696 research outputs found
Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone
We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. Our results show that, as expected, the cheating MCTS
outperforms all the other strategies; ISMCTS is stronger than all the
rule-based players implementing well-known and most advanced strategies and it
also turns out to be a challenging opponent for human players.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game
Answer Set Programming Modulo `Space-Time'
We present ASP Modulo `Space-Time', a declarative representational and
computational framework to perform commonsense reasoning about regions with
both spatial and temporal components. Supported are capabilities for mixed
qualitative-quantitative reasoning, consistency checking, and inferring
compositions of space-time relations; these capabilities combine and synergise
for applications in a range of AI application areas where the processing and
interpretation of spatio-temporal data is crucial. The framework and resulting
system is the only general KR-based method for declaratively reasoning about
the dynamics of `space-time' regions as first-class objects. We present an
empirical evaluation (with scalability and robustness results), and include
diverse application examples involving interpretation and control tasks
Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset
The automatic categorization of crime news is useful to create statistics on the type of crimes occurring in a certain area. This assignment can be treated as a text categorization problem. Several studies have shown that the use of word embeddings improves outcomes in many Natural Language Processing (NLP), including text categorization. The scope of this paper is to explore the use of word embeddings for Italian crime news text categorization. The approach followed is to compare different document pre-processing, Word2Vec models and methods to obtain word embeddings, including the extraction of bigrams and keyphrases. Then, supervised and unsupervised Machine Learning categorization algorithms have been applied and compared. In addition, the imbalance issue of the input dataset has been addressed by using Synthetic Minority Oversampling Technique (SMOTE) to oversample the elements in the minority classes. Experiments conducted on an Italian dataset of 17,500 crime news articles collected from 2011 till 2021 show very promising results. The supervised categorization has proven to be better than the unsupervised categorization, overcoming 80% both in precision and recall, reaching an accuracy of 0.86. Furthermore, lemmatization, bigrams and keyphrase extraction are not so decisive. In the end, the availability of our model on GitHub together with the code we used to extract word embeddings allows replicating our approach to other corpus either in Italian or other languages
A Grammar for Reproducible and Painless Extract-Transform-Load Operations on Medium Data
Many interesting data sets available on the Internet are of a medium
size---too big to fit into a personal computer's memory, but not so large that
they won't fit comfortably on its hard disk. In the coming years, data sets of
this magnitude will inform vital research in a wide array of application
domains. However, due to a variety of constraints they are cumbersome to
ingest, wrangle, analyze, and share in a reproducible fashion. These
obstructions hamper thorough peer-review and thus disrupt the forward progress
of science. We propose a predictable and pipeable framework for R (the
state-of-the-art statistical computing environment) that leverages SQL (the
venerable database architecture and query language) to make reproducible
research on medium data a painless reality.Comment: 30 pages, plus supplementary material
ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a set of
algorithms on a per-instance basis in order to exploit the varying performance
of algorithms over a set of instances. The algorithm selection problem is
attracting increasing attention from researchers and practitioners in AI. Years
of fruitful applications in a number of domains have resulted in a large amount
of data, but the community lacks a standard format or repository for this data.
This situation makes it difficult to share and compare different approaches
effectively, as is done in other, more established fields. It also
unnecessarily hinders new researchers who want to work in this area. To address
this problem, we introduce a standardized format for representing algorithm
selection scenarios and a repository that contains a growing number of data
sets from the literature. Our format has been designed to be able to express a
wide variety of different scenarios. Demonstrating the breadth and power of our
platform, we describe a set of example experiments that build and evaluate
algorithm selection models through a common interface. The results display the
potential of algorithm selection to achieve significant performance
improvements across a broad range of problems and algorithms.Comment: Accepted to be published in Artificial Intelligence Journa
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
Knowledge Graph Embeddings (KGEs) have been intensively explored in recent
years due to their promise for a wide range of applications. However, existing
studies focus on improving the final model performance without acknowledging
the computational cost of the proposed approaches, in terms of execution time
and environmental impact. This paper proposes a simple yet effective KGE
framework which can reduce the training time and carbon footprint by orders of
magnitudes compared with state-of-the-art approaches, while producing
competitive performance. We highlight three technical innovations: full batch
learning via relational matrices, closed-form Orthogonal Procrustes Analysis
for KGEs, and non-negative-sampling training. In addition, as the first KGE
method whose entity embeddings also store full relation information, our
trained models encode rich semantics and are highly interpretable.
Comprehensive experiments and ablation studies involving 13 strong baselines
and two standard datasets verify the effectiveness and efficiency of our
algorithm.Comment: To appear at NAACL 202
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