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We propose in this paper a framework of prioritised logic programs (PLP) to represent priority information explicitly in a program. Differently of others approaches, we do not restrain the preference relation only to literals, but to sets of literals. As consequence, we can express in PLPs sophisticated forms of preferences without changing the programs or introducing new atoms to obtain artificially the intended preferences. Besides, inspired by various developments in the literature on preference, we present a comprehensive and systematic treatment to deal with preferences in logic programming. In fact, we introduced 32 different criteria (semantics) to establish preference between partial stable models as well as those semantics whose definition depends on partial stable models. We show some properties of our framework; in particular, we guarantee these semantics for PLP generalise their counterparts for logic programs without preferences.

BRACIS 2021

A Systematic Approach to Define Semantics for Prioritised Logic Programs

preferences

logic programming

semantics

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

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**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

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Organized by C4AI, the 10th Brazilian Conference on Intelligent Systems (BRACIS) will take place from November 29th to December 3rd, 2021.

This work investigates the use of a Deep Neural Network (DNN) to perform an estimation of the Weapon Engagement Zone (WEZ) maximum launch range. The WEZ allows the pilot to identify an airspace in which the available missile has a more significant probability of successfully engaging a particular target, i.e., a hypothetical area surrounding an aircraft in which an adversary is vulnerable to a shot. We propose an approach to determine the WEZ of a given missile using 50,000 simulated launches in variate conditions. These simulations are used to train a DNN that can predict the WEZ when the aircraft finds itself on different firing conditions, with a coefficient of determination of 0.99. It provides another procedure concerning preceding research since it employs a non-discretized model, i.e., it considers all directions of the WEZ at once, which has not been done previously. Additionally, the proposed method uses an experimental design that allows for fewer simulation runs, providing faster model training.

Weapon Engagement Zone Maximum Launch Range Estimation Using Deep Neural Network

Recently, Brazil's National Council of Justice (CNJ) highlighted the importance of robust solutions to perform automated lawsuit classification. A correct lawsuit classification substantially improves the assertiveness of (i) distribution, (ii) organization of the agenda of court hearing and sessions, (iii) classification of urgent measures and evidence, (iv) identification of prescription and (v) prevention. This paper investigates different text classification methods and different combinations of embeddings, extracted from Portuguese language models, and information about legislation cited in the initial documents. The models were trained with a Golden Collection of 16 thousand initial petitions and indictments from the Court of Justice of the State of Ceará, in Brazil, whose lawsuits were classified in the five more representative CNJ's classes - Common Civil Procedure, Execution of Extrajudicial Title, Criminal Action - Ordinary Procedure, Special Civil Court Procedure, and Tax Enforcement. Our best result was obtained by the BERT model, achieving 0.88 of F1 score (macro), in the experiment scenario that represents the lawsuit in an embedding formed by concatenating the texts of all the petitions that contain at least one citation to one legislation. Legal documents have specific characteristics such as long documents, specialized vocabulary, formal syntax, semantics based on a broad specific domain of knowledge, and citations to laws. Our interpretation is that the representation of the document through contextual embeddings generated by BERT, as well as the architecture of the model with bidirectional contexts, makes it possible to capture the specific context of the domain of legal documents.

Text Classification in Legal Documents Extracted from Lawsuits in Brazilian Courts

Bloat is a common issue regarding Genetic Programming (GP), specially noted in Symbolic Regression (SR) problems. Due to this, GP tends to generate a huge amount of ineffective code that could be avoided or removed. Code editing is one of many approaches to avoid bloat. The objective in this strategy is to mutate or remove subtrees which do not contribute to the final solution. Two known methods of redundant code removal, the Rule Based Simplification (RBS) and Equivalent Decision Simplification (EDS) are extended in a new operator presented in this paper, called Dynamic Operator with RBS and EDS (DORE). This operator gives the algebraic simplification table used by RBS the potential to learn from reductions performed by EDS. An initial benchmark highlighted how the RBS table can grow as much as 86% with DORE, and reducing the time spent on simplification by 16.83%. Experiments with the other three SR problems were performed showing a considerable improvement on fitness of the generated programs, besides a slight reduction in the population of the average tree size.

Improving Rule Based and Equivalent Decision Simplifications for Bloat Control in Genetic Programming Using a Dynamic Operator

In the Steiner Tree Problem in Graphs, a subset of nodes, called terminals, must be efficiently interconnected. The graph is undirected and weighted, and candidate solutions can include additional nodes called Steiner vertices. The problem is NP-complete, and optimization algorithms based on population metaheuristics, e.g., Genetic Algorithms (GAs), have been proposed. However, traditional recombination operators may produce inefficient solutions for graph-based problems. We propose a new recombination operator for the Steiner Tree Problem based on the graph representation. The new operator is a kind of partition crossover: it breaks the graph formed by the union of two parents solutions and decomposes the evaluation function by finding connected subgraphs. We also investigate two soft-repair operators that produce small changes in candidate solutions: an MST transformation and a pruning repair. The GA with the proposed crossover operator was able to find the global optimum solution for all tested instances and has a success rate of 28\% in the worst case. The experiments with the proposed crossover presented a quick convergence to optimal solutions, and an average solution cost lower in most cases than other approaches.

A Graph-based crossover and soft-repair operators for the Steiner Tree Problem

Adding self-loops at each vertex of a graph improves the performance of quantum walks algorithms over loopless algorithms. Many works approach quantum walks to search for a single marked vertex. In this article, we experimentally address several problems related to quantum walk in the hypercube with self-loops to search for multiple marked vertices. We first investigate the quantum walk in the loopless hypercube. We saw that neighbor vertices are also amplified and that approximately $1/2$ of the system energy is concentrated in them. We show that the optimal value of $l$ for a single marked vertex is not optimal for multiple marked vertices. We define a new value of $l = (n/N)\cdot k$ to search multiple marked vertices. Next, we use this new value of $l$ found to analyze the search for multiple marked vertices non-adjacent and show that the probability of success is close to $1$. We also use the new value of $l$ found to analyze the search for several marked vertices that are adjacent and show that the probability of success is directly proportional to the density of marked vertices in the neighborhood. We also show that, in the case where neighbors are marked, if there is at least one non-adjacent marked vertex, the probability of success increases to close to $1$. The results found show that the self-loop value for the quantum walk in the hypercube to search for several marked vertices is $l = (n / N) \cdot k $.

Lackadaisical quantum walk in the hypercube to search for multiple marked vertices

Several hybrid approaches have been proposed to solve numerical constrained optimization problems. In this paper we present an Improved Interval Differential Evolution (I2DE) that uses structural information of the instance during the optimization process. We extend the math operations supported by a multi-interval core implementation that allows pruning infeasible solutions by using local consistency techniques and a backtrack-free local search. Furthermore, we propose a reformulation of interval evolutionary mutation strategies. A comprehensive experimental analysis is conducted over COCONUT and CEC2018 competition benchmarks and indicates that the hybridization between metaheuristics and constraint programming significantly improves the quality of the solutions. The experimental evaluation shows that our black-box version of I2DE outperformed several state-of-the-art solvers.

I2DE: Improved Interval Differential Evolution for Numerical Constrained Global Optimization

The extensive exploration of the Low Earth Orbit (LEO) has created a dangerous spacial environment, where space debris has threatened the feasibility of future operations. In this sense, Active Debris Removal (ADR) missions are required to clean up the space, deorbiting the debris with a spacecraft. ADR mission planning has been investigated in the literature by means of metaheuristic approaches, focused on maximizing the amount of removed debris given the constraints of the spacecraft. The state-of-the-art approach uses an inver-over and maximal open walk algorithms to solve this problem. However, that approach fails to deal with large instances and duration constraints. This work extends the state of the art, increasing its performance and modeling all the constraints. Experimental results evidence the improvements over the original approach, including the ability to run for scenarios with thousands of debris.

An Enhanced TSP-based Approach for Active Debris Removal Mission Planning

Deception is an essential behavior in many card games. Despite this fact, it is not trivial to capture the intent of a human strategist when making deceptive decisions. That is even harder when dealing with deception in card games, where components of uncertainty, hidden information, luck and randomness introduce the need of case-based decision making. Approaching this problem along with the investigation of the game of Truco, a quite popular game in Southern regions of South America, my presentation introduces an approach that combines active learning and Case-Based Reasoning (CBR) in which agents request a human specialist to review a reused game action retrieved from a case base containing played Truco hands. That happens when the agents are confronted with game situations that are identified as opportunities for deception. The goal is to actively capture problem-solving experiences in which deception can be used, and later employ such case knowledge in the enhancement of the deceptive capabilities of the Truco agents. Experimental results show that the use of the learned cases enabled different kinds of Truco agents to play more aggressively, being more deceptive and performing a larger number of successful bluffs.

Active Learning and Case-Based Reasoning for the Deceptive Play in the Card Game of Truco

Acceptability semantics for the frameworks of weighted argumentation can satisfy up to one of the principles of Quality Precedence, Cardinality Precedence or Compensation, which are pairwise incompatible. In this paper we define two new principles: Quality Compensation and Cardinality Compensation, which are weakened versions of Quality Precedence and Cardinality Precedence, respectively. We show that these new principles are compatible with Compensation and propose two new semantics: a T-conorm-based, which can satisfy Quality Compensation and a cumulative sum-based semantics, which satisfies Cardinality Compensation.

On the Refinement of Compensation-based Semantics for Weighted Argumentation Frameworks

We introduce an interrogation mark ? in ASPIC+ languages as a plausibility operator to enhance any defeasible conclusion does not have the same status as an irrefutable one. The resulting framework, dubbed ASPIC?, is tailored to make a distinction between strong inconsistencies and weak inconsistencies. The aim is to avoid the former and to tolerate the latter. This means the extensions obtained from the ASPIC? framework are free of strong conflicts, but tolerant to weak conflicts. Then, in the current study, we show ASPIC? satisfy reasonable properties. In particular, we focus on the property that a conflict between two arguments should not interfere with the acceptability of other unrelated arguments. With this purpose in mind, we prove under which conditions the important principles of Non-interference and Crash-Resistance hold in ASPIC?.

A Systematic Approach to Define Semantics for Prioritised Logic Programs

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Weapon Engagement Zone Maximum Launch Range Estimation Using Deep Neural Network

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