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21,202 | Exploration in Feature Space for Reinforcement Learning | cs.AI | The infamous exploration-exploitation dilemma is one of the oldest and most
important problems in reinforcement learning (RL). Deliberate and effective
exploration is necessary for RL agents to succeed in most environments.
However, until very recently even very sophisticated RL algorithms employed
simple, undirected e... | computer science |
21,203 | Performance Prediction and Optimization of Solar Water Heater via a
Knowledge-Based Machine Learning Method | cs.AI | Measuring the performance of solar energy and heat transfer systems requires
a lot of time, economic cost and manpower. Meanwhile, directly predicting their
performance is challenging due to the complicated internal structures.
Fortunately, a knowledge-based machine learning method can provide a promising
prediction an... | computer science |
21,204 | Can Machines Think in Radio Language? | cs.AI | People can think in auditory, visual and tactile forms of language, so can
machines principally. But is it possible for them to think in radio language?
According to a first principle presented for general intelligence, i.e. the
principle of language's relativity, the answer may give an exceptional solution
for robot a... | computer science |
21,205 | Interactive Learning of State Representation through Natural Language
Instruction and Explanation | cs.AI | One significant simplification in most previous work on robot learning is the
closed-world assumption where the robot is assumed to know ahead of time a
complete set of predicates describing the state of the physical world. However,
robots are not likely to have a complete model of the world especially when
learning a ... | computer science |
21,206 | Recurrent Network-based Deterministic Policy Gradient for Solving
Bipedal Walking Challenge on Rugged Terrains | cs.AI | This paper presents the learning algorithm based on the Recurrent
Network-based Deterministic Policy Gradient. The Long-Short Term Memory is
utilized to enable the Partially Observed Markov Decision Process framework.
The novelty are improvements of LSTM networks: update of multi-step temporal
difference, removal of ba... | computer science |
21,207 | MSC: A Dataset for Macro-Management in StarCraft II | cs.AI | Macro-management is an important problem in StarCraft, which has been studied
for a long time. Various datasets together with assorted methods have been
proposed in the last few years. But these datasets have some defects for
boosting the academic and industrial research: 1) There're neither standard
preprocessing, par... | computer science |
21,208 | Causality and Temporal Dependencies in the Design of Fault Management
Systems | cs.AI | Reasoning about causes and effects naturally arises in the engineering of
safety-critical systems. A classical example is Fault Tree Analysis, a
deductive technique used for system safety assessment, whereby an undesired
state is reduced to the set of its immediate causes. The design of fault
management systems also re... | computer science |
21,209 | Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human
Motion Analysis | cs.AI | This work handles the inverse reinforcement learning (IRL) problem where only
a small number of demonstrations are available from a demonstrator for each
high-dimensional task, insufficient to estimate an accurate reward function.
Observing that each demonstrator has an inherent reward for each state and the
task-speci... | computer science |
21,210 | Emergent Complexity via Multi-Agent Competition | cs.AI | Reinforcement learning algorithms can train agents that solve problems in
complex, interesting environments. Normally, the complexity of the trained
agent is closely related to the complexity of the environment. This suggests
that a highly capable agent requires a complex environment for training. In
this paper, we poi... | computer science |
21,211 | Deep Reinforcement Learning: Framework, Applications, and Embedded
Implementations | cs.AI | The recent breakthroughs of deep reinforcement learning (DRL) technique in
Alpha Go and playing Atari have set a good example in handling large state and
actions spaces of complicated control problems. The DRL technique is comprised
of (i) an offline deep neural network (DNN) construction phase, which derives
the corre... | computer science |
21,212 | PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement
Learning and Sampling-based Planning | cs.AI | We present PRM-RL, a hierarchical method for long-range navigation task
completion that combines sampling-based path planning with reinforcement
learning (RL) agents. The RL agents learn short-range, point-to-point
navigation policies that capture robot dynamics and task constraints without
knowledge of the large-scale... | computer science |
21,213 | Neural Program Meta-Induction | cs.AI | Most recently proposed methods for Neural Program Induction work under the
assumption of having a large set of input/output (I/O) examples for learning
any underlying input-output mapping. This paper aims to address the problem of
data and computation efficiency of program induction by leveraging information
from relat... | computer science |
21,214 | Counterfactual Conditionals in Quantified Modal Logic | cs.AI | We present a novel formalization of counterfactual conditionals in a
quantified modal logic. Counterfactual conditionals play a vital role in
ethical and moral reasoning. Prior work has shown that moral reasoning systems
(and more generally, theory-of-mind reasoning systems) should be at least as
expressive as first-or... | computer science |
21,215 | Explaining Trained Neural Networks with Semantic Web Technologies: First
Steps | cs.AI | The ever increasing prevalence of publicly available structured data on the
World Wide Web enables new applications in a variety of domains. In this paper,
we provide a conceptual approach that leverages such data in order to explain
the input-output behavior of trained artificial neural networks. We apply
existing Sem... | computer science |
21,216 | Combinatorial Multi-armed Bandits for Real-Time Strategy Games | cs.AI | Games with large branching factors pose a significant challenge for game tree
search algorithms. In this paper, we address this problem with a sampling
strategy for Monte Carlo Tree Search (MCTS) algorithms called {\em na\"{i}ve
sampling}, based on a variant of the Multi-armed Bandit problem called {\em
Combinatorial M... | computer science |
21,217 | Functional Decision Theory: A New Theory of Instrumental Rationality | cs.AI | This paper describes and motivates a new decision theory known as functional
decision theory (FDT), as distinct from causal decision theory and evidential
decision theory. Functional decision theorists hold that the normative
principle for action is to treat one's decision as the output of a fixed
mathematical function... | computer science |
21,218 | Network Model Selection Using Task-Focused Minimum Description Length | cs.AI | Networks are fundamental models for data used in practically every
application domain. In most instances, several implicit or explicit choices
about the network definition impact the translation of underlying data to a
network representation, and the subsequent question(s) about the underlying
system being represented.... | computer science |
21,219 | Causal Rule Sets for Identifying Subgroups with Enhanced Treatment
Effect | cs.AI | We introduce a novel generative model for interpretable subgroup analysis for
causal inference applications, Causal Rule Sets (CRS). A CRS model uses a small
set of short rules to capture a subgroup where the average treatment effect is
elevated compared to the entire population. We present a Bayesian framework for
lea... | computer science |
21,220 | Intention-Net: Integrating Planning and Deep Learning for Goal-Directed
Autonomous Navigation | cs.AI | How can a delivery robot navigate reliably to a destination in a new office
building, with minimal prior information? To tackle this challenge, this paper
introduces a two-level hierarchical approach, which integrates model-free deep
learning and model-based path planning. At the low level, a neural-network
motion cont... | computer science |
21,221 | Characterizing Driving Context from Driver Behavior | cs.AI | Because of the increasing availability of spatiotemporal data, a variety of
data-analytic applications have become possible. Characterizing driving
context, where context may be thought of as a combination of location and time,
is a new challenging application. An example of such a characterization is
finding the corre... | computer science |
21,222 | Decision Trees for Helpdesk Advisor Graphs | cs.AI | We use decision trees to build a helpdesk agent reference network to
facilitate the on-the-job advising of junior or less experienced staff on how
to better address telecommunication customer fault reports. Such reports
generate field measurements and remote measurements which, when coupled with
location data and clien... | computer science |
21,223 | On Using Linear Diophantine Equations to Tune the extent of Look Ahead
while Hiding Decision Tree Rules | cs.AI | This paper focuses on preserving the privacy of sensitive pat-terns when
inducing decision trees. We adopt a record aug-mentation approach for hiding
sensitive classification rules in binary datasets. Such a hiding methodology is
preferred over other heuristic solutions like output perturbation or
crypto-graphic techni... | computer science |
21,224 | Go game formal revealing by Ising model | cs.AI | Go gaming is a struggle for territory control between rival, black and white,
stones on a board. We model the Go dynamics in a game by means of the Ising
model whose interaction coefficients reflect essential rules and tactics
employed in Go to build long-term strategies. At any step of the game, the
energy functional ... | computer science |
21,225 | Safety-Aware Apprenticeship Learning | cs.AI | Apprenticeship learning (AL) is a class of "learning from demonstrations"
techniques where the reward function of a Markov Decision Process (MDP) is
unknown to the learning agent and the agent has to derive a good policy by
observing an expert's demonstrations. In this paper, we study the problem of
how to make AL algo... | computer science |
21,226 | Human-in-the-loop Artificial Intelligence | cs.AI | Little by little, newspapers are revealing the bright future that Artificial
Intelligence (AI) is building. Intelligent machines will help everywhere.
However, this bright future has a dark side: a dramatic job market contraction
before its unpredictable transformation. Hence, in a near future, large numbers
of job see... | computer science |
21,227 | FashionBrain Project: A Vision for Understanding Europe's Fashion Data
Universe | cs.AI | A core business in the fashion industry is the understanding and prediction
of customer needs and trends. Search engines and social networks are at the
same time a fundamental bridge and a costly middleman between the customer's
purchase intention and the retailer. To better exploit Europe's distinctive
characteristics... | computer science |
21,228 | Enhancements of linked data expressiveness for ontologies | cs.AI | The semantic web has received many contributions of researchers as ontologies
which, in this context, i.e. within RDF linked data, are formalized
conceptualizations that might use different protocols, such as RDFS, OWL DL and
OWL FULL. In this article, we describe new expressive techniques which were
found necessary af... | computer science |
21,229 | On modeling vagueness and uncertainty in data-to-text systems through
fuzzy sets | cs.AI | Vagueness and uncertainty management is counted among one of the challenges
that remain unresolved in systems that generate texts from non-linguistic data,
known as data-to-text systems. In the last decade, work in fuzzy linguistic
summarization and description of data has raised the interest of using fuzzy
sets to mod... | computer science |
21,230 | An efficient SAT formulation for learning multiple criteria
non-compensatory sorting rules from examples | cs.AI | The literature on Multiple Criteria Decision Analysis (MCDA) proposes several
methods in order to sort alternatives evaluated on several attributes into
ordered classes. Non Compensatory Sorting models (NCS) assign alternatives to
classes based on the way they compare to multicriteria profiles separating the
consecutiv... | computer science |
21,231 | Towards a new paradigm for assistive technology at home: research
challenges, design issues and performance assessment | cs.AI | Providing elderly and people with special needs, including those suffering
from physical disabilities and chronic diseases, with the possibility of
retaining their independence at best is one of the most important challenges
our society is expected to face. Assistance models based on the home care
paradigm are being ad... | computer science |
21,232 | Partial Knowledge In Embeddings | cs.AI | Representing domain knowledge is crucial for any task. There has been a wide
range of techniques developed to represent this knowledge, from older logic
based approaches to the more recent deep learning based techniques (i.e.
embeddings). In this paper, we discuss some of these methods, focusing on the
representational... | computer science |
21,233 | Abnormal Spatial-Temporal Pattern Analysis for Niagara Frontier Border
Wait Times | cs.AI | Border crossing delays cause problems like huge economics loss and heavy
environmental pollutions. To understand more about the nature of border
crossing delay, this study applies a dictionary-based compression algorithm to
process the historical Niagara Frontier border wait times data. It can identify
the abnormal spa... | computer science |
21,234 | Automata Guided Hierarchical Reinforcement Learning for Zero-shot Skill
Composition | cs.AI | An obstacle that prevents the wide adoption of (deep) reinforcement learning
(RL) in control systems is its need for a large amount of interactions with the
environ- ment in order to master a skill. The learned skill usually generalizes
poorly across domains and re-training is often necessary when presented with a
new ... | computer science |
21,235 | Visualizing and Understanding Atari Agents | cs.AI | Deep reinforcement learning (deep RL) agents have achieved remarkable success
in a broad range of game-playing and continuous control tasks. While these
agents are effective at maximizing rewards, it is often unclear what strategies
they use to do so. In this paper, we take a step toward explaining deep RL
agents throu... | computer science |
21,236 | Erratum: Link prediction in drug-target interactions network using
similarity indices | cs.AI | Background: In silico drug-target interaction (DTI) prediction plays an
integral role in drug repositioning: the discovery of new uses for existing
drugs. One popular method of drug repositioning is network-based DTI
prediction, which uses complex network theory to predict DTIs from a
drug-target network. Currently, mo... | computer science |
21,237 | Servant of Many Masters: Shifting priorities in Pareto-optimal
sequential decision-making | cs.AI | It is often argued that an agent making decisions on behalf of two or more
principals who have different utility functions should adopt a {\em
Pareto-optimal} policy, i.e., a policy that cannot be improved upon for one
agent without making sacrifices for another. A famous theorem of Harsanyi shows
that, when the princi... | computer science |
21,238 | Counterfactual Explanations without Opening the Black Box: Automated
Decisions and the GDPR | cs.AI | There has been much discussion of the right to explanation in the EU General
Data Protection Regulation, and its existence, merits, and disadvantages.
Implementing a right to explanation that opens the black box of algorithmic
decision-making faces major legal and technical barriers. Explaining the
functionality of com... | computer science |
21,239 | Interpretable and Pedagogical Examples | cs.AI | Teachers intentionally pick the most informative examples to show their
students. However, if the teacher and student are neural networks, the examples
that the teacher network learns to give, although effective at teaching the
student, are typically uninterpretable. We show that training the student and
teacher iterat... | computer science |
21,240 | Weight-Based Variable Ordering in the Context of High-Level
Consistencies | cs.AI | Dom/wdeg is one of the best performing heuristics for dynamic variable
ordering in backtrack search [Boussemart et al., 2004]. As originally defined,
this heuristic increments the weight of the constraint that causes a domain
wipeout (i.e., a dead-end) when enforcing arc consistency during search. "The
process of weigh... | computer science |
21,241 | Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep
Reinforcement Learning | cs.AI | Rather than learning new control policies for each new task, it is possible,
when tasks share some structure, to compose a "meta-policy" from previously
learned policies. This paper reports results from experiments using Deep
Reinforcement Learning on a continuous-state, discrete-action autonomous
driving simulator. We... | computer science |
21,242 | Semantic Web Today: From Oil Rigs to Panama Papers | cs.AI | The next leap on the internet has already started as Semantic Web. At its
core, Semantic Web transforms the document oriented web to a data oriented web
enriched with semantics embedded as metadata. This change in perspective
towards the web offers numerous benefits for vast amount of data intensive
industries that are... | computer science |
21,243 | Exploration in NetHack with Secret Discovery | cs.AI | Roguelike games generally feature exploration problems as a critical, yet
often repetitive element of gameplay. Automated approaches, however, face
challenges in terms of optimality, as well as due to incomplete information,
such as from the presence of secret doors. This paper presents an algorithmic
approach to explo... | computer science |
21,244 | CogSciK: Clustering for Cognitive Science Motivated Decision Making | cs.AI | Computational models of decisionmaking must contend with the variance of
context and any number of possible decisions that a defined strategic actor can
make at a given time. Relying on cognitive science theory, the authors have
created an algorithm that captures the orientation of the actor towards an
object and array... | computer science |
21,245 | Discovering Representative Examples for Program Synthesis | cs.AI | Program synthesis is a class of regression problems where one seeks a
solution, in the form of a source-code program, mapping the inputs to their
corresponding outputs exactly. Due to its precise and combinatorial nature,
program synthesis is commonly formulated as a constraint satisfaction problem,
where input-output ... | computer science |
21,246 | Repairing Ontologies via Axiom Weakening | cs.AI | Ontology engineering is a hard and error-prone task, in which small changes
may lead to errors, or even produce an inconsistent ontology. As ontologies
grow in size, the need for automated methods for repairing inconsistencies
while preserving as much of the original knowledge as possible increases. Most
previous appro... | computer science |
21,247 | First Results from Using Game Refinement Measure and Learning
Coefficient in Scrabble | cs.AI | This paper explores the entertainment experience and learning experience in
Scrabble. It proposes a new measure from the educational point of view, which
we call learning coefficient, based on the balance between the learner's skill
and the challenge in Scrabble. Scrabble variants, generated using different
size of boa... | computer science |
21,248 | Learning with Options that Terminate Off-Policy | cs.AI | A temporally abstract action, or an option, is specified by a policy and a
termination condition: the policy guides option behavior, and the termination
condition roughly determines its length. Generally, learning with longer
options (like learning with multi-step returns) is known to be more efficient.
However, if the... | computer science |
21,249 | Neural-Symbolic Learning and Reasoning: A Survey and Interpretation | cs.AI | The study and understanding of human behaviour is relevant to computer
science, artificial intelligence, neural computation, cognitive science,
philosophy, psychology, and several other areas. Presupposing cognition as
basis of behaviour, among the most prominent tools in the modelling of
behaviour are computational-lo... | computer science |
21,250 | Self-Regulating Artificial General Intelligence | cs.AI | Here we examine the paperclip apocalypse concern for artificial general
intelligence (or AGI) whereby a superintelligent AI with a simple goal (ie.,
producing paperclips) accumulates power so that all resources are devoted
towards that simple goal and are unavailable for any other use. We provide
conditions under which... | computer science |
21,251 | Learning Abduction under Partial Observability | cs.AI | Juba recently proposed a formulation of learning abductive reasoning from
examples, in which both the relative plausibility of various explanations, as
well as which explanations are valid, are learned directly from data. The main
shortcoming of this formulation of the task is that it assumes access to
full-information... | computer science |
21,252 | Prediction Under Uncertainty with Error-Encoding Networks | cs.AI | In this work we introduce a new framework for performing temporal predictions
in the presence of uncertainty. It is based on a simple idea of disentangling
components of the future state which are predictable from those which are
inherently unpredictable, and encoding the unpredictable components into a
low-dimensional... | computer science |
21,253 | An Empirical Study of the Effects of Spurious Transitions on
Abstraction-based Heuristics | cs.AI | The efficient solution of state space search problems is often attempted by
guiding search algorithms with heuristics (estimates of the distance from any
state to the goal). A popular way for creating heuristic functions is by using
an abstract version of the state space. However, the quality of
abstraction-based heuri... | computer science |
21,254 | Tree Projections and Constraint Optimization Problems: Fixed-Parameter
Tractability and Parallel Algorithms | cs.AI | Tree projections provide a unifying framework to deal with most structural
decomposition methods of constraint satisfaction problems (CSPs). Within this
framework, a CSP instance is decomposed into a number of sub-problems, called
views, whose solutions are either already available or can be computed
efficiently. The g... | computer science |
21,255 | Goal-Driven Query Answering for Existential Rules with Equality | cs.AI | Inspired by the magic sets for Datalog, we present a novel goal-driven
approach for answering queries over terminating existential rules with equality
(aka TGDs and EGDs). Our technique improves the performance of query answering
by pruning the consequences that are not relevant for the query. This is
challenging in ou... | computer science |
21,256 | TorusE: Knowledge Graph Embedding on a Lie Group | cs.AI | Knowledge graphs are useful for many artificial intelligence (AI) tasks.
However, knowledge graphs often have missing facts. To populate the graphs,
knowledge graph embedding models have been developed. Knowledge graph embedding
models map entities and relations in a knowledge graph to a vector space and
predict unknow... | computer science |
21,257 | A Generally Applicable, Highly Scalable Measurement Computation and
Optimization Approach to Sequential Model-Based Diagnosis | cs.AI | Model-Based Diagnosis deals with the identification of the real cause of a
system's malfunction based on a formal system model and observations of the
system behavior. When a malfunction is detected, there is usually not enough
information available to pinpoint the real cause and one needs to discriminate
between multi... | computer science |
21,258 | Good and safe uses of AI Oracles | cs.AI | An Oracle is a design for potentially high power artificial intelligences
(AIs), where the AI is made safe by restricting it to only answer questions.
Unfortunately most designs cause the Oracle to be motivated to manipulate
humans with the contents of their answers, and Oracles of potentially high
intelligence might b... | computer science |
21,259 | The Neural Network Pushdown Automaton: Model, Stack and Learning
Simulations | cs.AI | In order for neural networks to learn complex languages or grammars, they
must have sufficient computational power or resources to recognize or generate
such languages. Though many approaches have been discussed, one ob- vious
approach to enhancing the processing power of a recurrent neural network is to
couple it with... | computer science |
21,260 | Predicting vehicular travel times by modeling heterogeneous influences
between arterial roads | cs.AI | Predicting travel times of vehicles in urban settings is a useful and
tangible quantity of interest in the context of intelligent transportation
systems. We address the problem of travel time prediction in arterial roads
using data sampled from probe vehicles. There is only a limited literature on
methods using data in... | computer science |
21,261 | Quantile Markov Decision Process | cs.AI | In this paper, we consider the problem of optimizing the quantiles of the
cumulative rewards of Markov Decision Processes (MDP), to which we refers as
Quantile Markov Decision Processes (QMDP). Traditionally, the goal of a Markov
Decision Process (MDP) is to maximize expected cumulative reward over a defined
horizon (p... | computer science |
21,262 | Using Noisy Extractions to Discover Causal Knowledge | cs.AI | Knowledge bases (KB) constructed through information extraction from text
play an important role in query answering and reasoning. In this work, we study
a particular reasoning task, the problem of discovering causal relationships
between entities, known as causal discovery. There are two contrasting types of
approache... | computer science |
21,263 | Using experimental game theory to transit human values to ethical AI | cs.AI | Knowing the reflection of game theory and ethics, we develop a mathematical
representation to bridge the gap between the concepts in moral philosophy
(e.g., Kantian and Utilitarian) and AI ethics industry technology standard
(e.g., IEEE P7000 standard series for Ethical AI). As an application, we
demonstrate how human ... | computer science |
21,264 | From Algorithmic Black Boxes to Adaptive White Boxes: Declarative
Decision-Theoretic Ethical Programs as Codes of Ethics | cs.AI | Ethics of algorithms is an emerging topic in various disciplines such as
social science, law, and philosophy, but also artificial intelligence (AI). The
value alignment problem expresses the challenge of (machine) learning values
that are, in some way, aligned with human requirements or values. In this paper
I argue fo... | computer science |
21,265 | One Model for the Learning of Language | cs.AI | A major target of linguistics and cognitive science has been to understand
what class of learning systems can acquire the key structures of natural
language. Until recently, the computational requirements of language have been
used to argue that learning is impossible without a highly constrained
hypothesis space. Here... | computer science |
21,266 | Exploring the Use of Shatter for AllSAT Through Ramsey-Type Problems | cs.AI | In the context of SAT solvers, Shatter is a popular tool for symmetry
breaking on CNF formulas. Nevertheless, little has been said about its use in
the context of AllSAT problems: problems where we are interested in listing all
the models of a Boolean formula. AllSAT has gained much popularity in recent
years due to it... | computer science |
21,267 | Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player
Online Battle Arena Games | cs.AI | Esports has emerged as a popular genre for players as well as spectators,
supporting a global entertainment industry. Esports analytics has evolved to
address the requirement for data-driven feedback, and is focused on
cyber-athlete evaluation, strategy and prediction. Towards the latter, previous
work has used match d... | computer science |
21,268 | Double Deep Machine Learning | cs.AI | Very important breakthroughs in data-centric machine learning algorithms led
to impressive performance in transactional point applications such as detecting
anger in speech, alerts from a Face Recognition system, or EKG interpretation.
Non-transactional applications, e.g. medical diagnosis beyond the EKG results,
requi... | computer science |
21,269 | Learning to select computations | cs.AI | Efficient use of limited computational resources is essential to
intelligence. Selecting computations optimally according to rational
metareasoning would achieve this, but rational metareasoning is computationally
intractable. Inspired by psychology and neuroscience, we propose the first
learning algorithm for approxim... | computer science |
21,270 | The destiny of constant structure discrete time closed semantic systems | cs.AI | Constant structure closed semantic systems are the systems each element of
which receives its definition through the correspondent unchangeable set of
other elements of the system. Discrete time means here that the definitions of
the elements change iteratively and simultaneously based on the "neighbor
portraits" from ... | computer science |
21,271 | Modeling Epistemological Principles for Bias Mitigation in AI Systems:
An Illustration in Hiring Decisions | cs.AI | Artificial Intelligence (AI) has been used extensively in automatic decision
making in a broad variety of scenarios, ranging from credit ratings for loans
to recommendations of movies. Traditional design guidelines for AI models focus
essentially on accuracy maximization, but recent work has shown that
economically irr... | computer science |
21,272 | Facets, Tiers and Gems: Ontology Patterns for Hypernormalisation | cs.AI | There are many methodologies and techniques for easing the task of ontology
building. Here we describe the intersection of two of these: ontology
normalisation and fully programmatic ontology development. The first of these
describes a standardized organisation for an ontology, with singly inherited
self-standing entit... | computer science |
21,273 | Related family-based attribute reduction of covering information systems
when varying attribute sets | cs.AI | In practical situations, there are many dynamic covering information systems
with variations of attributes, but there are few studies on related
family-based attribute reduction of dynamic covering information systems. In
this paper, we first investigate updated mechanisms of constructing attribute
reducts for consiste... | computer science |
21,274 | Situationally Aware Options | cs.AI | Hierarchical abstractions, also known as options -- a type of temporally
extended action (Sutton et. al. 1999) that enables a reinforcement learning
agent to plan at a higher level, abstracting away from the lower-level details.
In this work, we learn reusable options whose parameters can vary, encouraging
different be... | computer science |
21,275 | Recurrent Relational Networks for Complex Relational Reasoning | cs.AI | Humans possess an ability to abstractly reason about objects and their
interactions, an ability not shared with state-of-the-art deep learning models.
Relational networks, introduced by Santoro et al. (2017), add the capacity for
relational reasoning to deep neural networks, but are limited in the complexity
of the rea... | computer science |
21,276 | Asymmetric Action Abstractions for Multi-Unit Control in Adversarial
Real-Time Games | cs.AI | Action abstractions restrict the number of legal actions available during
search in multi-unit real-time adversarial games, thus allowing algorithms to
focus their search on a set of promising actions. Optimal strategies derived
from un-abstracted spaces are guaranteed to be no worse than optimal strategies
derived fro... | computer science |
21,277 | Building Machines that Learn and Think for Themselves: Commentary on
Lake et al., Behavioral and Brain Sciences, 2017 | cs.AI | We agree with Lake and colleagues on their list of key ingredients for
building humanlike intelligence, including the idea that model-based reasoning
is essential. However, we favor an approach that centers on one additional
ingredient: autonomy. In particular, we aim toward agents that can both build
and exploit their... | computer science |
21,278 | Improvised Comedy as a Turing Test | cs.AI | The best improvisational theatre actors can make any scene partner, of any
skill level or ability, appear talented and proficient in the art form, and
thus "make them shine". To challenge this improvisational paradigm, we built an
artificial intelligence (AI) trained to perform live shows alongside human
actors for hum... | computer science |
21,279 | Cascade Attribute Learning Network | cs.AI | We propose the cascade attribute learning network (CALNet), which can learn
attributes in a control task separately and assemble them together. Our
contribution is twofold: first we propose attribute learning in reinforcement
learning (RL). Attributes used to be modeled using constraint functions or
terms in the object... | computer science |
21,280 | D numbers theory based game-theoretic framework in adversarial decision
making under fuzzy environment | cs.AI | Adversarial decision making is a particular type of decision making problem
where the gain a decision maker obtains as a result of his decisions is
affected by the actions taken by others. Representation of alternatives'
evaluations and methods to find the optimal alternative are two important
aspects in the adversaria... | computer science |
21,281 | Pedagogical learning | cs.AI | A common assumption in machine learning is that training data are i.i.d.
samples from some distribution. Processes that generate i.i.d. samples are, in
a sense, uninformative---they produce data without regard to how good this data
is for learning. By contrast, cognitive science research has shown that when
people gene... | computer science |
21,282 | A general unified framework for interval pairwise comparison matrices | cs.AI | Interval Pairwise Comparison Matrices have been widely used to account for
uncertain statements concerning the preferences of decision makers. Several
approaches have been proposed in the literature, such as multiplicative and
fuzzy interval matrices. In this paper, we propose a general unified approach
to Interval Pai... | computer science |
21,283 | How linguistic descriptions of data can help to the teaching-learning
process in higher education, case of study: artificial intelligence | cs.AI | Artificial Intelligence is a central topic in the computer science
curriculum. From the year 2011 a project-based learning methodology based on
computer games has been designed and implemented into the intelligence
artificial course at the University of the Bio-Bio. The project aims to develop
software-controlled agent... | computer science |
21,284 | Diversity Constraints in Public Housing Allocation | cs.AI | The state of Singapore operates a national public housing program, accounting
for over 80% of its residential real estate. Singapore uses its housing
allocation program to ensure ethnic diversity in its neighborhoods; it does so
by imposing ethnic quotas: every ethnic group must not own more than a certain
percentage i... | computer science |
21,285 | Crossmodal Attentive Skill Learner | cs.AI | This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated
with the recently-introduced Asynchronous Advantage Option-Critic (A2OC)
architecture [Harb et al., 2017] to enable hierarchical reinforcement learning
across multiple sensory inputs. We provide concrete examples where the approach
not only i... | computer science |
21,286 | Classification of entities via their descriptive sentences | cs.AI | Hypernym identification of open-domain entities is crucial for taxonomy
construction as well as many higher-level applications. Current methods suffer
from either low precision or low recall. To decrease the difficulty of this
problem, we adopt a classification-based method. We pre-define a concept
taxonomy and classif... | computer science |
21,287 | A Modification of Particle Swarm Optimization using Random Walk | cs.AI | Particle swarm optimization comes under lot of changes after James Kennedy
and Russell Eberhart first proposes the idea in 1995. The changes has been done
mainly on Inertia parameters in velocity updating equation so that the
convergence rate will be higher. We are proposing a novel approach where
particles movement wi... | computer science |
21,288 | A reinforcement learning algorithm for building collaboration in
multi-agent systems | cs.AI | This paper presents a proof-of concept study for demonstrating the viability
of building collaboration among multiple agents through standard Q learning
algorithm embedded in particle swarm optimisation. Collaboration is formulated
to be achieved among the agents via some sort competition, where the agents are
expected... | computer science |
21,289 | Towards Data Quality Assessment in Online Advertising | cs.AI | In online advertising, our aim is to match the advertisers with the most
relevant users to optimize the campaign performance. In the pursuit of
achieving this goal, multiple data sources provided by the advertisers or
third-party data providers are utilized to choose the set of users according to
the advertisers' targe... | computer science |
21,290 | Improved Learning in Evolution Strategies via Sparser Inter-Agent
Network Topologies | cs.AI | We draw upon a previously largely untapped literature on human collective
intelligence as a source of inspiration for improving deep learning. Implicit
in many algorithms that attempt to solve Deep Reinforcement Learning (DRL)
tasks is the network of processors along which parameter values are shared. So
far, existing ... | computer science |
21,291 | Knowledge Graph Embedding with Iterative Guidance from Soft Rules | cs.AI | Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of
current research. Combining such an embedding model with logic rules has
recently attracted increasing attention. Most previous attempts made a one-time
injection of logic rules, ignoring the interactive nature between embedding
learning and l... | computer science |
21,292 | Learning to Compose Skills | cs.AI | We present a differentiable framework capable of learning a wide variety of
compositions of simple policies that we call skills. By recursively composing
skills with themselves, we can create hierarchies that display complex
behavior. Skill networks are trained to generate skill-state embeddings that
are provided as in... | computer science |
21,293 | New Techniques for Inferring L-Systems Using Genetic Algorithm | cs.AI | Lindenmayer systems (L-systems) are a formal grammar system that iteratively
rewrites all symbols of a string, in parallel. When visualized with a graphical
interpretation, the images have self-similar shapes that appear frequently in
nature, and they have been particularly successful as a concise, reusable
technique f... | computer science |
21,294 | A double competitive strategy based learning automata algorithm | cs.AI | Learning Automata (LA) are considered as one of the most powerful tools in
the field of reinforcement learning. The family of estimator algorithms is
proposed to improve the convergence rate of LA and has made great achievements.
However, the estimators perform poorly on estimating the reward probabilities
of actions i... | computer science |
21,295 | Novel Exploration Techniques (NETs) for Malaria Policy Interventions | cs.AI | The task of decision-making under uncertainty is daunting, especially for
problems which have significant complexity. Healthcare policy makers across the
globe are facing problems under challenging constraints, with limited tools to
help them make data driven decisions. In this work we frame the process of
finding an o... | computer science |
21,296 | Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to
Stop Worrying and Love the Social and Behavioural Sciences | cs.AI | In his seminal book `The Inmates are Running the Asylum: Why High-Tech
Products Drive Us Crazy And How To Restore The Sanity' [2004, Sams
Indianapolis, IN, USA], Alan Cooper argues that a major reason why software is
often poorly designed (from a user perspective) is that programmers are in
charge of design decisions, ... | computer science |
21,297 | Interactive Reinforcement Learning for Object Grounding via Self-Talking | cs.AI | Humans are able to identify a referred visual object in a complex scene via a
few rounds of natural language communications. Success communication requires
both parties to engage and learn to adapt for each other. In this paper, we
introduce an interactive training method to improve the natural language
conversation sy... | computer science |
21,298 | From knowledge-based to data-driven modeling of fuzzy rule-based
systems: A critical reflection | cs.AI | This paper briefly elaborates on a development in (applied) fuzzy logic that
has taken place in the last couple of decades, namely, the complementation or
even replacement of the traditional knowledge-based approach to fuzzy
rule-based systems design by a data-driven one. It is argued that the classical
rule-based mode... | computer science |
21,299 | Simulated Annealing Algorithm for Graph Coloring | cs.AI | The goal of this Random Walks project is to code and experiment the Markov
Chain Monte Carlo (MCMC) method for the problem of graph coloring. In this
report, we present the plots of cost function \(\mathbf{H}\) by varying the
parameters like \(\mathbf{q}\) (Number of colors that can be used in coloring)
and \(\mathbf{c... | computer science |
21,300 | SERKET: An Architecture for Connecting Stochastic Models to Realize a
Large-Scale Cognitive Model | cs.AI | To realize human-like robot intelligence, a large-scale cognitive
architecture is required for robots to understand the environment through a
variety of sensors with which they are equipped. In this paper, we propose a
novel framework named Serket that enables the construction of a large-scale
generative model and its ... | computer science |
21,301 | End-to-End Relation Extraction using Markov Logic Networks | cs.AI | The task of end-to-end relation extraction consists of two sub-tasks: i)
identifying entity mentions along with their types and ii) recognizing semantic
relations among the entity mention pairs. %Identifying entity mentions along
with their types and recognizing semantic relations among the entity mentions,
are two ver... | computer science |
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