Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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21,402 | Abstractly Interpreting Argumentation Frameworks for Sharpening
Extensions | cs.AI | Cycles of attacking arguments pose non-trivial issues in Dung style
argumentation theory, apparent behavioural difference between odd and even
length cycles being a notable one. While a few methods were proposed for
treating them, to - in particular - enable selection of acceptable arguments in
an odd-length cycle when... | computer science |
21,403 | Learning from Richer Human Guidance: Augmenting Comparison-Based
Learning with Feature Queries | cs.AI | We focus on learning the desired objective function for a robot. Although
trajectory demonstrations can be very informative of the desired objective,
they can also be difficult for users to provide. Answers to comparison queries,
asking which of two trajectories is preferable, are much easier for users, and
have emerge... | computer science |
21,404 | Augmented Artificial Intelligence: a Conceptual Framework | cs.AI | All artificial Intelligence (AI) systems make errors. These errors are
unexpected, and differ often from the typical human mistakes ("non-human"
errors). The AI errors should be corrected without damage of existing skills
and, hopefully, avoiding direct human expertise. This paper presents an initial
summary report of ... | computer science |
21,405 | Evolutionary Computation plus Dynamic Programming for the Bi-Objective
Travelling Thief Problem | cs.AI | This research proposes a novel indicator-based hybrid evolutionary approach
that combines approximate and exact algorithms. We apply it to a new
bi-criteria formulation of the travelling thief problem, which is known to the
Evolutionary Computation community as a benchmark multi-component optimisation
problem that inte... | computer science |
21,406 | Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete
and Incomplete Data Sets | cs.AI | Learning a Bayesian networks with bounded treewidth is important for reducing
the complexity of the inferences. We present a novel anytime algorithm (k-MAX)
method for this task, which scales up to thousands of variables. Through
extensive experiments we show that it consistently yields higher-scoring
structures than i... | computer science |
21,407 | Balancing Two-Player Stochastic Games with Soft Q-Learning | cs.AI | Within the context of video games the notion of perfectly rational agents can
be undesirable as it leads to uninteresting situations, where humans face tough
adversarial decision makers. Current frameworks for stochastic games and
reinforcement learning prohibit tuneable strategies as they seek optimal
performance. In ... | computer science |
21,408 | Narrow Artificial Intelligence with Machine Learning for Real-Time
Estimation of a Mobile Agents Location Using Hidden Markov Models | cs.AI | We propose to use a supervised machine learning technique to track the
location of a mobile agent in real time. Hidden Markov Models are used to build
artificial intelligence that estimates the unknown position of a mobile target
moving in a defined environment. This narrow artificial intelligence performs
two distinct... | computer science |
21,409 | More Robust Doubly Robust Off-policy Evaluation | cs.AI | We study the problem of off-policy evaluation (OPE) in reinforcement learning
(RL), where the goal is to estimate the performance of a policy from the data
generated by another policy(ies). In particular, we focus on the doubly robust
(DR) estimators that consist of an importance sampling (IS) component and a
performan... | computer science |
21,410 | Graph Planning with Expected Finite Horizon | cs.AI | Graph planning gives rise to fundamental algorithmic questions such as
shortest path, traveling salesman problem, etc. A classical problem in discrete
planning is to consider a weighted graph and construct a path that maximizes
the sum of weights for a given time horizon $T$. However, in many scenarios,
the time horizo... | computer science |
21,411 | Distinguishing Question Subjectivity from Difficulty for Improved
Crowdsourcing | cs.AI | The questions in a crowdsourcing task typically exhibit varying degrees of
difficulty and subjectivity. Their joint effects give rise to the variation in
responses to the same question by different crowd-workers. This variation is
low when the question is easy to answer and objective, and high when it is
difficult and ... | computer science |
21,412 | The Complex Event Recognition Group | cs.AI | The Complex Event Recognition (CER) group is a research team, affiliated with
the National Centre of Scientific Research "Demokritos" in Greece. The CER
group works towards advanced and efficient methods for the recognition of
complex events in a multitude of large, heterogeneous and interdependent data
streams. Its re... | computer science |
21,413 | Reasoning in a Hierarchical System with Missing Group Size Information | cs.AI | The paper analyzes the problem of judgments or preferences subsequent to
initial analysis by autonomous agents in a hierarchical system where the higher
level agents does not have access to group size information. We propose methods
that reduce instances of preference reversal of the kind encountered in
Simpson's parad... | computer science |
21,414 | A New Multi Criteria Decision Making Method: Approach of Logarithmic
Concept (APLOCO) | cs.AI | The primary aim of the study is to introduce APLOCO method which is developed
for the solution of multicriteria decision making problems both theoretically
and practically. In this context, application subject of APLACO constitutes
evaluation of investment potential of different cities in metropolitan status
in Turkey.... | computer science |
21,415 | Blockchain and Artificial Intelligence | cs.AI | It is undeniable that artificial intelligence (AI) and blockchain concepts
are spreading at a phenomenal rate. Both technologies have distinct degree of
technological complexity and multi-dimensional business implications. However,
a common misunderstanding about blockchain concept, in particular, is that
blockchain is... | computer science |
21,416 | Learning Robust and Adaptive Real-World Continuous Control Using
Simulation and Transfer Learning | cs.AI | We use model-free reinforcement learning, extensive simulation, and transfer
learning to develop a continuous control algorithm that has good zero-shot
performance in a real physical environment. We train a simulated agent to act
optimally across a set of similar environments, each with dynamics drawn from a
prior dist... | computer science |
21,417 | Rebalancing Dockless Bike Sharing Systems | cs.AI | Bike sharing provides an environment-friendly way for traveling and is
booming all over the world. Yet, due to the high similarity of user travel
patterns, the bike imbalance problem constantly occurs, especially for dockless
bike sharing systems, causing significant impact on service quality and company
revenue. Thus,... | computer science |
21,418 | Story Generation and Aviation Incident Representation | cs.AI | This working note discusses the topic of story generation, with a view to
identifying the knowledge required to understand aviation incident narratives
(which have structural similarities to stories), following the premise that to
understand aviation incidents, one should at least be able to generate examples
of them. ... | computer science |
21,419 | Morphologic for knowledge dynamics: revision, fusion, abduction | cs.AI | Several tasks in artificial intelligence require to be able to find models
about knowledge dynamics. They include belief revision, fusion and belief
merging, and abduction. In this paper we exploit the algebraic framework of
mathematical morphology in the context of propositional logic, and define
operations such as di... | computer science |
21,420 | Who Killed Albert Einstein? From Open Data to Murder Mystery Games | cs.AI | This paper presents a framework for generating adventure games from open
data. Focusing on the murder mystery type of adventure games, the generator is
able to transform open data from Wikipedia articles, OpenStreetMap and images
from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves
around the murd... | computer science |
21,421 | From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in
the Style of Alpha(Go) Zero | cs.AI | Despite the recent successes of deep neural networks in various fields such
as image and speech recognition, natural language processing, and reinforcement
learning, we still face big challenges in bringing the power of numeric
optimization to symbolic reasoning. Researchers have proposed different avenues
such as neur... | computer science |
21,422 | Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal
Networks | cs.AI | A reliable modeling of uncertain evidence in Bayesian networks based on a
set-valued quantification is proposed. Both soft and virtual evidences are
considered. We show that evidence propagation in this setup can be reduced to
standard updating in an augmented credal network, equivalent to a set of
consistent Bayesian ... | computer science |
21,423 | An Anytime Algorithm for Task and Motion MDPs | cs.AI | Integrated task and motion planning has emerged as a challenging problem in
sequential decision making, where a robot needs to compute high-level strategy
and low-level motion plans for solving complex tasks. While high-level
strategies require decision making over longer time-horizons and scales, their
feasibility dep... | computer science |
21,424 | Detecting truth on components | cs.AI | We investigate and generalize to an extended framework the notion of 'true on
components' introduced by Zhou, Wang and Sun in their paper "Automated
Reducible Geometric Theorem Proving and Discovery by Gr\"obner Basis Method",
J. Automat. Reasoning 59 (3), 331-344, 2017. A new, simple criterion is
presented for a state... | computer science |
21,425 | Monte Carlo Q-learning for General Game Playing | cs.AI | Recently, the interest in reinforcement learning in game playing has been
renewed. This is evidenced by the groundbreaking results achieved by AlphaGo.
General Game Playing (GGP) provides a good testbed for reinforcement learning,
currently one of the hottest fields of AI. In GGP, a specification of games
rules is give... | computer science |
21,426 | Artificial intelligence and pediatrics: A synthetic mini review | cs.AI | The use of artificial intelligence intelligencein medicine can be traced back
to 1968 when Paycha published his paper Le diagnostic a l'aide d'intelligences
artificielle, presentation de la premiere machine diagnostri. Few years later
Shortliffe et al. presented an expert system named Mycin which was able to
identify b... | computer science |
21,427 | Implicit Robot-Human Communication in Adversarial and Collaborative
Environments | cs.AI | Users of AI systems may rely upon them to produce plans for achieving desired
objectives. Such AI systems should be able to compute obfuscated plans whose
execution in adversarial situations protects privacy as well as legible plans
which are easy for team-members to understand in collaborative situations. We
develop a... | computer science |
21,428 | HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under
Uncertainty | cs.AI | Planning under uncertainty is critical for robust robot performance in
uncertain, dynamic environments, but it incurs high computational cost.
State-of-the-art online search algorithms, such as DESPOT, have vastly improved
the computational efficiency of planning under uncertainty and made it a
valuable tool for roboti... | computer science |
21,429 | Large Neighborhood-Based Metaheuristic and Branch-and-Price for the
Pickup and Delivery Problem with Split Loads | cs.AI | We consider the multi-vehicle one-to-one pickup and delivery problem with
split loads, a NP-hard problem linked with a variety of applications for bulk
product transportation, bike-sharing systems and inventory re-balancing. This
problem is notoriously difficult due to the interaction of two challenging
vehicle routing... | computer science |
21,430 | A Machine Learning Approach to Air Traffic Route Choice Modelling | cs.AI | Air Traffic Flow and Capacity Management (ATFCM) is one of the constituent
parts of Air Traffic Management (ATM). The goal of ATFCM is to make airport and
airspace capacity meet traffic demand and, when capacity opportunities are
exhausted, optimise traffic flows to meet the available capacity. One of the
key enablers ... | computer science |
21,431 | Learning High-level Representations from Demonstrations | cs.AI | Hierarchical learning (HL) is key to solving complex sequential decision
problems with long horizons and sparse rewards. It allows learning agents to
break-up large problems into smaller, more manageable subtasks. A common
approach to HL, is to provide the agent with a number of high-level skills that
solve small parts... | computer science |
21,432 | Analysis of Cause-Effect Inference via Regression Errors | cs.AI | We address the problem of inferring the causal relation between two variables
by comparing the least-squares errors of the predictions in both possible
causal directions. Under the assumption of an independence between the function
relating cause and effect, the conditional noise distribution, and the
distribution of t... | computer science |
21,433 | The problem of the development ontology-driven architecture of
intellectual software systems | cs.AI | The paper describes the architecture of the intelligence system for automated
design of ontological knowledge bases of domain areas and the software model of
the management GUI (Graphical User Interface) subsystem | computer science |
21,434 | Design and software implementation of subsystems for creating and using
the ontological base of a research scientist | cs.AI | Creation of the information systems and tools for scientific research and
development support has always been one of the central directions of the
development of computer science. The main features of the modern evolution of
scientific research and development are the transdisciplinary approach and the
deep intellectua... | computer science |
21,435 | Technique for designing a domain ontology | cs.AI | The article describes the technique for designing a domain ontology, shows
the flowchart of algorithm design and example of constructing a fragment of the
ontology of the subject area of Computer Science is considered. | computer science |
21,436 | Integrated Tools for Engineering Ontologies | cs.AI | The article presents an overview of current specialized ontology engineering
tools, as well as texts' annotation tools based on ontologies. The main
functions and features of these tools, their advantages and disadvantages are
discussed. A systematic comparative analysis of means for engineering
ontologies is presented... | computer science |
21,437 | Principles of design and software development models of
ontological-driven computer systems | cs.AI | This paper describes the design principles of methodology of
knowledge-oriented information systems based on ontological approach. Such
systems implement technology subject-oriented extraction of knowledge from the
set of natural language texts and their formal and logical presentation and
application processing | computer science |
21,438 | Expert System for Diagnosis of Chest Diseases Using Neural Networks | cs.AI | This article represents one of the contemporary trends in the application of
the latest methods of information and communication technology for medicine
through an expert system helps the doctor to diagnose some chest diseases which
is important because of the frequent spread of chest diseases nowadays in
addition to t... | computer science |
21,439 | Automated Playtesting with Procedural Personas through MCTS with Evolved
Heuristics | cs.AI | This paper describes a method for generative player modeling and its
application to the automatic testing of game content using archetypal player
models called procedural personas. Theoretically grounded in psychological
decision theory, procedural personas are implemented using a variation of Monte
Carlo Tree Search (... | computer science |
21,440 | Superrational types | cs.AI | We present a formal analysis of Douglas Hofstadter's concept of
\emph{superrationality}. We start by defining superrationally justifiable
actions, and study them in symmetric games. We then model the beliefs of the
players, in a way that leads them to different choices than the usual
assumption of rationality by restri... | computer science |
21,441 | Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior
Explanations | cs.AI | There is a growing interest within the AI research community to develop
autonomous systems capable of explaining their behavior to users. One aspect of
the explanation generation problem that has yet to receive much attention is
the task of explaining plans to users whose level of expertise differ from that
of the expl... | computer science |
21,442 | Using Automatic Generation of Relaxation Constraints to Improve the
Preimage Attack on 39-step MD4 | cs.AI | In this paper we construct preimage attack on the truncated variant of the
MD4 hash function. Specifically, we study the MD4-39 function defined by the
first 39 steps of the MD4 algorithm. We suggest a new attack on MD4-39, which
develops the ideas proposed by H. Dobbertin in 1998. Namely, the special
relaxation constr... | computer science |
21,443 | Epistemic Graphs for Representing and Reasoning with Positive and
Negative Influences of Arguments | cs.AI | This paper introduces epistemic graphs as a generalization of the epistemic
approach to probabilistic argumentation. In these graphs, an argument can be
believed or disbelieved up to a given degree, thus providing a more
fine--grained alternative to the standard Dung's approaches when it comes to
determining the status... | computer science |
21,444 | Machine Theory of Mind | cs.AI | Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans'
ability to represent the mental states of others, including their desires,
beliefs, and intentions. We propose to train a machine to build such models
too. We design a Theory of Mind neural network -- a ToMnet -- which uses
meta-learning to build ... | computer science |
21,445 | Convergent Actor-Critic Algorithms Under Off-Policy Training and
Function Approximation | cs.AI | We present the first class of policy-gradient algorithms that work with both
state-value and policy function-approximation, and are guaranteed to converge
under off-policy training. Our solution targets problems in reinforcement
learning where the action representation adds to the-curse-of-dimensionality;
that is, with... | computer science |
21,446 | A Polynomial Time Subsumption Algorithm for Nominal Safe
$\mathcal{ELO}_\bot$ under Rational Closure | cs.AI | Description Logics (DLs) under Rational Closure (RC) is a well-known
framework for non-monotonic reasoning in DLs. In this paper, we address the
concept subsumption decision problem under RC for nominal safe
$\mathcal{ELO}_\bot$, a notable and practically important DL representative of
the OWL 2 profile OWL 2 EL.
Our... | computer science |
21,447 | On Looking for Local Expansion Invariants in Argumentation Semantics | cs.AI | We study invariant local expansion operators for conflict-free and admissible
sets in Abstract Argumentation Frameworks (AFs). Such operators are directly
applied on AFs, and are invariant with respect to a chosen "semantics" (that is
w.r.t. each of the conflict free/admissible set of arguments). Accordingly, we
derive... | computer science |
21,448 | Budget Constrained Bidding by Model-free Reinforcement Learning in
Display Advertising | cs.AI | Real-time bidding (RTB) is almost the most important mechanism in online
display advertising, where proper bid for each page view plays a vital and
essential role for good marketing results. Budget constrained bidding is a
typical scenario in RTB mechanism where the advertisers hope to maximize total
value of winning i... | computer science |
21,449 | A Matrix Approach for Weighted Argumentation Frameworks: a Preliminary
Report | cs.AI | The assignment of weights to attacks in a classical Argumentation Framework
allows to compute semantics by taking into account the different importance of
each argument. We represent a Weighted Argumentation Framework by a non-binary
matrix, and we characterize the basic extensions (such as w-admissible, w-
stable, w-c... | computer science |
21,450 | Optimal Stochastic Delivery Planning in Full-Truckload and
Less-Than-Truckload Delivery | cs.AI | With an increasing demand from emerging logistics businesses, Vehicle Routing
Problem with Private fleet and common Carrier (VRPPC) has been introduced to
manage package delivery services from a supplier to customers. However, almost
all of existing studies focus on the deterministic problem that assumes all
parameters... | computer science |
21,451 | Semantic Vector Spaces for Broadening Consideration of Consequences | cs.AI | Reasoning systems with too simple a model of the world and human intent are
unable to consider potential negative side effects of their actions and modify
their plans to avoid them (e.g., avoiding potential errors). However,
hand-encoding the enormous and subtle body of facts that constitutes common
sense into a knowle... | computer science |
21,452 | Reinforcement Learning on Web Interfaces Using Workflow-Guided
Exploration | cs.AI | Reinforcement learning (RL) agents improve through trial-and-error, but when
reward is sparse and the agent cannot discover successful action sequences,
learning stagnates. This has been a notable problem in training deep RL agents
to perform web-based tasks, such as booking flights or replying to emails,
where a singl... | computer science |
21,453 | PSO-based Fuzzy Markup Language for Student Learning Performance
Evaluation and Educational Application | cs.AI | This paper proposes an agent with particle swarm optimization (PSO) based on
a Fuzzy Markup Language (FML) for students learning performance evaluation and
educational applications, and the proposed agent is according to the response
data from a conventional test and an item response theory. First, we apply a
GS-based ... | computer science |
21,454 | One Big Net For Everything | cs.AI | I apply recent work on "learning to think" (2015) and on PowerPlay (2011) to
the incremental training of an increasingly general problem solver, continually
learning to solve new tasks without forgetting previous skills. The problem
solver is a single recurrent neural network (or similar general purpose
computer) calle... | computer science |
21,455 | Prototyping Virtual Reality Serious Games for Building Earthquake
Preparedness: The Auckland City Hospital Case Study | cs.AI | Enhancing evacuee safety is a key factor in reducing the number of injuries
and deaths that result from earthquakes. One way this can be achieved is by
training occupants. Virtual Reality (VR) and Serious Games (SGs), represent
novel techniques that may overcome the limitations of traditional training
approaches. VR an... | computer science |
21,456 | Antifragility for Intelligent Autonomous Systems | cs.AI | Antifragile systems grow measurably better in the presence of hazards. This
is in contrast to fragile systems which break down in the presence of hazards,
robust systems that tolerate hazards up to a certain degree, and resilient
systems that -- like self-healing systems -- revert to their earlier expected
behavior aft... | computer science |
21,457 | A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human
to Autonomous Eliciting Agents | cs.AI | This paper offers a multi-disciplinary review of knowledge acquisition
methods in human activity systems. The review captures the degree of
involvement of various types of agencies in the knowledge acquisition process,
and proposes a classification with three categories of methods: the human
agent, the human-inspired a... | computer science |
21,458 | Human-in-the-Loop Synthesis for Partially Observable Markov Decision
Processes | cs.AI | We study planning problems where autonomous agents operate inside
environments that are subject to uncertainties and not fully observable.
Partially observable Markov decision processes (POMDPs) are a natural formal
model to capture such problems. Because of the potentially huge or even
infinite belief space in POMDPs,... | computer science |
21,459 | Introduction to the SP theory of intelligence | cs.AI | This article provides a brief introduction to the "Theory of Intelligence"
and its realisation in the "SP Computer Model". The overall goal of the SP
programme of research, in accordance with long-established principles in
science, has been the simplification and integration of observations and
concepts across artifici... | computer science |
21,460 | Domain Modelling in Computational Persuasion for Behaviour Change in
Healthcare | cs.AI | The aim of behaviour change is to help people to change aspects of their
behaviour for the better (e.g., to decrease calorie intake, to drink in
moderation, to take more exercise, to complete a course of antibiotics once
started, etc.). In current persuasion technology for behaviour change, the
emphasis is on helping p... | computer science |
21,461 | Selective Experience Replay for Lifelong Learning | cs.AI | Deep reinforcement learning has emerged as a powerful tool for a variety of
learning tasks, however deep nets typically exhibit forgetting when learning
multiple tasks in sequence. To mitigate forgetting, we propose an experience
replay process that augments the standard FIFO buffer and selectively stores
experiences i... | computer science |
21,462 | General Video Game AI: a Multi-Track Framework for Evaluating Agents,
Games and Content Generation Algorithms | cs.AI | General Video Game Playing (GVGP) aims at designing an agent that is capable
of playing multiple video games with no human intervention. In 2014, The
General Video Game AI (GVGAI) competition framework was created and released
with the purpose of providing researchers a common open-source and easy to use
platform for t... | computer science |
21,463 | Deep Reinforcement Learning for Sponsored Search Real-time Bidding | cs.AI | Bidding optimization is one of the most critical problems in online
advertising. Sponsored search (SS) auction, due to the randomness of user query
behavior and platform nature, usually adopts keyword-level bidding strategies.
In contrast, the display advertising (DA), as a relatively simpler scenario for
auction, has ... | computer science |
21,464 | Composable Planning with Attributes | cs.AI | The tasks that an agent will need to solve often are not known during
training. However, if the agent knows which properties of the environment are
important then, after learning how its actions affect those properties, it may
be able to use this knowledge to solve complex tasks without training
specifically for them. ... | computer science |
21,465 | Knowledge Base Relation Detection via Multi-View Matching | cs.AI | Relation detection is a core component for Knowledge Base Question Answering
(KBQA). In this paper, we propose a KB relation detection model via multi-view
matching which utilizes more useful information extracted from question and KB.
The matching inside each view is through multiple perspectives to compare two
input ... | computer science |
21,466 | Estimating Total Search Space Size for Specific Piece Sets in Chess | cs.AI | Automatic chess problem or puzzle composition typically involves generating
and testing various different positions, sometimes using particular piece sets.
Once a position has been generated, it is then usually tested for positional
legality based on the game rules. However, it is useful to be able to estimate
what the... | computer science |
21,467 | Multi-Agent Imitation Learning for Driving Simulation | cs.AI | Simulation is an appealing option for validating the safety of autonomous
vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been
shown to learn representative human driver models. These human driver models
were learned through training in single-agent environments, but they have
difficulty in gene... | computer science |
21,468 | Analyzing Business Process Anomalies Using Autoencoders | cs.AI | Businesses are naturally interested in detecting anomalies in their internal
processes, because these can be indicators for fraud and inefficiencies. Within
the domain of business intelligence, classic anomaly detection is not very
frequently researched. In this paper, we propose a method, using autoencoders,
for detec... | computer science |
21,469 | An Interactive Many Objective Evolutionary Algorithm with Cascade
Clustering and Reference Point Incremental Learning | cs.AI | Researches have shown difficulties in obtaining proximity while maintaining
diversity for solving many-objective optimization problems (MaOPs). The
complexities of the true Pareto Front (PF) also pose serious challenges for the
pervasive algorithms for their insufficient ability to adapt to the
characteristics of the t... | computer science |
21,470 | Some Considerations on Learning to Explore via Meta-Reinforcement
Learning | cs.AI | We consider the problem of exploration in meta reinforcement learning. Two
new meta reinforcement learning algorithms are suggested: E-MAML and
E-$\text{RL}^2$. Results are presented on a novel environment we call `Krazy
World' and a set of maze environments. We show E-MAML and E-$\text{RL}^2$
deliver better performanc... | computer science |
21,471 | A Swift Heuristic Method for Work Order Scheduling under the
Skilled-Workforce Constraint | cs.AI | The considered problem is how to optimally allocate a set of jobs to
technicians of different skills such that the number of technicians of each
skill does not exceed the number of persons with that skill designation. The
key motivation is the quick sensitivity analysis in terms of the workforce size
which is quite nec... | computer science |
21,472 | Exploring Novel Game Spaces with Fluidic Games | cs.AI | With the growing integration of smartphones into our daily lives, and their
increased ease of use, mobile games have become highly popular across all
demographics. People listen to music, play games or read the news while in
transit or bridging gap times. While mobile gaming is gaining popularity,
mobile expression of ... | computer science |
21,473 | A real-time rule-based system for bridge management based on CART
decision tree and SMO algorithms | cs.AI | To real-time management of the bridges under dynamic conditions, this paper
develops a rule-based decision support framework to extract the necessary rules
from simulation results made by Aimsun. In this rule-based system, the
supervised and the unsupervised learning algorithms are applied to generalize
the rules where... | computer science |
21,474 | Explanatory relations in arbitrary logics based on satisfaction systems,
cutting and retraction | cs.AI | The aim of this paper is to introduce a new framework for defining abductive
reasoning operators based on a notion of retraction in arbitrary logics defined
as satisfaction systems. We show how this framework leads to the design of
explanatory relations satisfying properties of abductive reasoning, and discuss
its appl... | computer science |
21,475 | A Genetic Programming Framework for 2D Platform AI | cs.AI | There currently exists a wide range of techniques to model and evolve
artificial players for games. Existing techniques range from black box neural
networks to entirely hand-designed solutions. In this paper, we demonstrate the
feasibility of a genetic programming framework using human controller input to
derive meanin... | computer science |
21,476 | Memory Search and Sense from Shallow Hierarchies | cs.AI | This paper describes an automatic process for combining patterns and
features, to guide a search process and reason about it. It is based on the
functionality that a human brain might have, which is a highly distributed
network of simple neuronal components that can apply some level of matching and
cross-referencing ov... | computer science |
21,477 | Intent-aware Multi-agent Reinforcement Learning | cs.AI | This paper proposes an intent-aware multi-agent planning framework as well as
a learning algorithm. Under this framework, an agent plans in the goal space to
maximize the expected utility. The planning process takes the belief of other
agents' intents into consideration. Instead of formulating the learning problem
as a... | computer science |
21,478 | Discovering Underlying Plans Based on Shallow Models | cs.AI | Plan recognition aims to discover target plans (i.e., sequences of actions)
behind observed actions, with history plan libraries or domain models in hand.
Previous approaches either discover plans by maximally "matching" observed
actions to plan libraries, assuming target plans are from plan libraries, or
infer plans b... | computer science |
21,479 | Decision-making processes in the Cognitive Theory of True Conditions | cs.AI | The Cognitive Theory of True Conditions (CTTC) is a proposal to design the
implementation of cognitive abilities and to describe the model-theoretic
semantics of symbolic cognitive architectures. The CTTC is formulated
mathematically using the multi-optional many-sorted past present future(MMPPF)
structures. This artic... | computer science |
21,480 | OntoWind: An Improved and Extended Wind Energy Ontology | cs.AI | Ontologies are critical sources of semantic information for many application
domains. Hence, there are ontologies proposed and utilized for domains such as
medicine, chemical engineering, and electrical energy. In this paper, we
present an improved and extended version of a wind energy ontology previously
proposed. Fir... | computer science |
21,481 | A Brandom-ian view of Reinforcement Learning towards strong-AI | cs.AI | The analytic philosophy of Robert Brandom, based on the ideas of pragmatism,
paints a picture of sapience, through inferentialism. In this paper, we present
a theory, that utilizes essential elements of Brandom's philosophy, towards the
objective of achieving strong-AI. We do this by connecting the constitutive
element... | computer science |
21,482 | SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially
Optimal Outcomes | cs.AI | In multiagent environments, the capability of learning is important for an
agent to behave appropriately in face of unknown opponents and dynamic
environment. From the system designer's perspective, it is desirable if the
agents can learn to coordinate towards socially optimal outcomes, while also
avoiding being exploi... | computer science |
21,483 | Compositional Attention Networks for Machine Reasoning | cs.AI | We present the MAC network, a novel fully differentiable neural network
architecture, designed to facilitate explicit and expressive reasoning. Drawing
inspiration from first principles of computer organization, MAC moves away from
monolithic black-box neural architectures towards a design that encourages both
transpar... | computer science |
21,484 | Concise Fuzzy Representation of Big Graphs: a Dimensionality Reduction
Approach | cs.AI | The enormous amount of data to be represented using large graphs exceeds in
some cases the resources of a conventional computer. Edges in particular can
take up a considerable amount of memory as compared to the number of nodes.
However, rigorous edge storage might not always be essential to be able to draw
the needed ... | computer science |
21,485 | Institutional Metaphors for Designing Large-Scale Distributed AI versus
AI Techniques for Running Institutions | cs.AI | Artificial Intelligence (AI) started out with an ambition to reproduce the
human mind, but, as the sheer scale of that ambition became apparent, quickly
retreated into either studying specialized intelligent behaviours, or proposing
overarching architectural concepts for interfacing specialized intelligent
behaviour co... | computer science |
21,486 | Highly Automated Learning for Improved Active Safety of Vulnerable Road
Users | cs.AI | Highly automated driving requires precise models of traffic participants.
Many state of the art models are currently based on machine learning
techniques. Among others, the required amount of labeled data is one major
challenge. An autonomous learning process addressing this problem is proposed.
The initial models are ... | computer science |
21,487 | Learning and analyzing vector encoding of symbolic representations | cs.AI | We present a formal language with expressions denoting general symbol
structures and queries which access information in those structures. A
sequence-to-sequence network processing this language learns to encode symbol
structures and query them. The learned representation (approximately) shares a
simple linearity prope... | computer science |
21,488 | Intelligible Artificial Intelligence | cs.AI | Since Artificial Intelligence (AI) software uses techniques like deep
lookahead search and stochastic optimization of huge neural networks to fit
mammoth datasets, it often results in complex behavior that is difficult for
people to understand. Yet organizations are deploying AI algorithms in many
mission-critical sett... | computer science |
21,489 | On the Algebra in Boole's Laws of Thought | cs.AI | This article explores the ideas that went into George Boole's development of
an algebra for logical inference in his book The Laws of Thought. We explore in
particular his wife Mary Boole's claim that he was deeply influenced by Indian
logic and argue that his work was more than a framework for processing
propositions.... | computer science |
21,490 | Solving the Course-timetabling Problem of Cairo University Using Max-SAT | cs.AI | Due to the good performance of current SAT (satisfiability) and Max-SAT
(maximum ssatisfiability) solvers, many real-life optimization problems such as
scheduling can be solved by encoding them into Max-SAT. In this paper we tackle
the course timetabling problem of the department of mathematics, Cairo
University by enc... | computer science |
21,491 | Fractal AI: A fragile theory of intelligence | cs.AI | Fractal AI is a theory for general artificial intelligence. It allows to
derive new mathematical tools that constitute the foundations for a new kind of
stochastic calculus, by modelling information using cellular automaton-like
structures instead of smooth functions.
In the repository included we are presenting a ne... | computer science |
21,492 | The 2017 AIBIRDS Competition | cs.AI | This paper presents an overview of the sixth AIBIRDS competition, held at the
26th International Joint Conference on Artificial Intelligence. This
competition tasked participants with developing an intelligent agent which can
play the physics-based puzzle game Angry Birds. This game uses a sophisticated
physics engine ... | computer science |
21,493 | A Study of Car-to-Train Assignment Problem for Rail Express Cargos on
Scheduled and Unscheduled Train Service Network | cs.AI | Freight train services in a railway network system are generally divided into
two categories: one is the unscheduled train, whose operating frequency
fluctuates with origin-destination (OD) demands; the other is the scheduled
train, which is running based on regular timetable just like the passenger
trains. The timetab... | computer science |
21,494 | A New Result on the Complexity of Heuristic Estimates for the A*
Algorithm | cs.AI | Relaxed models are abstract problem descriptions generated by ignoring
constraints that are present in base-level problems. They play an important
role in planning and search algorithms, as it has been shown that the length of
an optimal solution to a relaxed model yields a monotone heuristic for an A?
search of a base... | computer science |
21,495 | Automated Curriculum Learning by Rewarding Temporally Rare Events | cs.AI | Reward shaping allows reinforcement learning (RL) agents to accelerate
learning by receiving additional reward signals. However, these signals can be
difficult to design manually, especially for complex RL tasks. We propose a
simple and general approach that determines the reward of pre-defined events by
their rarity a... | computer science |
21,496 | Lasso type classifiers with a reject option | stat.ML | We consider the problem of binary classification where one can, for a
particular cost, choose not to classify an observation. We present a simple
proof for the oracle inequality for the excess risk of structural risk
minimizers using a lasso type penalty. | computer science |
21,497 | Metric Embedding for Nearest Neighbor Classification | stat.ML | The distance metric plays an important role in nearest neighbor (NN)
classification. Usually the Euclidean distance metric is assumed or a
Mahalanobis distance metric is optimized to improve the NN performance. In this
paper, we study the problem of embedding arbitrary metric spaces into a
Euclidean space with the goal... | computer science |
21,498 | Degenerating families of dendrograms | stat.ML | Dendrograms used in data analysis are ultrametric spaces, hence objects of
nonarchimedean geometry. It is known that there exist $p$-adic representation
of dendrograms. Completed by a point at infinity, they can be viewed as
subtrees of the Bruhat-Tits tree associated to the $p$-adic projective line.
The implications a... | computer science |
21,499 | Families of dendrograms | stat.ML | A conceptual framework for cluster analysis from the viewpoint of p-adic
geometry is introduced by describing the space of all dendrograms for n
datapoints and relating it to the moduli space of p-adic Riemannian spheres
with punctures using a method recently applied by Murtagh (2004b). This method
embeds a dendrogram ... | computer science |
21,500 | Online Learning in Discrete Hidden Markov Models | stat.ML | We present and analyse three online algorithms for learning in discrete
Hidden Markov Models (HMMs) and compare them with the Baldi-Chauvin Algorithm.
Using the Kullback-Leibler divergence as a measure of generalisation error we
draw learning curves in simplified situations. The performance for learning
drifting concep... | computer science |
21,501 | Supervised Machine Learning with a Novel Kernel Density Estimator | stat.ML | In recent years, kernel density estimation has been exploited by computer
scientists to model machine learning problems. The kernel density estimation
based approaches are of interest due to the low time complexity of either O(n)
or O(n*log(n)) for constructing a classifier, where n is the number of sampling
instances.... | computer science |
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