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21,002 | Action and perception for spatiotemporal patterns | cs.AI | This is a contribution to the formalization of the concept of agents in
multivariate Markov chains. Agents are commonly defined as entities that act,
perceive, and are goal-directed. In a multivariate Markov chain (e.g. a
cellular automaton) the transition matrix completely determines the dynamics.
This seems to contra... | computer science |
21,003 | A New Probabilistic Algorithm for Approximate Model Counting | cs.AI | Constrained counting is important in domains ranging from artificial
intelligence to software analysis. There are already a few approaches for
counting models over various types of constraints. Recently, hashing-based
approaches achieve both theoretical guarantees and scalability, but still rely
on solution enumeration... | computer science |
21,004 | Fuzzy Recommendations in Marketing Campaigns | cs.AI | The population in Sweden is growing rapidly due to immigration. In this
light, the issue of infrastructure upgrades to provide telecommunication
services is of importance. New antennas can be installed at hot spots of user
demand, which will require an investment, and/or the clientele expansion can be
carried out in a ... | computer science |
21,005 | On Natural Language Generation of Formal Argumentation | cs.AI | In this paper we provide a first analysis of the research questions that
arise when dealing with the problem of communicating pieces of formal
argumentation through natural language interfaces. It is a generally held
opinion that formal models of argumentation naturally capture human argument,
and some preliminary stud... | computer science |
21,006 | Schema Networks: Zero-shot Transfer with a Generative Causal Model of
Intuitive Physics | cs.AI | The recent adaptation of deep neural network-based methods to reinforcement
learning and planning domains has yielded remarkable progress on individual
tasks. Nonetheless, progress on task-to-task transfer remains limited. In
pursuit of efficient and robust generalization, we introduce the Schema
Network, an object-ori... | computer science |
21,007 | Towards Grounding Conceptual Spaces in Neural Representations | cs.AI | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. It aims at bridging the gap between symbolic and
subsymbolic processing. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. In this paper, ... | computer science |
21,008 | Improving Scalability of Inductive Logic Programming via Pruning and
Best-Effort Optimisation | cs.AI | Inductive Logic Programming (ILP) combines rule-based and statistical
artificial intelligence methods, by learning a hypothesis comprising a set of
rules given background knowledge and constraints for the search space. We focus
on extending the XHAIL algorithm for ILP which is based on Answer Set
Programming and we eva... | computer science |
21,009 | Value-Decomposition Networks For Cooperative Multi-Agent Learning | cs.AI | We study the problem of cooperative multi-agent reinforcement learning with a
single joint reward signal. This class of learning problems is difficult
because of the often large combined action and observation spaces. In the fully
centralized and decentralized approaches, we find the problem of spurious
rewards and a p... | computer science |
21,010 | Evaluating the quality of tourist agendas customized to different travel
styles | cs.AI | Many tourist applications provide a personalized tourist agenda with the list
of recommended activities to the user. These applications must undoubtedly deal
with the constraints and preferences that define the user interests. Among
these preferences, we can find those that define the travel style of the user,
such as ... | computer science |
21,011 | Data set operations to hide decision tree rules | cs.AI | This paper focuses on preserving the privacy of sensitive patterns when
inducing decision trees. We adopt a record augmentation approach for hiding
sensitive classification rules in binary datasets. Such a hiding methodology is
preferred over other heuristic solutions like output perturbation or
cryptographic technique... | computer science |
21,012 | Learning to Schedule Deadline- and Operator-Sensitive Tasks | cs.AI | The use of semi-autonomous and autonomous robotic assistants to aid in care
of the elderly is expected to ease the burden on human caretakers, with
small-stage testing already occurring in a variety of countries. Yet, it is
likely that these robots will need to request human assistance via
teleoperation when domain exp... | computer science |
21,013 | Scalable Co-Optimization of Morphology and Control in Embodied Machines | cs.AI | Evolution sculpts both the body plans and nervous systems of agents together
over time. In contrast, in AI and robotics, a robot's body plan is usually
designed by hand, and control policies are then optimized for that fixed
design. The task of simultaneously co-optimizing the morphology and controller
of an embodied r... | computer science |
21,014 | User Intent Classification using Memory Networks: A Comparative Analysis
for a Limited Data Scenario | cs.AI | In this report, we provide a comparative analysis of different techniques for
user intent classification towards the task of app recommendation. We analyse
the performance of different models and architectures for multi-label
classification over a dataset with a relative large number of classes and only
a handful examp... | computer science |
21,015 | Session Analysis using Plan Recognition | cs.AI | This paper presents preliminary results of our work with a major financial
company, where we try to use methods of plan recognition in order to
investigate the interactions of a costumer with the company's online interface.
In this paper, we present the first steps of integrating a plan recognition
algorithm in a real-... | computer science |
21,016 | A Thorough Formalization of Conceptual Spaces | cs.AI | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. After pointing out a problem with the convexity requirement, we propose
a formalization ... | computer science |
21,017 | Structure Learning in Motor Control:A Deep Reinforcement Learning Model | cs.AI | Motor adaptation displays a structure-learning effect: adaptation to a new
perturbation occurs more quickly when the subject has prior exposure to
perturbations with related structure. Although this `learning-to-learn' effect
is well documented, its underlying computational mechanisms are poorly
understood. We present ... | computer science |
21,018 | Expert and Non-Expert Opinion about Technological Unemployment | cs.AI | There is significant concern that technological advances, especially in
Robotics and Artificial Intelligence (AI), could lead to high levels of
unemployment in the coming decades. Studies have estimated that around half of
all current jobs are at risk of automation. To look into this issue in more
depth, we surveyed ex... | computer science |
21,019 | Ensemble Framework for Real-time Decision Making | cs.AI | This paper introduces a new framework for real-time decision making in video
games. An Ensemble agent is a compound agent composed of multiple agents, each
with its own tasks or goals to achieve. Usually when dealing with real-time
decision making, reactive agents are used; that is agents that return a
decision based o... | computer science |
21,020 | On the enumeration of sentences by compactness | cs.AI | Presented is a Julia meta-program that discovers compact theories from data
if they exist. It writes candidate theories in Julia and then validates:
tossing the bad theories and keeping the good theories. Compactness is measured
by a metric: such as the number of space-time derivatives. The underlying
algorithm is appl... | computer science |
21,021 | CAN: Creative Adversarial Networks, Generating "Art" by Learning About
Styles and Deviating from Style Norms | cs.AI | We propose a new system for generating art. The system generates art by
looking at art and learning about style; and becomes creative by increasing the
arousal potential of the generated art by deviating from the learned styles. We
build over Generative Adversarial Networks (GAN), which have shown the ability
to learn ... | computer science |
21,022 | MAGIX: Model Agnostic Globally Interpretable Explanations | cs.AI | Explaining the behavior of a black box machine learning model at the instance
level is useful for building trust. However, what is also important is
understanding how the model behaves globally. Such an understanding provides
insight into both the data on which the model was trained and the
generalization power of the ... | computer science |
21,023 | Explanation in Artificial Intelligence: Insights from the Social
Sciences | cs.AI | There has been a recent resurgence in the area of explainable artificial
intelligence as researchers and practitioners seek to provide more transparency
to their algorithms. Much of this research is focused on explicitly explaining
decisions or actions to a human observer, and it should not be controversial to
say that... | computer science |
21,024 | Model Selection with Nonlinear Embedding for Unsupervised Domain
Adaptation | cs.AI | Domain adaptation deals with adapting classifiers trained on data from a
source distribution, to work effectively on data from a target distribution. In
this paper, we introduce the Nonlinear Embedding Transform (NET) for
unsupervised domain adaptation. The NET reduces cross-domain disparity through
nonlinear domain al... | computer science |
21,025 | Count-Based Exploration in Feature Space for Reinforcement Learning | cs.AI | We introduce a new count-based optimistic exploration algorithm for
Reinforcement Learning (RL) that is feasible in environments with
high-dimensional state-action spaces. The success of RL algorithms in these
domains depends crucially on generalisation from limited training experience.
Function approximation technique... | computer science |
21,026 | Specifying Non-Markovian Rewards in MDPs Using LDL on Finite Traces
(Preliminary Version) | cs.AI | In Markov Decision Processes (MDPs), the reward obtained in a state depends
on the properties of the last state and action. This state dependency makes it
difficult to reward more interesting long-term behaviors, such as always
closing a door after it has been opened, or providing coffee only following a
request. Exten... | computer science |
21,027 | Random Forests for Industrial Device Functioning Diagnostics Using
Wireless Sensor Networks | cs.AI | In this paper, random forests are proposed for operating devices diagnostics
in the presence of a variable number of features. In various contexts, like
large or difficult-to-access monitored areas, wired sensor networks providing
features to achieve diagnostics are either very costly to use or totally
impossible to sp... | computer science |
21,028 | Handling PDDL3.0 State Trajectory Constraints with Temporal Landmarks | cs.AI | Temporal landmarks have been proved to be a helpful mechanism to deal with
temporal planning problems, specifically to improve planners performance and
handle problems with deadline constraints. In this paper, we show the strength
of using temporal landmarks to handle the state trajectory constraints of
PDDL3.0. We ana... | computer science |
21,029 | Optimal choice: new machine learning problem and its solution | cs.AI | The task of learning to pick a single preferred example out a finite set of
examples, an "optimal choice problem", is a supervised machine learning problem
with complex, structured input. Problems of optimal choice emerge often in
various practical applications. We formalize the problem, show that it does not
satisfy t... | computer science |
21,030 | Relating Complexity-theoretic Parameters with SAT Solver Performance | cs.AI | Over the years complexity theorists have proposed many structural parameters
to explain the surprising efficiency of conflict-driven clause-learning (CDCL)
SAT solvers on a wide variety of large industrial Boolean instances. While some
of these parameters have been studied empirically, until now there has not been
a un... | computer science |
21,031 | SUNNY-CP and the MiniZinc Challenge | cs.AI | In Constraint Programming (CP) a portfolio solver combines a variety of
different constraint solvers for solving a given problem. This fairly recent
approach enables to significantly boost the performance of single solvers,
especially when multicore architectures are exploited. In this work we give a
brief overview of ... | computer science |
21,032 | Learning Knowledge Graph Embeddings with Type Regularizer | cs.AI | Learning relations based on evidence from knowledge bases relies on
processing the available relation instances. Many relations, however, have
clear domain and range, which we hypothesize could help learn a better, more
generalizing, model. We include such information in the RESCAL model in the
form of a regularization... | computer science |
21,033 | Indoor UAV scheduling with Restful Task Assignment Algorithm | cs.AI | Research in UAV scheduling has obtained an emerging interest from scientists
in the optimization field. When the scheduling itself has established a strong
root since the 19th century, works on UAV scheduling in indoor environment has
come forth in the latest decade. Several works on scheduling UAV operations in
indoor... | computer science |
21,034 | Restricted Causal Inference Algorithm | cs.AI | This paper proposes a new algorithm for recovery of belief network structure
from data handling hidden variables. It consists essentially in an extension of
the CI algorithm of Spirtes et al. by restricting the number of conditional
dependencies checked up to k variables and in an extension of the original CI
by additi... | computer science |
21,035 | A study of existing Ontologies in the IoT-domain | cs.AI | Several domains have adopted the increasing use of IoT-based devices to
collect sensor data for generating abstractions and perceptions of the real
world. This sensor data is multi-modal and heterogeneous in nature. This
heterogeneity induces interoperability issues while developing cross-domain
applications, thereby r... | computer science |
21,036 | Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem
to Suboptimal Variants | cs.AI | In multi-agent path finding (MAPF) the task is to find non-conflicting paths
for multiple agents. In this paper we focus on finding suboptimal solutions for
MAPF for the sum-of-costs variant. Recently, a SAT-based approached was
developed to solve this problem and proved beneficial in many cases when
compared to other ... | computer science |
21,037 | Development of the SP machine | cs.AI | This paper describes the main things that need to be done to develop the "SP
machine", based on the "SP theory of intelligence" and its realisation in the
"SP computer model". The SP machine may be developed initially as a software
virtual machine with high levels of parallel processing, hosted on a
high-performance co... | computer science |
21,038 | OPEB: Open Physical Environment Benchmark for Artificial Intelligence | cs.AI | Artificial Intelligence methods to solve continuous- control tasks have made
significant progress in recent years. However, these algorithms have important
limitations and still need significant improvement to be used in industry and
real- world applications. This means that this area is still in an active
research pha... | computer science |
21,039 | Visualizing the Consequences of Evidence in Bayesian Networks | cs.AI | This paper addresses the challenge of viewing and navigating Bayesian
networks as their structural size and complexity grow. Starting with a review
of the state of the art of visualizing Bayesian networks, an area which has
largely been passed over, we improve upon existing visualizations in three
ways. First, we apply... | computer science |
21,040 | Window-of-interest based Multi-objective Evolutionary Search for
Satisficing Concepts | cs.AI | The set-based concept approach has been suggested as a means to
simultaneously explore different design concepts, which are meaningful sub-sets
of the entire set of solutions. Previous efforts concerning the suggested
approach focused on either revealing the global front (s-Pareto front), of all
the concepts, or on fin... | computer science |
21,041 | ELF: An Extensive, Lightweight and Flexible Research Platform for
Real-time Strategy Games | cs.AI | In this paper, we propose ELF, an Extensive, Lightweight and Flexible
platform for fundamental reinforcement learning research. Using ELF, we
implement a highly customizable real-time strategy (RTS) engine with three game
environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a
miniature version of S... | computer science |
21,042 | Interpretable & Explorable Approximations of Black Box Models | cs.AI | We propose Black Box Explanations through Transparent Approximations (BETA),
a novel model agnostic framework for explaining the behavior of any black-box
classifier by simultaneously optimizing for fidelity to the original model and
interpretability of the explanation. To this end, we develop a novel objective
functio... | computer science |
21,043 | SADA: A General Framework to Support Robust Causation Discovery with
Theoretical Guarantee | cs.AI | Causation discovery without manipulation is considered a crucial problem to a
variety of applications. The state-of-the-art solutions are applicable only
when large numbers of samples are available or the problem domain is
sufficiently small. Motivated by the observations of the local sparsity
properties on causal stru... | computer science |
21,044 | Learning to Design Games: Strategic Environments in Deep Reinforcement
Learning | cs.AI | In typical reinforcement learning (RL), the environment is assumed given and
the goal of the learning is to identify an optimal policy for the agent taking
actions through its interactions with the environment. In this paper, we extend
this setting by considering the environment is not given, but controllable and
learn... | computer science |
21,045 | Machine Learning, Deepest Learning: Statistical Data Assimilation
Problems | cs.AI | We formulate a strong equivalence between machine learning, artificial
intelligence methods and the formulation of statistical data assimilation as
used widely in physical and biological sciences. The correspondence is that
layer number in the artificial network setting is the analog of time in the
data assimilation se... | computer science |
21,046 | Model enumeration in propositional circumscription via unsatisfiable
core analysis | cs.AI | Many practical problems are characterized by a preference relation over
admissible solutions, where preferred solutions are minimal in some sense. For
example, a preferred diagnosis usually comprises a minimal set of reasons that
is sufficient to cause the observed anomaly. Alternatively, a minimal
correction subset co... | computer science |
21,047 | Application of Fuzzy Assessing for Reliability Decision Making | cs.AI | This paper proposes a new fuzzy assessing procedure with application in
management decision making. The proposed fuzzy approach build the membership
functions for system characteristics of a standby repairable system. This
method is used to extract a family of conventional crisp intervals from the
fuzzy repairable syst... | computer science |
21,048 | Trust-PCL: An Off-Policy Trust Region Method for Continuous Control | cs.AI | Trust region methods, such as TRPO, are often used to stabilize policy
optimization algorithms in reinforcement learning (RL). While current trust
region strategies are effective for continuous control, they typically require
a prohibitively large amount of on-policy interaction with the environment. To
address this pr... | computer science |
21,049 | Well-Founded Operators for Normal Hybrid MKNF Knowledge Bases | cs.AI | Hybrid MKNF knowledge bases have been considered one of the dominant
approaches to combining open world ontology languages with closed world
rule-based languages. Currently, the only known inference methods are based on
the approach of guess-and-verify, while most modern SAT/ASP solvers are built
under the DPLL archite... | computer science |
21,050 | Emergence of Locomotion Behaviours in Rich Environments | cs.AI | The reinforcement learning paradigm allows, in principle, for complex
behaviours to be learned directly from simple reward signals. In practice,
however, it is common to carefully hand-design the reward function to encourage
a particular solution, or to derive it from demonstration data. In this paper
explore how a ric... | computer science |
21,051 | Measuring Relations Between Concepts In Conceptual Spaces | cs.AI | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by regions in this space.
Our recent mathematical formalization of this framework is capable of
representing correlations... | computer science |
21,052 | Lexicographic choice functions | cs.AI | We investigate a generalisation of the coherent choice functions considered
by Seidenfeld et al. (2010), by sticking to the convexity axiom but imposing no
Archimedeanity condition. We define our choice functions on vector spaces of
options, which allows us to incorporate as special cases both Seidenfeld et
al.'s (2010... | computer science |
21,053 | An Optimal Bayesian Network Based Solution Scheme for the Constrained
Stochastic On-line Equi-Partitioning Problem | cs.AI | A number of intriguing decision scenarios revolve around partitioning a
collection of objects to optimize some application specific objective function.
This problem is generally referred to as the Object Partitioning Problem (OPP)
and is known to be NP-hard. We here consider a particularly challenging version
of OPP, n... | computer science |
21,054 | Deductive and Analogical Reasoning on a Semantically Embedded Knowledge
Graph | cs.AI | Representing knowledge as high-dimensional vectors in a continuous semantic
vector space can help overcome the brittleness and incompleteness of
traditional knowledge bases. We present a method for performing deductive
reasoning directly in such a vector space, combining analogy, association, and
deduction in a straigh... | computer science |
21,055 | The Intentional Unintentional Agent: Learning to Solve Many Continuous
Control Tasks Simultaneously | cs.AI | This paper introduces the Intentional Unintentional (IU) agent. This agent
endows the deep deterministic policy gradients (DDPG) agent for continuous
control with the ability to solve several tasks simultaneously. Learning to
solve many tasks simultaneously has been a long-standing, core goal of
artificial intelligence... | computer science |
21,056 | Automated Game Design Learning | cs.AI | While general game playing is an active field of research, the learning of
game design has tended to be either a secondary goal of such research or it has
been solely the domain of humans. We propose a field of research, Automated
Game Design Learning (AGDL), with the direct purpose of learning game designs
directly th... | computer science |
21,057 | CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis | cs.AI | We propose and evaluate a new technique for learning hybrid automata
automatically by observing the runtime behavior of a dynamical system. Working
from a sequence of continuous state values and predicates about the
environment, CHARDA recovers the distinct dynamic modes, learns a model for
each mode from a given set o... | computer science |
21,058 | Proceedings of the 2017 AdKDD & TargetAd Workshop | cs.AI | Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with
the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax,
Nova Scotia, Canada. | computer science |
21,059 | Learning Macromanagement in StarCraft from Replays using Deep Learning | cs.AI | The real-time strategy game StarCraft has proven to be a challenging
environment for artificial intelligence techniques, and as a result, current
state-of-the-art solutions consist of numerous hand-crafted modules. In this
paper, we show how macromanagement decisions in StarCraft can be learned
directly from game repla... | computer science |
21,060 | P-Tree Programming | cs.AI | We propose a novel method for automatic program synthesis. P-Tree Programming
represents the program search space through a single probabilistic prototype
tree. From this prototype tree we form program instances which we evaluate on a
given problem. The error values from the evaluations are propagated through the
proto... | computer science |
21,061 | Mechanics Automatically Recognized via Interactive Observation: Jumping | cs.AI | Jumping has been an important mechanic since its introduction in Donkey Kong.
It has taken a variety of forms and shown up in numerous games, with each jump
having a different feel. In this paper, we use a modified Nintendo
Entertainment System (NES) emulator to semi-automatically run experiments on a
large subset (30%... | computer science |
21,062 | Independence, Conditionality and Structure of Dempster-Shafer Belief
Functions | cs.AI | Several approaches of structuring (factorization, decomposition) of
Dempster-Shafer joint belief functions from literature are reviewed with
special emphasis on their capability to capture independence from the point of
view of the claim that belief functions generalize bayes notion of probability.
It is demonstrated... | computer science |
21,063 | Identification and Interpretation of Belief Structure in Dempster-Shafer
Theory | cs.AI | Mathematical Theory of Evidence called also Dempster-Shafer Theory (DST) is
known as a foundation for reasoning when knowledge is expressed at various
levels of detail. Though much research effort has been committed to this theory
since its foundation, many questions remain open. One of the most important
open question... | computer science |
21,064 | A Formal Framework to Characterize Interpretability of Procedures | cs.AI | We provide a novel notion of what it means to be interpretable, looking past
the usual association with human understanding. Our key insight is that
interpretability is not an absolute concept and so we define it relative to a
target model, which may or may not be a human. We define a framework that
allows for comparin... | computer science |
21,065 | Automatic Mapping of NES Games with Mappy | cs.AI | Game maps are useful for human players, general-game-playing agents, and
data-driven procedural content generation. These maps are generally made by
hand-assembling manually-created screenshots of game levels. Besides being
tedious and error-prone, this approach requires additional effort for each new
game and level to... | computer science |
21,066 | Dependency Injection for Programming by Optimization | cs.AI | Programming by Optimization tools perform automatic software configuration
according to the specification supplied by a software developer. Developers
specify design spaces for program components, and the onerous task of
determining which configuration best suits a given use case is determined using
automated analysis ... | computer science |
21,067 | Constraints, Lazy Constraints, or Propagators in ASP Solving: An
Empirical Analysis | cs.AI | Answer Set Programming (ASP) is a well-established declarative paradigm. One
of the successes of ASP is the availability of efficient systems.
State-of-the-art systems are based on the ground+solve approach. In some
applications this approach is infeasible because the grounding of one or few
constraints is expensive. I... | computer science |
21,068 | Clingo goes Linear Constraints over Reals and Integers | cs.AI | The recent series 5 of the ASP system clingo provides generic means to
enhance basic Answer Set Programming (ASP) with theory reasoning capabilities.
We instantiate this framework with different forms of linear constraints,
discuss the respective implementations, and present techniques of how to use
these constraints i... | computer science |
21,069 | On (Anti)Conditional Independence in Dempster-Shafer Theory | cs.AI | This paper verifies a result of {Shenoy:94} concerning graphoidal structure
of Shenoy's notion of independence for Dempster-Shafer theory of belief
functions. Shenoy proved that his notion of independence has graphoidal
properties for positive normal valuations.
The requirement of strict positive normal valuations as... | computer science |
21,070 | Advances in Artificial Intelligence Require Progress Across all of
Computer Science | cs.AI | Advances in Artificial Intelligence require progress across all of computer
science. | computer science |
21,071 | Reliability Assessment of Distribution System Using Fuzzy Logic for
Modelling of Transformer and Line Uncertainties | cs.AI | Reliability assessment of distribution system, based on historical data and
probabilistic methods, leads to an unreliable estimation of reliability indices
since the data for the distribution components are usually inaccurate or
unavailable. Fuzzy logic is an efficient method to deal with the uncertainty in
reliability... | computer science |
21,072 | Fast Restricted Causal Inference | cs.AI | Hidden variables are well known sources of disturbance when recovering belief
networks from data based only on measurable variables. Hence models assuming
existence of hidden variables are under development.
This paper presents a new algorithm "accelerating" the known CI algorithm of
Spirtes, Glymour and Scheines {Sp... | computer science |
21,073 | AI Challenges in Human-Robot Cognitive Teaming | cs.AI | Among the many anticipated roles for robots in the future is that of being a
human teammate. Aside from all the technological hurdles that have to be
overcome with respect to hardware and control to make robots fit to work with
humans, the added complication here is that humans have many conscious and
subconscious expe... | computer science |
21,074 | FML-based Dynamic Assessment Agent for Human-Machine Cooperative System
on Game of Go | cs.AI | In this paper, we demonstrate the application of Fuzzy Markup Language (FML)
to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an
FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The
proposed FDAA comprises an intelligent decision-making and learning mechanism,
an intellige... | computer science |
21,075 | Tunnel Effects in Cognition: A new Mechanism for Scientific Discovery
and Education | cs.AI | It is quite exceptional, if it ever happens, that a new conceptual domain be
built from scratch. Usually, it is developed and mastered in interaction, both
positive and negative, with other more operational existing domains. Few
reasoning mechanisms have been proposed to account for the interplay of
different conceptua... | computer science |
21,076 | Improving Naive Bayes for Regression with Optimised Artificial Surrogate
Data | cs.AI | Can we evolve better training data for machine learning algorithms? To
investigate this question we use population-based optimisation algorithms to
generate artificial surrogate training data for naive Bayes for regression. We
demonstrate that the generalisation performance of naive Bayes for regression
models is enhan... | computer science |
21,077 | Improving Adherence to Heart Failure Management Guidelines via Abductive
Reasoning | cs.AI | Management of chronic diseases such as heart failure (HF) is a major public
health problem. A standard approach to managing chronic diseases by medical
community is to have a committee of experts develop guidelines that all
physicians should follow. Due to their complexity, these guidelines are
difficult to implement a... | computer science |
21,078 | Coalition formation for Multi-agent Pursuit based on Neural Network and
AGRMF Model | cs.AI | An approach for coalition formation of multi-agent pursuit based on neural
network and AGRMF model is proposed.This paper constructs a novel neural work
called AGRMF-ANN which consists of feature extraction part and group generation
part. On one hand,The convolutional layers of feature extraction part can
abstract the ... | computer science |
21,079 | When You Must Forget: beyond strong persistence when forgetting in
answer set programming | cs.AI | Among the myriad of desirable properties discussed in the context of
forgetting in Answer Set Programming (ASP), strong persistence naturally
captures its essence. Recently, it has been shown that it is not always
possible to forget a set of atoms from a program while obeying this property,
and a precise criterion rega... | computer science |
21,080 | A Comprehensive Implementation of Conceptual Spaces | cs.AI | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points and concepts
are represented by regions in a (potentially) high-dimensional space. Based on
our recent formalization, we present a comprehensive implementation of the
conceptual ... | computer science |
21,081 | Knowledge will Propel Machine Understanding of Content: Extrapolating
from Current Examples | cs.AI | Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be c... | computer science |
21,082 | Eigenlogic: Interpretable Quantum Observables with applications to Fuzzy
Behavior of Vehicular Robots | cs.AI | This work proposes a formulation of propositional logic, named Eigenlogic,
using quantum observables as propositions. The eigenvalues of these operators
are the truth-values and the associated eigenvectors the interpretations of the
propositional system. Fuzzy logic arises naturally when considering vectors
outside the... | computer science |
21,083 | Logic Programming approaches for routing fault-free and
maximally-parallel Wavelength Routed Optical Networks on Chip (Application
paper) | cs.AI | One promising trend in digital system integration consists of boosting
on-chip communication performance by means of silicon photonics, thus
materializing the so-called Optical Networks-on-Chip (ONoCs). Among them,
wavelength routing can be used to route a signal to destination by univocally
associating a routing path ... | computer science |
21,084 | Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems | cs.AI | Bayesian Filtering for plan and activity recognition is challenging for
scenarios that contain many observation equivalent entities (i.e. entities that
produce the same observations). This is due to the combinatorial explosion in
the number of hypotheses that need to be tracked. However, this class of
problems exhibits... | computer science |
21,085 | Outcome-Oriented Predictive Process Monitoring: Review and Benchmark | cs.AI | Predictive business process monitoring refers to the act of making
predictions about the future state of ongoing cases of a business process,
based on their incomplete execution traces and logs of historical (completed)
traces. Motivated by the increasingly pervasive availability of fine-grained
event data about busine... | computer science |
21,086 | Towards learning domain-independent planning heuristics | cs.AI | Automated planning remains one of the most general paradigms in Artificial
Intelligence, providing means of solving problems coming from a wide variety of
domains. One of the key factors restricting the applicability of planning is
its computational complexity resulting from exponentially large search spaces.
Heuristic... | computer science |
21,087 | A Framework for Easing the Development of Applications Embedding Answer
Set Programming | cs.AI | Answer Set Programming (ASP) is a well-established declarative problem
solving paradigm which became widely used in AI and recognized as a powerful
tool for knowledge representation and reasoning (KRR), especially for its high
expressiveness and the ability to deal also with incomplete knowledge.
Recently, thanks to ... | computer science |
21,088 | Preference Reasoning in Matching Procedures: Application to the
Admission Post-Baccalaureat Platform | cs.AI | Because preferences naturally arise and play an important role in many
real-life decisions, they are at the backbone of various fields. In particular
preferences are increasingly used in almost all matching procedures-based
applications. In this work we highlight the benefit of using AI insights on
preferences in a lar... | computer science |
21,089 | Adversarial Sets for Regularising Neural Link Predictors | cs.AI | In adversarial training, a set of models learn together by pursuing competing
goals, usually defined on single data instances. However, in relational
learning and other non-i.i.d domains, goals can also be defined over sets of
instances. For example, a link predictor for the is-a relation needs to be
consistent with th... | computer science |
21,090 | Domain Recursion for Lifted Inference with Existential Quantifiers | cs.AI | In recent work, we proved that the domain recursion inference rule makes
domain-lifted inference possible on several relational probability models
(RPMs) for which the best known time complexity used to be exponential. We also
identified two classes of RPMs for which inference becomes domain lifted when
using domain re... | computer science |
21,091 | Mutual Alignment Transfer Learning | cs.AI | Training robots for operation in the real world is a complex, time consuming
and potentially expensive task. Despite significant success of reinforcement
learning in games and simulations, research in real robot applications has not
been able to match similar progress. While sample complexity can be reduced by
training... | computer science |
21,092 | Evidence combination for a large number of sources | cs.AI | The theory of belief functions is an effective tool to deal with the multiple
uncertain information. In recent years, many evidence combination rules have
been proposed in this framework, such as the conjunctive rule, the cautious
rule, the PCR (Proportional Conflict Redistribution) rules and so on. These
rules can be ... | computer science |
21,093 | Un modèle pour la représentation des connaissances temporelles dans
les documents historiques | cs.AI | Processing and publishing the data of the historical sciences in the semantic
web is an interesting challenge in which the representation of temporal aspects
plays a key role. We propose in this paper a model of temporal knowledge
representation adapted to work on historical documents. This model is based on
the notion... | computer science |
21,094 | Speeding-up ProbLog's Parameter Learning | cs.AI | ProbLog is a state-of-art combination of logic programming and probabilities;
in particular ProbLog offers parameter learning through a variant of the EM
algorithm. However, the resulting learning algorithm is rather slow, even when
the data are complete. In this short paper we offer some insights that lead to
orders o... | computer science |
21,095 | Closed-Loop Policies for Operational Tests of Safety-Critical Systems | cs.AI | Manufacturers of safety-critical systems must make the case that their
product is sufficiently safe for public deployment. Much of this case often
relies upon critical event outcomes from real-world testing, requiring
manufacturers to be strategic about how they allocate testing resources in
order to maximize their cha... | computer science |
21,096 | Declarative Sequential Pattern Mining of Care Pathways | cs.AI | Sequential pattern mining algorithms are widely used to explore care pathways
database, but they generate a deluge of patterns, mostly redundant or useless.
Clinicians need tools to express complex mining queries in order to generate
less but more significant patterns. These algorithms are not versatile enough
to answe... | computer science |
21,097 | A Decidable Very Expressive Description Logic for Databases (Extended
Version) | cs.AI | We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally
closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples
(generalising the positional notation), projections of relations, and global
and local objectification of relations, able to express inclusion, functional,
key, and e... | computer science |
21,098 | Anytime Exact Belief Propagation | cs.AI | Statistical Relational Models and, more recently, Probabilistic Programming,
have been making strides towards an integration of logic and probabilistic
reasoning. A natural expectation for this project is that a probabilistic logic
reasoning algorithm reduces to a logic reasoning algorithm when provided a
model that on... | computer science |
21,099 | Preservation of Semantic Properties during the Aggregation of Abstract
Argumentation Frameworks | cs.AI | An abstract argumentation framework can be used to model the argumentative
stance of an agent at a high level of abstraction, by indicating for every pair
of arguments that is being considered in a debate whether the first attacks the
second. When modelling a group of agents engaged in a debate, we may wish to
aggregat... | computer science |
21,100 | Leveraging Demonstrations for Deep Reinforcement Learning on Robotics
Problems with Sparse Rewards | cs.AI | We propose a general and model-free approach for Reinforcement Learning (RL)
on real robotics with sparse rewards. We build upon the Deep Deterministic
Policy Gradient (DDPG) algorithm to use demonstrations. Both demonstrations and
actual interactions are used to fill a replay buffer and the sampling ratio
between demo... | computer science |
21,101 | Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models | cs.AI | Lifted inference algorithms commonly exploit symmetries in a probabilistic
graphical model (PGM) for efficient inference. However, existing algorithms for
Boolean-valued domains can identify only those pairs of states as symmetric, in
which the number of ones and zeros match exactly (count symmetries). Moreover,
algori... | computer science |
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