.
Experiments show the effectiveness of proposed representation method.
| [
{
"version": "v1",
"created": "Wed, 17 Nov 2021 07:47:44 GMT"
}
] | 1,637,193,600,000 | [
[
"Bao",
"Hengyao",
""
],
[
"Li",
"Xihua",
""
],
[
"Zhao",
"Xuemin",
""
],
[
"Cao",
"Yunbo",
""
]
] |
2111.09078 | Hu Yulan | Yulan Hu, Yong Liu | Green CWS: Extreme Distillation and Efficient Decode Method Towards
Industrial Application | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Benefiting from the strong ability of the pre-trained model, the research on
Chinese Word Segmentation (CWS) has made great progress in recent years.
However, due to massive computation, large and complex models are incapable of
empowering their ability for industrial use. On the other hand, for
low-resource scenarios, the prevalent decode method, such as Conditional Random
Field (CRF), fails to exploit the full information of the training data. This
work proposes a fast and accurate CWS framework that incorporates a
light-weighted model and an upgraded decode method (PCRF) towards industrially
low-resource CWS scenarios. First, we distill a Transformer-based student model
as an encoder, which not only accelerates the inference speed but also combines
open knowledge and domain-specific knowledge. Second, the perplexity score to
evaluate the language model is fused into the CRF module to better identify the
word boundaries. Experiments show that our work obtains relatively high
performance on multiple datasets with as low as 14\% of time consumption
compared with the original BERT-based model. Moreover, under the low-resource
setting, we get superior results in comparison with the traditional decoding
methods.
| [
{
"version": "v1",
"created": "Wed, 17 Nov 2021 12:45:02 GMT"
}
] | 1,637,193,600,000 | [
[
"Hu",
"Yulan",
""
],
[
"Liu",
"Yong",
""
]
] |
2111.09084 | Xu Zheng | Ramon Vinas, Xu Zheng and Jer Hayes | A Graph-based Imputation Method for Sparse Medical Records | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Electronic Medical Records (EHR) are extremely sparse. Only a small
proportion of events (symptoms, diagnoses, and treatments) are observed in the
lifetime of an individual. The high degree of missingness of EHR can be
attributed to a large number of factors, including device failure, privacy
concerns, or other unexpected reasons. Unfortunately, many traditional
imputation methods are not well suited for highly sparse data and scale poorly
to high dimensional datasets. In this paper, we propose a graph-based
imputation method that is both robust to sparsity and to unreliable unmeasured
events. Our approach compares favourably to several standard and
state-of-the-art imputation methods in terms of performance and runtime.
Moreover, results indicate that the model learns to embed different event types
in a clinically meaningful way. Our work can facilitate the diagnosis of novel
diseases based on the clinical history of past events, with the potential to
increase our understanding of the landscape of comorbidities.
| [
{
"version": "v1",
"created": "Wed, 17 Nov 2021 13:06:08 GMT"
}
] | 1,637,193,600,000 | [
[
"Vinas",
"Ramon",
""
],
[
"Zheng",
"Xu",
""
],
[
"Hayes",
"Jer",
""
]
] |
2111.09093 | Steve Alpern | Steve Alpern | The Faulty GPS Problem: Shortest Time Paths in Networks with Unreliable
Directions | 16 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper optimizes motion planning when there is a known risk that the road
choice suggested by a Satnav (GPS) is not on a shortest path. At every branch
node of a network Q, a Satnav (GPS) points to the arc leading to the
destination, or home node, H - but only with a high known probability p. Always
trusting the Satnav's suggestion may lead to an infinite cycle. If one wishes
to reach H in least expected time, with what probability q=q(Q,p) should one
trust the pointer (if not, one chooses randomly among the other arcs)? We call
this the Faulty Satnav (GPS) Problem. We also consider versions where the trust
probability q can depend on the degree of the current node and a `treasure
hunt' where two searchers try to reach H first. The agent searching for H need
not be a car, that is just a familiar example -- it could equally be a UAV
receiving unreliable GPS information. This problem has its origin not in driver
frustration but in the work of Fonio et al (2017) on ant navigation, where the
pointers correspond to pheromone markers pointing to the nest. Neither the
driver or ant will know the exact process by which a choice (arc) is suggested,
which puts the problem into the domain of how much to trust an option suggested
by AI.
| [
{
"version": "v1",
"created": "Wed, 17 Nov 2021 13:20:08 GMT"
}
] | 1,637,193,600,000 | [
[
"Alpern",
"Steve",
""
]
] |
2111.09475 | Xuejing Zheng | Xuejing Zheng, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo | Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward
Machines | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Continuously learning new tasks using high-level ideas or knowledge is a key
capability of humans. In this paper, we propose Lifelong reinforcement learning
with Sequential linear temporal logic formulas and Reward Machines (LSRM),
which enables an agent to leverage previously learned knowledge to fasten
learning of logically specified tasks. For the sake of more flexible
specification of tasks, we first introduce Sequential Linear Temporal Logic
(SLTL), which is a supplement to the existing Linear Temporal Logic (LTL)
formal language. We then utilize Reward Machines (RM) to exploit structural
reward functions for tasks encoded with high-level events, and propose
automatic extension of RM and efficient knowledge transfer over tasks for
continuous learning in lifetime. Experimental results show that LSRM
outperforms the methods that learn the target tasks from scratch by taking
advantage of the task decomposition using SLTL and knowledge transfer over RM
during the lifelong learning process.
| [
{
"version": "v1",
"created": "Thu, 18 Nov 2021 02:02:08 GMT"
}
] | 1,637,280,000,000 | [
[
"Zheng",
"Xuejing",
""
],
[
"Yu",
"Chao",
""
],
[
"Chen",
"Chen",
""
],
[
"Hao",
"Jianye",
""
],
[
"Zhuo",
"Hankz Hankui",
""
]
] |
2111.10061 | Alan Both | Alan Both, Dhirendra Singh, Afshin Jafari, Billie Giles-Corti, Lucy
Gunn | An Activity-Based Model of Transport Demand for Greater Melbourne | 35 pages, 10 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | In this paper, we present an algorithm for creating a synthetic population
for the Greater Melbourne area using a combination of machine learning,
probabilistic, and gravity-based approaches. We combine these techniques in a
hybrid model with three primary innovations: 1. when assigning activity
patterns, we generate individual activity chains for every agent, tailored to
their cohort; 2. when selecting destinations, we aim to strike a balance
between the distance-decay of trip lengths and the activity-based attraction of
destination locations; and 3. we take into account the number of trips
remaining for an agent so as to ensure they do not select a destination that
would be unreasonable to return home from. Our method is completely open and
replicable, requiring only publicly available data to generate a synthetic
population of agents compatible with commonly used agent-based modeling
software such as MATSim. The synthetic population was found to be accurate in
terms of distance distribution, mode choice, and destination choice for a
variety of population sizes.
| [
{
"version": "v1",
"created": "Fri, 19 Nov 2021 06:20:33 GMT"
}
] | 1,637,539,200,000 | [
[
"Both",
"Alan",
""
],
[
"Singh",
"Dhirendra",
""
],
[
"Jafari",
"Afshin",
""
],
[
"Giles-Corti",
"Billie",
""
],
[
"Gunn",
"Lucy",
""
]
] |
2111.10518 | Shahin Atakishiyev | Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel | Towards Safe, Explainable, and Regulated Autonomous Driving | Accepted for publication in the Explainable AI for Intelligent
Transportation Systems book | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | There has been recent and growing interest in the development and deployment
of autonomous vehicles, encouraged by the empirical successes of powerful
artificial intelligence techniques (AI), especially in the applications of deep
learning and reinforcement learning. However, as demonstrated by recent traffic
accidents, autonomous driving technology is not fully reliable for safe
deployment. As AI is the main technology behind the intelligent navigation
systems of self-driving vehicles, both the stakeholders and transportation
regulators require their AI-driven software architecture to be safe,
explainable, and regulatory compliant. In this paper, we propose a design
framework that integrates autonomous control, explainable AI (XAI), and
regulatory compliance to address this issue, and then provide an initial
validation of the framework with a critical analysis in a case study. Moreover,
we describe relevant XAI approaches that can help achieve the goals of the
framework.
| [
{
"version": "v1",
"created": "Sat, 20 Nov 2021 05:06:22 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jan 2022 22:34:03 GMT"
},
{
"version": "v3",
"created": "Thu, 14 Apr 2022 23:17:07 GMT"
},
{
"version": "v4",
"created": "Fri, 26 May 2023 05:28:30 GMT"
}
] | 1,685,318,400,000 | [
[
"Atakishiyev",
"Shahin",
""
],
[
"Salameh",
"Mohammad",
""
],
[
"Yao",
"Hengshuai",
""
],
[
"Goebel",
"Randy",
""
]
] |
2111.10595 | Zhicheng He | Zhicheng He | Quality and Computation Time in Optimization Problems | 6 pages, 3 figures, 1 table | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Optimization problems are crucial in artificial intelligence. Optimization
algorithms are generally used to adjust the performance of artificial
intelligence models to minimize the error of mapping inputs to outputs. Current
evaluation methods on optimization algorithms generally consider the
performance in terms of quality. However, not all optimization algorithms for
all test cases are evaluated equal from quality, the computation time should be
also considered for optimization tasks. In this paper, we investigate the
quality and computation time of optimization algorithms in optimization
problems, instead of the one-for-all evaluation of quality. We select the
well-known optimization algorithms (Bayesian optimization and evolutionary
algorithms) and evaluate them on the benchmark test functions in terms of
quality and computation time. The results show that BO is suitable to be
applied in the optimization tasks that are needed to obtain desired quality in
the limited function evaluations, and the EAs are suitable to search the
optimal of the tasks that are allowed to find the optimal solution with enough
function evaluations. This paper provides the recommendation to select suitable
optimization algorithms for optimization problems with different numbers of
function evaluations, which contributes to the efficiency that obtains the
desired quality with less computation time for optimization problems.
| [
{
"version": "v1",
"created": "Sat, 20 Nov 2021 14:09:47 GMT"
}
] | 1,637,625,600,000 | [
[
"He",
"Zhicheng",
""
]
] |
2111.10896 | Adrian Haret | Adrian Haret | Surprise Minimization Revision Operators | Presented at NMR 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Prominent approaches to belief revision prescribe the adoption of a new
belief that is as close as possible to the prior belief, in a process that,
even in the standard case, can be described as attempting to minimize surprise.
Here we extend the existing model by proposing a measure of surprise, dubbed
relative surprise, in which surprise is computed with respect not just to the
prior belief, but also to the broader context provided by the new information,
using a measure derived from familiar distance notions between truth-value
assignments. We characterize the surprise minimization revision operator thus
defined using a set of intuitive rationality postulates in the AGM mould, along
the way obtaining representation results for other existing revision operators
in the literature, such as the Dalal operator and a recently introduced
distance-based min-max operator.
| [
{
"version": "v1",
"created": "Sun, 21 Nov 2021 20:38:50 GMT"
}
] | 1,637,625,600,000 | [
[
"Haret",
"Adrian",
""
]
] |
2111.11107 | Th\'eophile Champion | Th\'eophile Champion, Lancelot Da Costa, Howard Bowman, Marek Grze\'s | Branching Time Active Inference: the theory and its generality | Accepted for publication in Neural Networks, 35 pages, 10 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Over the last 10 to 15 years, active inference has helped to explain various
brain mechanisms from habit formation to dopaminergic discharge and even
modelling curiosity. However, the current implementations suffer from an
exponential (space and time) complexity class when computing the prior over all
the possible policies up to the time-horizon. Fountas et al (2020) used Monte
Carlo tree search to address this problem, leading to impressive results in two
different tasks. In this paper, we present an alternative framework that aims
to unify tree search and active inference by casting planning as a structure
learning problem. Two tree search algorithms are then presented. The first
propagates the expected free energy forward in time (i.e., towards the leaves),
while the second propagates it backward (i.e., towards the root). Then, we
demonstrate that forward and backward propagations are related to active
inference and sophisticated inference, respectively, thereby clarifying the
differences between those two planning strategies.
| [
{
"version": "v1",
"created": "Mon, 22 Nov 2021 10:56:03 GMT"
},
{
"version": "v2",
"created": "Mon, 11 Apr 2022 20:03:48 GMT"
}
] | 1,649,808,000,000 | [
[
"Champion",
"Théophile",
""
],
[
"Da Costa",
"Lancelot",
""
],
[
"Bowman",
"Howard",
""
],
[
"Grześ",
"Marek",
""
]
] |
2111.11276 | Th\'eophile Champion | Th\'eophile Champion, Howard Bowman, Marek Grze\'s | Branching Time Active Inference: empirical study and complexity class
analysis | 39 pages, 11 figures, accepted for publication in Neural Networks | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Active inference is a state-of-the-art framework for modelling the brain that
explains a wide range of mechanisms such as habit formation, dopaminergic
discharge and curiosity. However, recent implementations suffer from an
exponential complexity class when computing the prior over all the possible
policies up to the time horizon. Fountas et al (2020) used Monte Carlo tree
search to address this problem, leading to very good results in two different
tasks. Additionally, Champion et al (2021a) proposed a tree search approach
based on (temporal) structure learning. This was enabled by the development of
a variational message passing approach to active inference, which enables
compositional construction of Bayesian networks for active inference. However,
this message passing tree search approach, which we call branching-time active
inference (BTAI), has never been tested empirically. In this paper, we present
an experimental study of BTAI in the context of a maze solving agent. In this
context, we show that both improved prior preferences and deeper search help
mitigate the vulnerability to local minima. Then, we compare BTAI to standard
active inference (AcI) on a graph navigation task. We show that for small
graphs, both BTAI and AcI successfully solve the task. For larger graphs, AcI
exhibits an exponential (space) complexity class, making the approach
intractable. However, BTAI explores the space of policies more efficiently,
successfully scaling to larger graphs. Then, BTAI was compared to the POMCP
algorithm on the frozen lake environment. The experiments suggest that BTAI and
the POMCP algorithm accumulate a similar amount of reward. Also, we describe
when BTAI receives more rewards than the POMCP agent, and when the opposite is
true. Finally, we compared BTAI to the approach of Fountas et al (2020) on the
dSprites dataset, and we discussed the pros and cons of each approach.
| [
{
"version": "v1",
"created": "Mon, 22 Nov 2021 15:30:35 GMT"
},
{
"version": "v2",
"created": "Tue, 24 May 2022 14:31:35 GMT"
}
] | 1,653,436,800,000 | [
[
"Champion",
"Théophile",
""
],
[
"Bowman",
"Howard",
""
],
[
"Grześ",
"Marek",
""
]
] |
2111.11329 | Dennis Soemers | Cameron Browne, \'Eric Piette, Matthew Stephenson, Dennis J.N.J.
Soemers | General Board Geometry | Accepted at Advances in Computer Games (ACG) 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Game boards are described in the Ludii general game system by their
underlying graphs, based on tiling, shape and graph operators, with the
automatic detection of important properties such as topological relationships
between graph elements, directions and radial step sequences. This approach
allows most conceivable game boards to be described simply and succinctly.
| [
{
"version": "v1",
"created": "Mon, 22 Nov 2021 16:39:07 GMT"
}
] | 1,637,625,600,000 | [
[
"Browne",
"Cameron",
""
],
[
"Piette",
"Éric",
""
],
[
"Stephenson",
"Matthew",
""
],
[
"Soemers",
"Dennis J. N. J.",
""
]
] |
2111.11779 | Rafael Pe\~naloza | Gabriella Pasi and Rafael Pe\~naloza | Answering Fuzzy Queries over Fuzzy DL-Lite Ontologies | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A prominent problem in knowledge representation is how to answer queries
taking into account also the implicit consequences of an ontology representing
domain knowledge. While this problem has been widely studied within the realm
of description logic ontologies, it has been surprisingly neglected within the
context of vague or imprecise knowledge, particularly from the point of view of
mathematical fuzzy logic. In this paper we study the problem of answering
conjunctive queries and threshold queries w.r.t. ontologies in fuzzy DL-Lite.
Specifically, we show through a rewriting approach that threshold query
answering w.r.t. consistent ontologies remains in $AC_0$ in data complexity,
but that conjunctive query answering is highly dependent on the selected
triangular norm, which has an impact on the underlying semantics. For the
idempodent G\"odel t-norm, we provide an effective method based on a reduction
to the classical case. This paper is under consideration in Theory and Practice
of Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Tue, 23 Nov 2021 10:45:54 GMT"
}
] | 1,637,712,000,000 | [
[
"Pasi",
"Gabriella",
""
],
[
"Peñaloza",
"Rafael",
""
]
] |
2111.11871 | Mohamed-Bachir Belaid | Mohamed-Bachir Belaid, Arnaud Gotlieb, Nadjib Lazaar | Solve Optimization Problems with Unknown Constraint Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In most optimization problems, users have a clear understanding of the
function to optimize (e.g., minimize the makespan for scheduling problems).
However, the constraints may be difficult to state and their modelling often
requires expertise in Constraint Programming. Active constraint acquisition has
been successfully used to support non-experienced users in learning constraint
networks through the generation of a sequence of queries. In this paper, we
propose Learn&Optimize, a method to solve optimization problems with known
objective function and unknown constraint network. It uses an active constraint
acquisition algorithm which learns the unknown constraints and computes
boundaries for the optimal solution during the learning process. As a result,
our method allows users to solve optimization problems without learning the
overall constraint network.
| [
{
"version": "v1",
"created": "Tue, 23 Nov 2021 13:39:41 GMT"
}
] | 1,637,712,000,000 | [
[
"Belaid",
"Mohamed-Bachir",
""
],
[
"Gotlieb",
"Arnaud",
""
],
[
"Lazaar",
"Nadjib",
""
]
] |
2111.11965 | Dmitry Maximov | Dmitry Maximov and Sekou A. K. Diane | Object Recognition by a Minimally Pre-Trained System in the Process of
Environment Exploration | arXiv admin note: text overlap with arXiv:1812.11969 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We update the method of describing and assessing the process of the study of
an abstract environment by a system, proposed earlier. We do not model any
biological cognition mechanisms and consider the system as an agent equipped
with an information processor (or a group of such agents), which makes a move
in the environment, consumes information supplied by the environment, and gives
out the next move (hence, the process is considered as a game). The system
moves in an unknown environment and should recognize new objects located in it.
In this case, the system should build comprehensive images of visible things
and memorize them if necessary (and it should also choose the current goal
set). The main problems here are object recognition, and the informational
reward rating in the game. Thus, the main novelty of the paper is a new method
of evaluating the amount of visual information about the object as the reward.
In such a system, we suggest using a minimally pre-trained neural network to be
responsible for the recognition: at first, we train the network only for
Biederman geons (geometrical primitives). The geons are generated
programmatically and we demonstrate that such a trained network recognizes
geons in real objects quite well. We also offer to generate, procedurally, new
objects from geon schemes (geon combinations in images) obtained from the
environment and to store them in a database. In this case, we do not obtain new
information about an object (i.e., our reward is maximal, thus the game and the
object cognition process stop) when we stop getting new schemes of this kind.
These schemes are generated from geons connected with the object. In the case
of a possibly known item, the informational reward is maximal when we have no
more detection uncertainty for any of the objects.
| [
{
"version": "v1",
"created": "Tue, 23 Nov 2021 15:59:22 GMT"
}
] | 1,637,712,000,000 | [
[
"Maximov",
"Dmitry",
""
],
[
"Diane",
"Sekou A. K.",
""
]
] |
2111.12144 | Mariusz Marek | Andrzej Kozik, Tomasz Machalewski, Mariusz Marek, Adrian Ochmann | Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS
Games | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The discovery of Behavior Trees (BTs) impacted the field of Artificial
Intelligence (AI) in games, by providing flexible and natural representation of
non-player characters (NPCs) logic, manageable by game-designers. Nevertheless,
increased pressure on ever better NPCs AI-agents forced complexity of
handcrafted BTs to became barely-tractable and error-prone. On the other hand,
while many just-launched on-line games suffer from player-shortage, the
existence of AI with a broad-range of capabilities could increase players
retention. Therefore, to handle above challenges, recent trends in the field
focused on automatic creation of AI-agents: from deep- and
reinforcementlearning techniques to combinatorial (constrained) optimization
and evolution of BTs. In this paper, we present a novel approach to
semi-automatic construction of AI-agents, that mimic and generalize given human
gameplays by adapting and tuning of expert-created BT under a developed
similarity metric between source and BT gameplays. To this end, we formulated
mixed discrete-continuous optimization problem, in which topological and
functional changes of the BT are reflected in numerical variables, and
constructed a dedicated hybrid-metaheuristic. The performance of presented
approach was verified experimentally in a prototype real-time strategy game.
Carried out experiments confirmed efficiency and perspectives of presented
approach, which is going to be applied in a commercial game.
| [
{
"version": "v1",
"created": "Tue, 23 Nov 2021 20:36:28 GMT"
}
] | 1,637,798,400,000 | [
[
"Kozik",
"Andrzej",
""
],
[
"Machalewski",
"Tomasz",
""
],
[
"Marek",
"Mariusz",
""
],
[
"Ochmann",
"Adrian",
""
]
] |
2111.12454 | Fabrizio Maria Maggi | Giacomo Bergami, Chiara Di Francescomarino, Chiara Ghidini, Fabrizio
Maria Maggi, Joonas Puura | Exploring Business Process Deviance with Sequential and Declarative
Patterns | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Business process deviance refers to the phenomenon whereby a subset of the
executions of a business process deviate, in a negative or positive way, with
respect to {their} expected or desirable outcomes. Deviant executions of a
business process include those that violate compliance rules, or executions
that undershoot or exceed performance targets. Deviance mining is concerned
with uncovering the reasons for deviant executions by analyzing event logs
stored by the systems supporting the execution of a business process. In this
paper, the problem of explaining deviations in business processes is first
investigated by using features based on sequential and declarative patterns,
and a combination of them. Then, the explanations are further improved by
leveraging the data attributes of events and traces in event logs through
features based on pure data attribute values and data-aware declarative rules.
The explanations characterizing the deviances are then extracted by direct and
indirect methods for rule induction. Using real-life logs from multiple
domains, a range of feature types and different forms of decision rules are
evaluated in terms of their ability to accurately discriminate between
non-deviant and deviant executions of a process as well as in terms of
understandability of the final outcome returned to the users.
| [
{
"version": "v1",
"created": "Wed, 24 Nov 2021 12:16:07 GMT"
}
] | 1,637,798,400,000 | [
[
"Bergami",
"Giacomo",
""
],
[
"Di Francescomarino",
"Chiara",
""
],
[
"Ghidini",
"Chiara",
""
],
[
"Maggi",
"Fabrizio Maria",
""
],
[
"Puura",
"Joonas",
""
]
] |
2111.12677 | Xinxing Wu | Xinxing Wu, Tao Wang, Qian Liu, Peide Liu, Guanrong Chen, Xu Zhang | Topological and Algebraic Structures of Atanassov's Intuitionistic
Fuzzy-Values Space | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We prove that the space of intuitionistic fuzzy values (IFVs) with a linear
order based on a score function and an accuracy function has the same algebraic
structure as the one induced by a linear order based on a similarity function
and an accuracy function. By introducing a new operator for IFVs via the linear
order based on a score function and an accuracy function, we show that such an
operator is a strong negation on IFVs. Moreover, we observe that the space of
IFVs is a complete lattice and a Kleene algebra with the new operator. We also
demonstrate that the topological space of IFVs with the order topology induced
by the above two linear orders is not separable and metrizable but compact and
connected. From some new perspectives,our results partially answer three open
problems posed by Atanassov [Intuitionistic Fuzzy Sets: Theory and
Applications, Springer, 1999] and [On Intuitionistic Fuzzy Sets Theory,
Springer, 2012]. Furthermore, we construct an isomorphism between the spaces of
IFVs and q-rung orthopedic fuzzy values (q-ROFVs) under the corresponding
linear orders. To this end, we introduce the concept of admissible similarity
measures with particular orders for IFSs, extending the existing definition of
the similarity measure for IFSs, and construct an admissible similarity measure
with a linear order based on a score function and an accuracy function, which
is effectively applied to a pattern recognition problem about the
classification of building materials.
| [
{
"version": "v1",
"created": "Wed, 17 Nov 2021 06:43:02 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jun 2022 07:35:42 GMT"
}
] | 1,654,128,000,000 | [
[
"Wu",
"Xinxing",
""
],
[
"Wang",
"Tao",
""
],
[
"Liu",
"Qian",
""
],
[
"Liu",
"Peide",
""
],
[
"Chen",
"Guanrong",
""
],
[
"Zhang",
"Xu",
""
]
] |
2111.13136 | Andrey Rivkin | Anti Alman, Fabrizio Maria Maggi, Marco Montali, Fabio Patrizi, and
Andrey Rivkin | Monitoring Hybrid Process Specifications with Conflict Management: The
Automata-theoretic Approach | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Business process monitoring approaches have thus far mainly focused on
monitoring the execution of a process with respect to a single process model.
However, in some cases it is necessary to consider multiple process
specifications simultaneously. In addition, these specifications can be
procedural, declarative, or a combination of both. For example, in the medical
domain, a clinical guideline describing the treatment of a specific disease
cannot account for all possible co-factors that can coexist for a specific
patient and therefore additional constraints may need to be considered. In some
cases, these constraints may be incompatible with clinical guidelines,
therefore requiring the violation of either the guidelines or the constraints.
In this paper, we propose a solution for monitoring the interplay of hybrid
process specifications expressed as a combination of (data-aware) Petri nets
and temporal logic rules. During the process execution, if these specifications
are in conflict with each other, it is possible to violate some of them. The
monitoring system is equipped with a violation cost model according to which
the system can recommend the next course of actions in a way that would either
avoid possible violations or minimize the total cost of violations.
| [
{
"version": "v1",
"created": "Thu, 25 Nov 2021 15:49:33 GMT"
}
] | 1,638,144,000,000 | [
[
"Alman",
"Anti",
""
],
[
"Maggi",
"Fabrizio Maria",
""
],
[
"Montali",
"Marco",
""
],
[
"Patrizi",
"Fabio",
""
],
[
"Rivkin",
"Andrey",
""
]
] |
2111.13271 | Fenareti Lampathaki | Evmorfia Biliri, Minas Pertselakis, Marios Phinikettos, Marios
Zacharias, Fenareti Lampathaki, Dimitrios Alexandrou | Designing a Trusted Data Brokerage Framework in the Aviation Domain | 9 pages, 2 figures | null | 10.1007/978-3-030-28464-0_21 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, there is growing interest in the ways the European aviation
industry can leverage the multi-source data fusion towards augmented domain
intelligence. However, privacy, legal and organisational policies together with
technical limitations, hinder data sharing and, thus, its benefits. The current
paper presents the ICARUS data policy and assets brokerage framework, which
aims to (a) formalise the data attributes and qualities that affect how
aviation data assets can be shared and handled subsequently to their
acquisition, including licenses, IPR, characterisation of sensitivity and
privacy risks, and (b) enable the creation of machine-processable data
contracts for the aviation industry. This involves expressing contractual terms
pertaining to data trading agreements into a machine-processable language and
supporting the diverse interactions among stakeholders in aviation data sharing
scenarios through a trusted and robust system based on the Ethereum platform.
| [
{
"version": "v1",
"created": "Thu, 25 Nov 2021 23:22:17 GMT"
}
] | 1,638,144,000,000 | [
[
"Biliri",
"Evmorfia",
""
],
[
"Pertselakis",
"Minas",
""
],
[
"Phinikettos",
"Marios",
""
],
[
"Zacharias",
"Marios",
""
],
[
"Lampathaki",
"Fenareti",
""
],
[
"Alexandrou",
"Dimitrios",
""
]
] |
2111.15108 | Juelin Huang | Benting Wan, Juelin Huang and Xi Chen | Interval-valued q-Rung Orthopair Fuzzy Choquet Integral Operators and
Its Application in Group Decision Making | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | It is more flexible for decision makers to evaluate by interval-valued q-rung
orthopair fuzzy set (IVq-ROFS),which offers fuzzy decision-making more
applicational space. Meanwhile, Choquet integralses non-additive set function
(fuzzy measure) to describe the interaction between attributes directly.In
particular, there are a large number of practical issues that have relevance
between attributes.Therefore,this paper proposes the correlation operator and
group decision-making method based on the interval-valued q-rung orthopair
fuzzy set Choquet integral.First,interval-valued q-rung orthopair fuzzy Choquet
integral average operator (IVq-ROFCA) and interval-valued q-rung orthopair
fuzzy Choquet integral geometric operator (IVq-ROFCG) are inves-tigated,and
their basic properties are proved.Furthermore, several operators based on
IVq-ROFCA and IVq-ROFCG are developed. Then, a group decision-making method
based on IVq-ROFCA is developed,which can solve the decision making problems
with interaction between attributes.Finally,through the implementation of the
warning management system for hypertension,it is shown that the operator and
group decision-making method proposed in this paper can handle complex
decision-making cases in reality, and the decision result is consistent with
the doctor's diagnosis result.Moreover,the comparison with the results of other
operators shows that the proposed operators and group decision-making method
are correct and effective,and the decision result will not be affected by the
change of q value.
| [
{
"version": "v1",
"created": "Tue, 30 Nov 2021 03:55:38 GMT"
}
] | 1,638,316,800,000 | [
[
"Wan",
"Benting",
""
],
[
"Huang",
"Juelin",
""
],
[
"Chen",
"Xi",
""
]
] |
2112.00797 | Abimbola Afolayan | Abimbola Helen Afolayan, Bolanle Adefowoke Ojokoh, and Adebayo
Adetunmbi | A Feedback Integrated Web-Based Multi-Criteria Group Decision Support
Model for Contractor Selection using Fuzzy Analytic Hierarchy Process | 20 pages, 2 figures | In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and
Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing,
Springer, Cham. 1251, 511-528. Springer, Cham | 10.1007/978-3-030-55187-2_38 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, a feedback integrated multi-criteria group decision support
model for contractor selection was proposed.
| [
{
"version": "v1",
"created": "Fri, 19 Nov 2021 17:57:32 GMT"
}
] | 1,638,489,600,000 | [
[
"Afolayan",
"Abimbola Helen",
""
],
[
"Ojokoh",
"Bolanle Adefowoke",
""
],
[
"Adetunmbi",
"Adebayo",
""
]
] |
2112.00848 | S\'ebastien Ferr\'e | S\'ebastien Ferr\'e (Univ Rennes, CNRS, IRISA) | First Steps of an Approach to the ARC Challenge based on Descriptive
Grid Models and the Minimum Description Length Principle | 26 pages, 6 figures, technical report of work in progress | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Abstraction and Reasoning Corpus (ARC) was recently introduced by
Fran\c{c}ois Chollet as a tool to measure broad intelligence in both humans and
machines. It is very challenging, and the best approach in a Kaggle competition
could only solve 20% of the tasks, relying on brute-force search for chains of
hand-crafted transformations. In this paper, we present the first steps
exploring an approach based on descriptive grid models and the Minimum
Description Length (MDL) principle. The grid models describe the contents of a
grid, and support both parsing grids and generating grids. The MDL principle is
used to guide the search for good models, i.e. models that compress the grids
the most. We report on our progress over a year, improving on the general
approach and the models. Out of the 400 training tasks, our performance
increased from 5 to 29 solved tasks, only using 30s computation time per task.
Our approach not only predicts the output grids, but also outputs an
intelligible model and explanations for how the model was incrementally built.
| [
{
"version": "v1",
"created": "Wed, 1 Dec 2021 21:58:47 GMT"
}
] | 1,638,489,600,000 | [
[
"Ferré",
"Sébastien",
"",
"Univ Rennes, CNRS, IRISA"
]
] |
2112.01451 | Alexander Neuwirth | Alexander Neuwirth and Derek Riley | Architecting and Visualizing Deep Reinforcement Learning Models | Presented at MICS 2020 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | To meet the growing interest in Deep Reinforcement Learning (DRL), we sought
to construct a DRL-driven Atari Pong agent and accompanying visualization tool.
Existing approaches do not support the flexibility required to create an
interactive exhibit with easily-configurable physics and a human-controlled
player. Therefore, we constructed a new Pong game environment, discovered and
addressed a number of unique data deficiencies that arise when applying DRL to
a new environment, architected and tuned a policy gradient based DRL model,
developed a real-time network visualization, and combined these elements into
an interactive display to help build intuition and awareness of the mechanics
of DRL inference.
| [
{
"version": "v1",
"created": "Thu, 2 Dec 2021 17:48:26 GMT"
}
] | 1,638,489,600,000 | [
[
"Neuwirth",
"Alexander",
""
],
[
"Riley",
"Derek",
""
]
] |
2112.01671 | Zekun Li | Zekun Li, Yao-Yi Chiang, Sasan Tavakkol, Basel Shbita, Johannes H.
Uhl, Stefan Leyk, and Craig A. Knoblock | An Automatic Approach for Generating Rich, Linked Geo-Metadata from
Historical Map Images | 10.1145/3394486.3403381 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Historical maps contain detailed geographic information difficult to find
elsewhere covering long-periods of time (e.g., 125 years for the historical
topographic maps in the US). However, these maps typically exist as scanned
images without searchable metadata. Existing approaches making historical maps
searchable rely on tedious manual work (including crowd-sourcing) to generate
the metadata (e.g., geolocations and keywords). Optical character recognition
(OCR) software could alleviate the required manual work, but the recognition
results are individual words instead of location phrases (e.g., "Black" and
"Mountain" vs. "Black Mountain"). This paper presents an end-to-end approach to
address the real-world problem of finding and indexing historical map images.
This approach automatically processes historical map images to extract their
text content and generates a set of metadata that is linked to large external
geospatial knowledge bases. The linked metadata in the RDF (Resource
Description Framework) format support complex queries for finding and indexing
historical maps, such as retrieving all historical maps covering mountain peaks
higher than 1,000 meters in California. We have implemented the approach in a
system called mapKurator. We have evaluated mapKurator using historical maps
from several sources with various map styles, scales, and coverage. Our results
show significant improvement over the state-of-the-art methods. The code has
been made publicly available as modules of the Kartta Labs project at
https://github.com/kartta-labs/Project.
| [
{
"version": "v1",
"created": "Fri, 3 Dec 2021 01:44:38 GMT"
}
] | 1,638,748,800,000 | [
[
"Li",
"Zekun",
""
],
[
"Chiang",
"Yao-Yi",
""
],
[
"Tavakkol",
"Sasan",
""
],
[
"Shbita",
"Basel",
""
],
[
"Uhl",
"Johannes H.",
""
],
[
"Leyk",
"Stefan",
""
],
[
"Knoblock",
"Craig A.",
""
]
] |
2112.02045 | Hepeng Li | Hepeng Li, Nicholas Clavette and Haibo He | An Analytical Update Rule for General Policy Optimization | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We present an analytical policy update rule that is independent of parametric
function approximators. The policy update rule is suitable for optimizing
general stochastic policies and has a monotonic improvement guarantee. It is
derived from a closed-form solution to trust-region optimization using calculus
of variation, following a new theoretical result that tightens existing bounds
for policy improvement using trust-region methods. The update rule builds a
connection between policy search methods and value function methods. Moreover,
off-policy reinforcement learning algorithms can be derived from the update
rule since it does not need to compute integration over on-policy states. In
addition, the update rule extends immediately to cooperative multi-agent
systems when policy updates are performed by one agent at a time.
| [
{
"version": "v1",
"created": "Fri, 3 Dec 2021 17:50:11 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Jan 2022 13:34:33 GMT"
},
{
"version": "v3",
"created": "Sat, 14 May 2022 20:10:44 GMT"
},
{
"version": "v4",
"created": "Fri, 15 Jul 2022 05:22:39 GMT"
}
] | 1,658,102,400,000 | [
[
"Li",
"Hepeng",
""
],
[
"Clavette",
"Nicholas",
""
],
[
"He",
"Haibo",
""
]
] |
2112.02333 | Ishan Tarunesh | Ishan Tarunesh, Somak Aditya, Monojit Choudhury | LoNLI: An Extensible Framework for Testing Diverse Logical Reasoning
Capabilities for NLI | arXiv admin note: substantial text overlap with arXiv:2107.07229 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Natural Language Inference (NLI) is considered a representative task to test
natural language understanding (NLU). In this work, we propose an extensible
framework to collectively yet categorically test diverse Logical reasoning
capabilities required for NLI (and, by extension, NLU). Motivated by behavioral
testing, we create a semi-synthetic large test bench (363 templates, 363k
examples) and an associated framework that offers the following utilities: 1)
individually test and analyze reasoning capabilities along 17 reasoning
dimensions (including pragmatic reasoning); 2) design experiments to study
cross-capability information content (leave one out or bring one in); and 3)
the synthetic nature enables us to control for artifacts and biases. We extend
a publicly available framework of automated test case instantiation from
free-form natural language templates (CheckList) and a well-defined taxonomy of
capabilities to cover a wide range of increasingly harder test cases while
varying the complexity of natural language. Through our analysis of
state-of-the-art NLI systems, we observe that our benchmark is indeed hard (and
non-trivial even with training on additional resources). Some capabilities
stand out as harder. Further, fine-grained analysis and fine-tuning experiments
reveal more insights about these capabilities and the models -- supporting and
extending previous observations; thus showing the utility of the proposed
testbench.
| [
{
"version": "v1",
"created": "Sat, 4 Dec 2021 13:41:31 GMT"
},
{
"version": "v2",
"created": "Sat, 2 Sep 2023 08:28:54 GMT"
}
] | 1,693,958,400,000 | [
[
"Tarunesh",
"Ishan",
""
],
[
"Aditya",
"Somak",
""
],
[
"Choudhury",
"Monojit",
""
]
] |
2112.02457 | Danny Arlen De Jes\'us G\'omez-Ram\'irez | Danny A. J. Gomez-Ramirez, Yoe A. Herrera-Jaramillo and Florian
Geismann | Artificial Cognitively-inspired Generation of the Notion of Topological
Group in the Context of Artificial Mathematical Intelligence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The new computational paradigm of conceptual computation has been introduced
in the research program of Artificial Mathematical Intelligence. We provide the
explicit artificial generation (or conceptual computation) for the fundamental
mathematical notion of topological groups. Specifically, we start with two
basic notions belonging to topology and abstract algebra, and we describe
recursively formal specifications in the Common Algebraic Specification
Language (CASL). The notion of conceptual blending between such conceptual
spaces can be materialized computationally in the Heterogeneous Tool Set
(HETS). The fundamental notion of topological groups is explicitly generated
through three different artificial specifications based on conceptual blending
and conceptual identification, starting with the concepts of continuous
functions and mathematical groups (described with minimal set-theoretical
conditions). This constitutes in additional heuristic evidence for the third
pillar of Artificial Mathematical Intelligence.
| [
{
"version": "v1",
"created": "Sun, 5 Dec 2021 01:39:34 GMT"
}
] | 1,638,835,200,000 | [
[
"Gomez-Ramirez",
"Danny A. J.",
""
],
[
"Herrera-Jaramillo",
"Yoe A.",
""
],
[
"Geismann",
"Florian",
""
]
] |
2112.02513 | Zhang Zhang | Zhang Zhang, Yifeng Zeng, Yinghui Pan | Intention Recognition for Multiple Agents | 17pages, 30figures, 1 table, 2 algorithms, journal | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intention recognition is an important step to facilitate collaboration among
multiple agents. Existing work mainly focuses on intention recognition in a
single-agent setting and uses a descriptive model, e.g. Bayesian networks, in
the recognition process. In this article, we develop a new approach of
identifying intentions for multiple agents through a clustering algorithm. We
first define an intention model for multiple agents of interest. We use a
prescriptive approach to model agents' behaviours where their intentions are
hidden in the implementation of their plans. We introduce landmarks into the
behavioural model therefore enhancing informative features to identify common
intentions for multiple agents. Subsequently, we further refine the model by
focusing only action sequences in their plan and provide a light model for
identifying and comparing their intentions. The new models provide a simple
approach of grouping agents' common intentions upon partial plans observed in
agents' interactions. Then, we transform the intention recognition into an
un-supervised learning problem and adapt a clustering algorithm to group
intentions of multiple agents through comparing their behavioural models. We
conduct the clustering process by measuring similarity of probability
distributions over potential landmarks in intention models so as to discover
agents' common intentions. Finally, we examine the new intention recognition
approaches in two problem domains. We demonstrate importance of recognising
common intentions of multiple agents in achieving their goals and provide
experimental results to show performance of the new approaches.
| [
{
"version": "v1",
"created": "Sun, 5 Dec 2021 08:50:39 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 20:07:48 GMT"
}
] | 1,667,174,400,000 | [
[
"Zhang",
"Zhang",
""
],
[
"Zeng",
"Yifeng",
""
],
[
"Pan",
"Yinghui",
""
]
] |
2112.02690 | Zhenhailong Wang | Zhenhailong Wang, Heng Ji | Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot
Sentiment Classification | 9 pages, 2 figures, Thirty-Sixth AAAI Conference on Artificial
Intelligence (AAAI2022) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | State-of-the-art brain-to-text systems have achieved great success in
decoding language directly from brain signals using neural networks. However,
current approaches are limited to small closed vocabularies which are far from
enough for natural communication. In addition, most of the high-performing
approaches require data from invasive devices (e.g., ECoG). In this paper, we
extend the problem to open vocabulary Electroencephalography(EEG)-To-Text
Sequence-To-Sequence decoding and zero-shot sentence sentiment classification
on natural reading tasks. We hypothesis that the human brain functions as a
special text encoder and propose a novel framework leveraging pre-trained
language models (e.g., BART). Our model achieves a 40.1% BLEU-1 score on
EEG-To-Text decoding and a 55.6% F1 score on zero-shot EEG-based ternary
sentiment classification, which significantly outperforms supervised baselines.
Furthermore, we show that our proposed model can handle data from various
subjects and sources, showing great potential for a high-performance open
vocabulary brain-to-text system once sufficient data is available
| [
{
"version": "v1",
"created": "Sun, 5 Dec 2021 21:57:22 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Dec 2021 19:46:33 GMT"
},
{
"version": "v3",
"created": "Mon, 8 Jan 2024 02:30:27 GMT"
}
] | 1,704,758,400,000 | [
[
"Wang",
"Zhenhailong",
""
],
[
"Ji",
"Heng",
""
]
] |
2112.02810 | Kyudam Choi | Kyudam Choi, Yurim Lee, Cheongwon Kim, Minsung Yoon | An Effective GCN-based Hierarchical Multi-label classification for
Protein Function Prediction | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose an effective method to improve Protein Function Prediction (PFP)
utilizing hierarchical features of Gene Ontology (GO) terms. Our method
consists of a language model for encoding the protein sequence and a Graph
Convolutional Network (GCN) for representing GO terms. To reflect the
hierarchical structure of GO to GCN, we employ node(GO term)-wise
representations containing the whole hierarchical information. Our algorithm
shows effectiveness in a large-scale graph by expanding the GO graph compared
to previous models. Experimental results show that our method outperformed
state-of-the-art PFP approaches.
| [
{
"version": "v1",
"created": "Mon, 6 Dec 2021 06:45:49 GMT"
}
] | 1,638,835,200,000 | [
[
"Choi",
"Kyudam",
""
],
[
"Lee",
"Yurim",
""
],
[
"Kim",
"Cheongwon",
""
],
[
"Yoon",
"Minsung",
""
]
] |
2112.02989 | Cong Wang | Cong Wang, Tongwei Lu | On the complexity of Dark Chinese Chess | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper provides a complexity analysis for the game of dark Chinese chess
(a.k.a. "JieQi"), a variation of Chinese chess. Dark Chinese chess combines
some of the most complicated aspects of board and card games, such as long-term
strategy or planning, large state space, stochastic, and imperfect-information,
which make it closer to the real world decision-making problem and pose great
challenges to game AI. Here we design a self-play program to calculate the game
tree complexity and average information set size of the game, and propose an
algorithm to calculate the number of information sets.
| [
{
"version": "v1",
"created": "Mon, 6 Dec 2021 13:08:53 GMT"
}
] | 1,638,835,200,000 | [
[
"Wang",
"Cong",
""
],
[
"Lu",
"Tongwei",
""
]
] |
2112.03168 | Mansi Sharma | Aditya Kanade and Mansi Sharma and M. Manivannan | Tele-EvalNet: A Low-cost, Teleconsultation System for Home based
Rehabilitation of Stroke Survivors using Multiscale CNN-LSTM Architecture | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Technology has an important role to play in the field of Rehabilitation,
improving patient outcomes and reducing healthcare costs. However, existing
approaches lack clinical validation, robustness and ease of use. We propose
Tele-EvalNet, a novel system consisting of two components: a live feedback
model and an overall performance evaluation model. The live feedback model
demonstrates feedback on exercise correctness with easy to understand
instructions highlighted using color markers. The overall performance
evaluation model learns a mapping of joint data to scores, given to the
performance by clinicians. The model does this by extracting clinically
approved features from joint data. Further, these features are encoded to a
lower dimensional space with an autoencoder. A novel multi-scale CNN-LSTM
network is proposed to learn a mapping of performance data to the scores by
leveraging features extracted at multiple scales. The proposed system shows a
high degree of improvement in score predictions and outperforms the
state-of-the-art rehabilitation models.
| [
{
"version": "v1",
"created": "Mon, 6 Dec 2021 16:58:00 GMT"
}
] | 1,638,835,200,000 | [
[
"Kanade",
"Aditya",
""
],
[
"Sharma",
"Mansi",
""
],
[
"Manivannan",
"M.",
""
]
] |
2112.04087 | Ganqiang Ye | Ganqiang Ye, Wen Zhang, Zhen Bi, Chi Man Wong, Chen Hui and Huajun
Chen | Improving Knowledge Graph Representation Learning by Structure
Contextual Pre-training | Accepted to IJCKG 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Representation learning models for Knowledge Graphs (KG) have proven to be
effective in encoding structural information and performing reasoning over KGs.
In this paper, we propose a novel pre-training-then-fine-tuning framework for
knowledge graph representation learning, in which a KG model is firstly
pre-trained with triple classification task, followed by discriminative
fine-tuning on specific downstream tasks such as entity type prediction and
entity alignment. Drawing on the general ideas of learning deep contextualized
word representations in typical pre-trained language models, we propose SCoP to
learn pre-trained KG representations with structural and contextual triples of
the target triple encoded. Experimental results demonstrate that fine-tuning
SCoP not only outperforms results of baselines on a portfolio of downstream
tasks but also avoids tedious task-specific model design and parameter
training.
| [
{
"version": "v1",
"created": "Wed, 8 Dec 2021 02:50:54 GMT"
}
] | 1,639,008,000,000 | [
[
"Ye",
"Ganqiang",
""
],
[
"Zhang",
"Wen",
""
],
[
"Bi",
"Zhen",
""
],
[
"Wong",
"Chi Man",
""
],
[
"Hui",
"Chen",
""
],
[
"Chen",
"Huajun",
""
]
] |
2112.04145 | Jiajun Fan | Jiajun Fan | A Review for Deep Reinforcement Learning in Atari:Benchmarks,
Challenges, and Solutions | preliminary work, preprint | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Arcade Learning Environment (ALE) is proposed as an evaluation platform
for empirically assessing the generality of agents across dozens of Atari 2600
games. ALE offers various challenging problems and has drawn significant
attention from the deep reinforcement learning (RL) community. From Deep
Q-Networks (DQN) to Agent57, RL agents seem to achieve superhuman performance
in ALE. However, is this the case? In this paper, to explore this problem, we
first review the current evaluation metrics in the Atari benchmarks and then
reveal that the current evaluation criteria of achieving superhuman performance
are inappropriate, which underestimated the human performance relative to what
is possible. To handle those problems and promote the development of RL
research, we propose a novel Atari benchmark based on human world records
(HWR), which puts forward higher requirements for RL agents on both final
performance and learning efficiency. Furthermore, we summarize the
state-of-the-art (SOTA) methods in Atari benchmarks and provide benchmark
results over new evaluation metrics based on human world records. We concluded
that at least four open challenges hinder RL agents from achieving superhuman
performance from those new benchmark results. Finally, we also discuss some
promising ways to handle those problems.
| [
{
"version": "v1",
"created": "Wed, 8 Dec 2021 06:52:23 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Dec 2021 14:48:34 GMT"
},
{
"version": "v3",
"created": "Sat, 11 Jun 2022 13:31:47 GMT"
},
{
"version": "v4",
"created": "Thu, 16 Jun 2022 16:55:57 GMT"
},
{
"version": "v5",
"created": "Mon, 27 Feb 2023 02:09:25 GMT"
}
] | 1,677,542,400,000 | [
[
"Fan",
"Jiajun",
""
]
] |
2112.04286 | Damien Pellier | Maxence Grand, Damien Pellier and Humbert Fiorino | TempAMLSI : Temporal Action Model Learning based on Grammar Induction | Proceedings of the International workshop of Knowledge Engineering
for Planning and Scheduling (ICAPS), 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hand-encoding PDDL domains is generally accepted as difficult, tedious and
error-prone. The difficulty is even greater when temporal domains have to be
encoded. Indeed, actions have a duration and their effects are not
instantaneous. In this paper, we present TempAMLSI, an algorithm based on the
AMLSI approach able to learn temporal domains. TempAMLSI is based on the
classical assumption done in temporal planning that it is possible to convert a
non-temporal domain into a temporal domain. TempAMLSI is the first approach
able to learn temporal domain with single hard envelope and Cushing's
intervals. We show experimentally that TempAMLSI is able to learn accurate
temporal domains, i.e., temporal domain that can be used directly to solve new
planning problem, with different forms of action concurrency.
| [
{
"version": "v1",
"created": "Wed, 8 Dec 2021 13:46:08 GMT"
}
] | 1,639,008,000,000 | [
[
"Grand",
"Maxence",
""
],
[
"Pellier",
"Damien",
""
],
[
"Fiorino",
"Humbert",
""
]
] |
2112.04751 | Kirill Krinkin | Kirill Krinkin and Yulia Shichkina and Andrey Ignatyev | Co-evolutionary hybrid intelligence | 4 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial intelligence is one of the drivers of modern technological
development. The current approach to the development of intelligent systems is
data-centric. It has several limitations: it is fundamentally impossible to
collect data for modeling complex objects and processes; training neural
networks requires huge computational and energy resources; solutions are not
explainable. The article discusses an alternative approach to the development
of artificial intelligence systems based on human-machine hybridization and
their co-evolution.
| [
{
"version": "v1",
"created": "Thu, 9 Dec 2021 08:14:56 GMT"
}
] | 1,639,094,400,000 | [
[
"Krinkin",
"Kirill",
""
],
[
"Shichkina",
"Yulia",
""
],
[
"Ignatyev",
"Andrey",
""
]
] |
2112.05218 | Mingxuan Li | Yiheng Xie, Mingxuan Li, Shangqun Yu, Michael Littman | Learning Generalizable Behavior via Visual Rewrite Rules | AAAI 2022 Workshop on Reinforcement Learning in Games | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Though deep reinforcement learning agents have achieved unprecedented success
in recent years, their learned policies can be brittle, failing to generalize
to even slight modifications of their environments or unfamiliar situations.
The black-box nature of the neural network learning dynamics makes it
impossible to audit trained deep agents and recover from such failures. In this
paper, we propose a novel representation and learning approach to capture
environment dynamics without using neural networks. It originates from the
observation that, in games designed for people, the effect of an action can
often be perceived in the form of local changes in consecutive visual
observations. Our algorithm is designed to extract such vision-based changes
and condense them into a set of action-dependent descriptive rules, which we
call ''visual rewrite rules'' (VRRs). We also present preliminary results from
a VRR agent that can explore, expand its rule set, and solve a game via
planning with its learned VRR world model. In several classical games, our
non-deep agent demonstrates superior performance, extreme sample efficiency,
and robust generalization ability compared with several mainstream deep agents.
| [
{
"version": "v1",
"created": "Thu, 9 Dec 2021 21:23:26 GMT"
}
] | 1,639,353,600,000 | [
[
"Xie",
"Yiheng",
""
],
[
"Li",
"Mingxuan",
""
],
[
"Yu",
"Shangqun",
""
],
[
"Littman",
"Michael",
""
]
] |
2112.05434 | Qiming Ye Mr | Qiming Ye, Yuxiang Feng, Jing Han, Marc Stettler, Panagiotis
Angeloudis | A Reinforcement Learning-based Adaptive Control Model for Future Street
Planning, An Algorithm and A Case Study | Proceeding for 57th ISOCARP World Planning Congress, Nov 8-11, 2021,
Doha, Qatar | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With the emerging technologies in Intelligent Transportation System (ITS),
the adaptive operation of road space is likely to be realised within decades.
An intelligent street can learn and improve its decision-making on the
right-of-way (ROW) for road users, liberating more active pedestrian space
while maintaining traffic safety and efficiency. However, there is a lack of
effective controlling techniques for these adaptive street infrastructures. To
fill this gap in existing studies, we formulate this control problem as a
Markov Game and develop a solution based on the multi-agent Deep Deterministic
Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign
ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street
parking areas in real-time. Integrated with the SUMO traffic simulator, this
model was evaluated using the road network of the South Kensington District
against three cases of divergent traffic conditions: pedestrian flow rates, AVs
traffic flow rates and parking demands. Results reveal that our model can
achieve an average reduction of 3.87% and 6.26% in street space assigned for
on-street parking and vehicular operations. Combined with space gained by
limiting the number of driving lanes, the average proportion of sidewalks to
total widths of streets can significantly increase by 10.13%.
| [
{
"version": "v1",
"created": "Fri, 10 Dec 2021 10:32:46 GMT"
}
] | 1,639,353,600,000 | [
[
"Ye",
"Qiming",
""
],
[
"Feng",
"Yuxiang",
""
],
[
"Han",
"Jing",
""
],
[
"Stettler",
"Marc",
""
],
[
"Angeloudis",
"Panagiotis",
""
]
] |
2112.05614 | Alun Preece | Mihai Boicu, Erik Blasch, Alun Preece | AAAI FSS-21: Artificial Intelligence in Government and Public Sector
Proceedings | Post-symposium proceedings including 9 papers | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proceedings of the AAAI Fall Symposium on Artificial Intelligence in
Government and Public Sector, Washington, DC, USA, November 4-6, 2021
| [
{
"version": "v1",
"created": "Fri, 10 Dec 2021 15:48:31 GMT"
}
] | 1,639,353,600,000 | [
[
"Boicu",
"Mihai",
""
],
[
"Blasch",
"Erik",
""
],
[
"Preece",
"Alun",
""
]
] |
2112.05638 | Wu Xing | Chaochen Gao, Xing Wu, Peng Wang, Jue Wang, Liangjun Zang, Zhongyuan
Wang, Songlin Hu | DistilCSE: Effective Knowledge Distillation For Contrastive Sentence
Embeddings | Work in progress | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large-scale contrastive learning models can learn very informative sentence
embeddings, but are hard to serve online due to the huge model size. Therefore,
they often play the role of "teacher", transferring abilities to small
"student" models through knowledge distillation. However, knowledge
distillation inevitably brings some drop in embedding effect. To tackle that,
we propose an effective knowledge distillation framework for contrastive
sentence embeddings, termed DistilCSE. It first applies knowledge distillation
on a large amount of unlabeled data, and then fine-tunes student models through
contrastive learning on limited labeled data. To achieve better distillation
results, we further propose Contrastive Knowledge Distillation (CKD). CKD uses
InfoNCE as the loss function in knowledge distillation, enhancing the objective
consistency among teacher model training, knowledge distillation, and student
model fine-tuning. Extensive experiments show that student models trained with
the proposed DistilCSE and CKD suffer from little or even no performance
decrease and consistently outperform the corresponding counterparts of the same
parameter size. Impressively, our 110M student model outperforms the latest
state-of-the-art model, i.e., Sentence-T5 (11B), with only 1% parameters and
0.25% unlabeled data.
| [
{
"version": "v1",
"created": "Fri, 10 Dec 2021 16:11:23 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Jan 2023 16:31:21 GMT"
}
] | 1,675,123,200,000 | [
[
"Gao",
"Chaochen",
""
],
[
"Wu",
"Xing",
""
],
[
"Wang",
"Peng",
""
],
[
"Wang",
"Jue",
""
],
[
"Zang",
"Liangjun",
""
],
[
"Wang",
"Zhongyuan",
""
],
[
"Hu",
"Songlin",
""
]
] |
2112.05700 | Brianna Richardson | Brianna Richardson, Juan E. Gilbert | A Framework for Fairness: A Systematic Review of Existing Fair AI
Solutions | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In a world of daily emerging scientific inquisition and discovery, the
prolific launch of machine learning across industries comes to little surprise
for those familiar with the potential of ML. Neither so should the congruent
expansion of ethics-focused research that emerged as a response to issues of
bias and unfairness that stemmed from those very same applications. Fairness
research, which focuses on techniques to combat algorithmic bias, is now more
supported than ever before. A large portion of fairness research has gone to
producing tools that machine learning practitioners can use to audit for bias
while designing their algorithms. Nonetheless, there is a lack of application
of these fairness solutions in practice. This systematic review provides an
in-depth summary of the algorithmic bias issues that have been defined and the
fairness solution space that has been proposed. Moreover, this review provides
an in-depth breakdown of the caveats to the solution space that have arisen
since their release and a taxonomy of needs that have been proposed by machine
learning practitioners, fairness researchers, and institutional stakeholders.
These needs have been organized and addressed to the parties most influential
to their implementation, which includes fairness researchers, organizations
that produce ML algorithms, and the machine learning practitioners themselves.
These findings can be used in the future to bridge the gap between
practitioners and fairness experts and inform the creation of usable fair ML
toolkits.
| [
{
"version": "v1",
"created": "Fri, 10 Dec 2021 17:51:20 GMT"
}
] | 1,639,353,600,000 | [
[
"Richardson",
"Brianna",
""
],
[
"Gilbert",
"Juan E.",
""
]
] |
2112.05742 | Adrian Groza | Roxana Szomiu and Adrian Groza | A Puzzle-Based Dataset for Natural Language Inference | null | null | 10.13140/RG.2.2.19206.09289 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We provide here a dataset for tasks related to natural language understanding
and natural language inference. The dataset contains logical puzzles in natural
language from three domains: comparing puzzles, knighs and knaves, and zebra
puzzles. Each puzzle is associated with the entire set of atomic questions that
can be generated based on the relations and individuals occurring in the text.
For each question we provide the correct answer: entailment, contradiction or
ambiguity. The answer's correctness is verified against theorem provers. Good
puzzles have two properties: (i) each piece of information is necessary and
(ii) no unnecessary information is provided. These properties make puzzles
interesting candidates for machine comprehension tasks.
| [
{
"version": "v1",
"created": "Fri, 10 Dec 2021 18:53:06 GMT"
}
] | 1,639,353,600,000 | [
[
"Szomiu",
"Roxana",
""
],
[
"Groza",
"Adrian",
""
]
] |
2112.06028 | Hankz Hankui Zhuo | Siqi Hong, Hankz Hankui Zhuo, Kebing Jin, Guang Shao, Zhanwen Zhou | Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | In retrosynthetic planning, the huge number of possible routes to synthesize
a complex molecule using simple building blocks leads to a combinatorial
explosion of possibilities. Even experienced chemists often have difficulty to
select the most promising transformations. The current approaches rely on
human-defined or machine-trained score functions which have limited chemical
knowledge or use expensive estimation methods for guiding. Here we an propose
experience-guided Monte Carlo tree search (EG-MCTS) to deal with this problem.
Instead of rollout, we build an experience guidance network to learn knowledge
from synthetic experiences during the search. Experiments on benchmark USPTO
datasets show that, EG-MCTS gains significant improvement over state-of-the-art
approaches both in efficiency and effectiveness. In a comparative experiment
with the literature, our computer-generated routes mostly matched the reported
routes. Routes designed for real drug compounds exhibit the effectiveness of
EG-MCTS on assisting chemists performing retrosynthetic analysis.
| [
{
"version": "v1",
"created": "Sat, 11 Dec 2021 17:14:15 GMT"
},
{
"version": "v2",
"created": "Sat, 10 Jun 2023 03:13:46 GMT"
}
] | 1,686,614,400,000 | [
[
"Hong",
"Siqi",
""
],
[
"Zhuo",
"Hankz Hankui",
""
],
[
"Jin",
"Kebing",
""
],
[
"Shao",
"Guang",
""
],
[
"Zhou",
"Zhanwen",
""
]
] |
2112.06055 | Juan Jose Garau-Luis | Juan Jose Garau-Luis and Skylar Eiskowitz and Nils Pachler and Edward
Crawley and Bruce Cameron | Towards Autonomous Satellite Communications: An AI-based Framework to
Address System-level Challenges | AAAI Workshop on AI to Accelerate Science and Engineering, at AAAI
Conference 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | The next generation of satellite constellations is designed to better address
the future needs of our connected society: highly-variable data demand, mobile
connectivity, and reaching more under-served regions. Artificial Intelligence
(AI) and learning-based methods are expected to become key players in the
industry, given the poor scalability and slow reaction time of current resource
allocation mechanisms. While AI frameworks have been validated for isolated
communication tasks or subproblems, there is still not a clear path to achieve
fully-autonomous satellite systems. Part of this issue results from the focus
on subproblems when designing models, instead of the necessary system-level
perspective. In this paper we try to bridge this gap by characterizing the
system-level needs that must be met to increase satellite autonomy, and
introduce three AI-based components (Demand Estimator, Offline Planner, and
Real Time Engine) that jointly address them. We first do a broad literature
review on the different subproblems and identify the missing links to the
system-level goals. In response to these gaps, we outline the three necessary
components and highlight their interactions. We also discuss how current models
can be incorporated into the framework and possible directions of future work.
| [
{
"version": "v1",
"created": "Sat, 11 Dec 2021 19:36:58 GMT"
}
] | 1,639,440,000,000 | [
[
"Garau-Luis",
"Juan Jose",
""
],
[
"Eiskowitz",
"Skylar",
""
],
[
"Pachler",
"Nils",
""
],
[
"Crawley",
"Edward",
""
],
[
"Cameron",
"Bruce",
""
]
] |
2112.06780 | Prateek Goel | Prateek Goel, Adam J. Johs, Manil Shrestha, and Rosina O. Weber | Explanation Container in Case-Based Biomedical Question-Answering | Incomplete acknowledgments. Paper to be withdrawn until further
notice | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The National Center for Advancing Translational Sciences(NCATS) Biomedical
Data Translator (Translator) aims to attenuate problems faced by translational
scientists. Translator is a multi-agent architecture consisting of six
autonomous relay agents (ARAs) and eight knowledge providers (KPs). In this
paper, we present the design of the Explanatory Agent (xARA), a case-based ARA
that answers biomedical queries by accessing multiple KPs, ranking results, and
explaining the ranking of results. The Explanatory Agent is designed with five
knowledge containers that include the four original knowledge containers and
one additional container for explanation - the Explanation Container. The
Explanation Container is case-based and designed with its own knowledge
containers.
| [
{
"version": "v1",
"created": "Mon, 13 Dec 2021 16:44:27 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Dec 2021 17:36:07 GMT"
}
] | 1,640,217,600,000 | [
[
"Goel",
"Prateek",
""
],
[
"Johs",
"Adam J.",
""
],
[
"Shrestha",
"Manil",
""
],
[
"Weber",
"Rosina O.",
""
]
] |
2112.06917 | Mao Luo | Mao Luo, Chu-Min Li, Xinyun Wu, Shuolin Li, Zhipeng L\"u | Branching Strategy Selection Approach Based on Vivification Ratio | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The two most effective branching strategies LRB and VSIDS perform differently
on different types of instances. Generally, LRB is more effective on crafted
instances, while VSIDS is more effective on application ones. However,
distinguishing the types of instances is difficult. To overcome this drawback,
we propose a branching strategy selection approach based on the vivification
ratio. This approach uses the LRB branching strategy more to solve the
instances with a very low vivification ratio. We tested the instances from the
main track of SAT competitions in recent years. The results show that the
proposed approach is robust and it significantly increases the number of solved
instances. It is worth mentioning that, with the help of our approach, the
solver Maple\_CM can solve more than 16 instances for the benchmark from the
2020 SAT competition.
| [
{
"version": "v1",
"created": "Sat, 11 Dec 2021 04:07:39 GMT"
}
] | 1,639,526,400,000 | [
[
"Luo",
"Mao",
""
],
[
"Li",
"Chu-Min",
""
],
[
"Wu",
"Xinyun",
""
],
[
"Li",
"Shuolin",
""
],
[
"Lü",
"Zhipeng",
""
]
] |
2112.07045 | Ahmad Hassanat | Ahmad B. Hassanat, Ghada A. Altarawneh, and Ahmad S. Tarawneh, David
Carfi, Abdullah Almuhaimeed | Fuzzy Win-Win: A Novel Approach to Quantify Win-Win Using Fuzzy Logic | 25 pages, 5 figures | Mathematics, 10, 2022, 884 | 10.3390/math10060884 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The classic win-win has a key flaw in that it cannot offer the parties the
right amounts of winning because each party believes they are winners. In
reality, one party may win more than the other. This strategy is not limited to
a single product or negotiation; it may be applied to a variety of situations
in life. We present a novel way to measure the win-win situation in this paper.
The proposed method employs Fuzzy logic to create a mathematical model that
aids negotiators in quantifying their winning percentages. The model is put to
the test on real-life negotiations scenarios such as the Iraqi-Jordanian oil
deal, and the iron ore negotiation (2005-2009). The presented model has shown
to be a useful tool in practice and can be easily generalized to be utilized in
other domains as well.
| [
{
"version": "v1",
"created": "Mon, 13 Dec 2021 22:17:43 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Feb 2022 13:25:50 GMT"
}
] | 1,647,561,600,000 | [
[
"Hassanat",
"Ahmad B.",
""
],
[
"Altarawneh",
"Ghada A.",
""
],
[
"Tarawneh",
"Ahmad S.",
""
],
[
"Carfi",
"David",
""
],
[
"Almuhaimeed",
"Abdullah",
""
]
] |
2112.07493 | Samaneh Jozashoori | Samaneh Jozashoori, Ahmad Sakor, Enrique Iglesias, Maria-Esther Vidal | EABlock: A Declarative Entity Alignment Block for Knowledge Graph
Creation Pipelines | null | null | 10.1145/3477314.3507132 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite encoding enormous amount of rich and valuable data, existing data
sources are mostly created independently, being a significant challenge to
their integration. Mapping languages, e.g., RML and R2RML, facilitate
declarative specification of the process of applying meta-data and integrating
data into a knowledge graph. Mapping rules can also include knowledge
extraction functions in addition to expressing correspondences among data
sources and a unified schema. Combining mapping rules and functions represents
a powerful formalism to specify pipelines for integrating data into a knowledge
graph transparently. Surprisingly, these formalisms are not fully adapted, and
many knowledge graphs are created by executing ad-hoc programs to pre-process
and integrate data. In this paper, we present EABlock, an approach integrating
Entity Alignment (EA) as part of RML mapping rules. EABlock includes a block of
functions performing entity recognition from textual attributes and link the
recognized entities to the corresponding resources in Wikidata, DBpedia, and
domain specific thesaurus, e.g., UMLS. EABlock provides agnostic and efficient
techniques to evaluate the functions and transfer the mappings to facilitate
its application in any RML-compliant engine. We have empirically evaluated
EABlock performance, and results indicate that EABlock speeds up knowledge
graph creation pipelines that require entity recognition and linking in
state-of-the-art RML-compliant engines. EABlock is also publicly available as a
tool through a GitHub repository(https://github.com/SDM-TIB/EABlock) and a
DOI(https://doi.org/10.5281/zenodo.5779773).
| [
{
"version": "v1",
"created": "Tue, 14 Dec 2021 15:59:03 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Dec 2021 16:30:15 GMT"
}
] | 1,663,804,800,000 | [
[
"Jozashoori",
"Samaneh",
""
],
[
"Sakor",
"Ahmad",
""
],
[
"Iglesias",
"Enrique",
""
],
[
"Vidal",
"Maria-Esther",
""
]
] |
2112.07761 | Marek Szyku{\l}a | Jakub Kowalski, Maksymilian Mika, Wojciech Pawlik, Jakub Sutowicz,
Marek Szyku{\l}a, Mark H. M. Winands | Split Moves for Monte-Carlo Tree Search | AAAI 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In many games, moves consist of several decisions made by the player. These
decisions can be viewed as separate moves, which is already a common practice
in multi-action games for efficiency reasons. Such division of a player move
into a sequence of simpler / lower level moves is called \emph{splitting}. So
far, split moves have been applied only in forementioned straightforward cases,
and furthermore, there was almost no study revealing its impact on agents'
playing strength. Taking the knowledge-free perspective, we aim to answer how
to effectively use split moves within Monte-Carlo Tree Search (MCTS) and what
is the practical impact of split design on agents' strength. This paper
proposes a generalization of MCTS that works with arbitrarily split moves. We
design several variations of the algorithm and try to measure the impact of
split moves separately on efficiency, quality of MCTS, simulations, and
action-based heuristics. The tests are carried out on a set of board games and
performed using the Regular Boardgames General Game Playing formalism, where
split strategies of different granularity can be automatically derived based on
an abstract description of the game. The results give an overview of the
behavior of agents using split design in different ways. We conclude that split
design can be greatly beneficial for single- as well as multi-action games.
| [
{
"version": "v1",
"created": "Tue, 14 Dec 2021 22:06:54 GMT"
}
] | 1,639,612,800,000 | [
[
"Kowalski",
"Jakub",
""
],
[
"Mika",
"Maksymilian",
""
],
[
"Pawlik",
"Wojciech",
""
],
[
"Sutowicz",
"Jakub",
""
],
[
"Szykuła",
"Marek",
""
],
[
"Winands",
"Mark H. M.",
""
]
] |
2112.07867 | Aman Madaan | Niket Tandon, Aman Madaan, Peter Clark, Keisuke Sakaguchi, Yiming Yang | Interscript: A dataset for interactive learning of scripts through error
feedback | AAAI'22-Workshop on Interactive Machine Learning | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | How can an end-user provide feedback if a deployed structured prediction
model generates inconsistent output, ignoring the structural complexity of
human language? This is an emerging topic with recent progress in synthetic or
constrained settings, and the next big leap would require testing and tuning
models in real-world settings. We present a new dataset, Interscript,
containing user feedback on a deployed model that generates complex everyday
tasks. Interscript contains 8,466 data points -- the input is a possibly
erroneous script and a user feedback, and the output is a modified script. We
posit two use-cases of \ours that might significantly advance the
state-of-the-art in interactive learning. The dataset is available at:
https://github.com/allenai/interscript.
| [
{
"version": "v1",
"created": "Wed, 15 Dec 2021 04:04:03 GMT"
},
{
"version": "v2",
"created": "Thu, 16 Dec 2021 03:31:52 GMT"
}
] | 1,639,699,200,000 | [
[
"Tandon",
"Niket",
""
],
[
"Madaan",
"Aman",
""
],
[
"Clark",
"Peter",
""
],
[
"Sakaguchi",
"Keisuke",
""
],
[
"Yang",
"Yiming",
""
]
] |
2112.08589 | Wen Zhang | Wen Zhang, Shumin Deng, Mingyang Chen, Liang Wang, Qiang Chen, Feiyu
Xiong, Xiangwen Liu, Huajun Chen | Knowledge Graph Embedding in E-commerce Applications: Attentive
Reasoning, Explanations, and Transferable Rules | Accepted at IJCKG2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Graphs (KGs), representing facts as triples, have been widely
adopted in many applications. Reasoning tasks such as link prediction and rule
induction are important for the development of KGs. Knowledge Graph Embeddings
(KGEs) embedding entities and relations of a KG into continuous vector spaces,
have been proposed for these reasoning tasks and proven to be efficient and
robust. But the plausibility and feasibility of applying and deploying KGEs in
real-work applications has not been well-explored. In this paper, we discuss
and report our experiences of deploying KGEs in a real domain application:
e-commerce. We first identity three important desiderata for e-commerce KG
systems: 1) attentive reasoning, reasoning over a few target relations of more
concerns instead of all; 2) explanation, providing explanations for a
prediction to help both users and business operators understand why the
prediction is made; 3) transferable rules, generating reusable rules to
accelerate the deployment of a KG to new systems. While non existing KGE could
meet all these desiderata, we propose a novel one, an explainable knowledge
graph attention network that make prediction through modeling correlations
between triples rather than purely relying on its head entity, relation and
tail entity embeddings. It could automatically selects attentive triples for
prediction and records the contribution of them at the same time, from which
explanations could be easily provided and transferable rules could be
efficiently produced. We empirically show that our method is capable of meeting
all three desiderata in our e-commerce application and outperform typical
baselines on datasets from real domain applications.
| [
{
"version": "v1",
"created": "Thu, 16 Dec 2021 03:26:36 GMT"
}
] | 1,639,699,200,000 | [
[
"Zhang",
"Wen",
""
],
[
"Deng",
"Shumin",
""
],
[
"Chen",
"Mingyang",
""
],
[
"Wang",
"Liang",
""
],
[
"Chen",
"Qiang",
""
],
[
"Xiong",
"Feiyu",
""
],
[
"Liu",
"Xiangwen",
""
],
[
"Chen",
"Huajun",
""
]
] |
2112.09462 | Jasmina Gajcin | Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati, Radu Marinescu,
Elizabeth Daly, Ivana Dusparic | Contrastive Explanations for Comparing Preferences of Reinforcement
Learning Agents | 7 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In complex tasks where the reward function is not straightforward and
consists of a set of objectives, multiple reinforcement learning (RL) policies
that perform task adequately, but employ different strategies can be trained by
adjusting the impact of individual objectives on reward function. Understanding
the differences in strategies between policies is necessary to enable users to
choose between offered policies, and can help developers understand different
behaviors that emerge from various reward functions and training
hyperparameters in RL systems. In this work we compare behavior of two policies
trained on the same task, but with different preferences in objectives. We
propose a method for distinguishing between differences in behavior that stem
from different abilities from those that are a consequence of opposing
preferences of two RL agents. Furthermore, we use only data on preference-based
differences in order to generate contrasting explanations about agents'
preferences. Finally, we test and evaluate our approach on an autonomous
driving task and compare the behavior of a safety-oriented policy and one that
prefers speed.
| [
{
"version": "v1",
"created": "Fri, 17 Dec 2021 11:57:57 GMT"
}
] | 1,639,958,400,000 | [
[
"Gajcin",
"Jasmina",
""
],
[
"Nair",
"Rahul",
""
],
[
"Pedapati",
"Tejaswini",
""
],
[
"Marinescu",
"Radu",
""
],
[
"Daly",
"Elizabeth",
""
],
[
"Dusparic",
"Ivana",
""
]
] |
2112.09573 | Shaul Zevin | Zevin Shaul, Sheikh Naaz | cgSpan: Closed Graph-Based Substructure Pattern Mining | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | gSpan is a popular algorithm for mining frequent subgraphs. cgSpan (closed
graph-based substructure pattern mining) is a gSpan extension that only mines
closed subgraphs. A subgraph g is closed in the graphs database if there is no
proper frequent supergraph of g that has equivalent occurrence with g. cgSpan
adds the Early Termination pruning method to the gSpan pruning methods, while
leaving the original gSpan steps unchanged. cgSpan also detects and handles
cases in which Early Termination should not be applied. To the best of our
knowledge, cgSpan is the first publicly available implementation for closed
graphs mining
| [
{
"version": "v1",
"created": "Fri, 17 Dec 2021 15:27:20 GMT"
}
] | 1,639,958,400,000 | [
[
"Shaul",
"Zevin",
""
],
[
"Naaz",
"Sheikh",
""
]
] |
2112.10190 | Koen Holtman | Koen Holtman | Demanding and Designing Aligned Cognitive Architectures | PERLS Workshop at 35th Conference on Neural Information Processing
Systems (NeurIPS 2021). This arXiv version extends the workshop camera-ready
version by adding four figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With AI systems becoming more powerful and pervasive, there is increasing
debate about keeping their actions aligned with the broader goals and needs of
humanity. This multi-disciplinary and multi-stakeholder debate must resolve
many issues, here we examine three of them. The first issue is to clarify what
demands stakeholders might usefully make on the designers of AI systems, useful
because the technology exists to implement them. We make this technical topic
more accessible by using the framing of cognitive architectures. The second
issue is to move beyond an analytical framing that treats useful intelligence
as being reward maximization only. To support this move, we define several AI
cognitive architectures that combine reward maximization with other technical
elements designed to improve alignment. The third issue is how stakeholders
should calibrate their interactions with modern machine learning researchers.
We consider how current fashions in machine learning create a narrative pull
that participants in technical and policy discussions should be aware of, so
that they can compensate for it. We identify several technically tractable but
currently unfashionable options for improving AI alignment.
| [
{
"version": "v1",
"created": "Sun, 19 Dec 2021 16:49:28 GMT"
}
] | 1,640,044,800,000 | [
[
"Holtman",
"Koen",
""
]
] |
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