scholarly journals AISpace2: An Interactive Visualization Tool for Learning and Teaching Artificial Intelligence

2020 ◽  
Vol 34 (09) ◽  
pp. 13436-13443
Author(s):  
Chenliang Zhou ◽  
Dominic Kuang ◽  
Jingru Liu ◽  
Hanbo Yang ◽  
Zijia Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Network ◽  
Open Source ◽  
Constraint Satisfaction ◽  
Interactive Visualization ◽  
Constraint Satisfaction Problems ◽  
Graphical Form ◽  
Visualization Tool ◽  
Learning And Teaching ◽  
High Level

AIspace is a set of tools used to learn and teach fundamental AI algorithms. The original version of AIspace was written in Java. There was not a clean separation of the algorithms and visualization; it was too complicated for students to modify the underlying algorithms. Its next generation, AIspace2, is built on AIPython, open source Python code that is designed to be as close as possible to pseudocode. AISpace2, visualized in JupyterLab, keeps the simple Python code, and uses hooks in AIPython to allow visualization of the algorithms. This allows students to see and modify the high-level algorithms in Python, and to visualize the output in a graphical form, aiming to better help them to build confidence and comfort in AI concepts and algorithms. So far we have tools for search, constraint satisfaction problems (CSP), planning and Bayesian network. In this paper we outline the tools and give some evaluations based on user feedback.

Artificial Intelligence Systems Based on NTA

2018 ◽  
pp. 257-279
Keyword(s):  
Artificial Intelligence ◽  
Constraint Satisfaction ◽  
Formal Concept Analysis ◽  
Semantic Networks ◽  
Concept Analysis ◽  
Constraint Satisfaction Problems ◽  
Formal Concept ◽  
Artificial Intelligence Systems

This chapter describes NTA capabilities in modeling various kinds of intelligence systems, namely discrete automata, systems for Formal Concept Analysis and for solving Constraint Satisfaction Problems, as well as for unified representation and processing of knowledge expressed in different conventional structures: productions, semantic networks, frames, etc.

scholarly journals Modeling Michael Jarrell’s CSP in Gelisp

2018 ◽  
Author(s):  
Mauricio Toro
Keyword(s):  
Constraint Satisfaction ◽  
Computer Music ◽  
Automatic Generation ◽  
Optimization Criterion ◽  
Constraint Satisfaction Problems ◽  
Time Limit ◽  
Musical Structures ◽  
High Level

Constraint Satisfaction Problems (CSPs) in computer music are used to solve harmonic, rhythmic or melodic problems. In addition,they can be used for automatic generation of musical structures satisfying a set of rules. Forinstance, the CSP proposed by compositor Michael Jarrell, which we explain in this document anddetail its implementation. Usually, a CSP is represented by a script defining the variables, theirdomain, and its constraints.Instead of writing a script, in Gelisp for OpenMusic (OM) we represent a program with a specialpatch. A patch is a visual algorithm, in which boxes represent functional calls, and connectionsare functional compositions. Inside this CSP patch, we can place special boxes: to connect eachconstraint in the CSP, to define variable and value heuristics, to define a time limit in the search,to connect the list of variables that we want to observe, and a box to connect the variable to bethe optimization criterion during the search.Furthermore, we provide a variety of boxes to represent simple constraints (e.g., a = b anda < 2) and high-level constraints (e.g., “the motive A occurs n times in the sequence S”). Theoutput of a CSP patch can be connected to three different kind of boxes: to find one solution, tofind all the solutions, and to perform propagation (narrow the domain of the variables) withoutsearch.

scholarly journals Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Jorge Humberto Moreno-Scott ◽  
José Carlos Ortiz-Bayliss ◽  
Hugo Terashima-Marín ◽  
Santiago Enrique Conant-Pablos
Keyword(s):  
Artificial Intelligence ◽  
Constraint Satisfaction ◽  
Operations Research ◽  
Special Interest ◽  
Research Community ◽  
Constraint Satisfaction Problems ◽  
The Past ◽  
Variable Ordering ◽  
Instance Space ◽  
Variable Ordering Heuristics

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems.

scholarly journals A Multidisciplinary Artificial Intelligence Model of an Affective Robot

10.5772/45662 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 6 ◽  
Author(s):  
Hooman Aghaebrahimi Samani ◽  
Elham Saadatian
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Network ◽  
Multidisciplinary Approach ◽  
Affective State ◽  
Dynamic Bayesian Network ◽  
Endocrine System ◽  
Affective States ◽  
Intelligence System ◽  
Human Love ◽  
High Level

A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA), based on the psychology of love, Artificial Endocrine System (AES), based on the physiology of love, and Affective State Transition (AST), based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN). The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.

scholarly journals Tractable Set Constraints

2012 ◽  
Vol 45 ◽  
pp. 731-759 ◽  
Author(s):  
M. Bodirsky ◽  
M. Hils
Keyword(s):  
Artificial Intelligence ◽  
Constraint Satisfaction ◽  
Constraint Satisfaction Problem ◽  
Description Logics ◽  
Constraint Satisfaction Problems ◽  
Algebraic Characterization ◽  
Crucial Importance ◽  
Hard Constraint ◽  
Set Constraints ◽  
Constraint Language

Many fundamental problems in artificial intelligence, knowledge representation, and verification involve reasoning about sets and relations between sets and can be modeled as set constraint satisfaction problems (set CSPs). Such problems are frequently intractable, but there are several important set CSPs that are known to be polynomial-time tractable. We introduce a large class of set CSPs that can be solved in quadratic time. Our class, which we call EI, contains all previously known tractable set CSPs, but also some new ones that are of crucial importance for example in description logics. The class of EI set constraints has an elegant universal-algebraic characterization, which we use to show that every set constraint language that properly contains all EI set constraints already has a finite sublanguage with an NP-hard constraint satisfaction problem.

PEMBUATAN PENDETEKSI OBYEK DENGAN METODE YOU ONLY LOOK ONCE (YOLO) UNTUK AUTOMATED TELLER MACHINE (ATM)

2019 ◽  
Vol 17 (1) ◽  
pp. 69-76
Author(s):  
Mohammad Shiddiq Ghozali
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Object Detection ◽  
Open Source ◽  
Automated Teller Machine

Perkembangan Teknologi Informasi dan Komunikasi begitu pesat di zaman sekarang ini. Diikuti pula dengan perkembangan di bidang Artificial Intelligence (AI) atau Kecerdasan Buatan. Di Indonesia sendiri masih belum begitu populer dikalangan masyarakat akan tetapi perusahaan-perusahaan IT berlomba-lomba menciptakan inovasi dibidang Kecerdasan Buatan dan penerapan Kecerdasan Buatan disegala aspek kehidupan. Contoh kasus di Automated Teller Machine (ATM), seringkali terjadi kejahatan di ATM seperti pengintaian nomor pin, skimming, lebanese loop dan kejahatan lainnya. Walaupun di ATM sudah terdapat CCTV akan tetapi penjahat menggunakan alat bantu untuk menutupi wajahnya seperti helm, topi, masker dan kacamata hitam. Biasanya didepan pintu masuk ATM terpampang larangan untuk tidak menggunakan helm, topi, masker dan kacamata hitam serta tidak membawa rokok. Akan tetapi larangan itu masih tetap ada yang melanggar, dikarenakan tidak ada tindak lanjut ketika seseorang menggunakan benda-benda yang dilarang dibawa kedalam ATM. Oleh karena itu penulis membuat sistem pendeteksi obyek di bidang Kecerdasan Buatan untuk mendeteksi benda-benda yang dilarang digunakan ketika berada di ATM. Salah satu metode yang digunakan untuk menciptakan Object Detection yaitu You Only Look Once (YOLO). Implementasi ide ini tersedia pada DARKNET (open source neural network). Cara kerja YOLO yaitu dengan melihat seluruh gambar sekali, kemudian melewati jaringan saraf sekali langsung mendeteksi object yang ada. Oleh karena itu disebut You Only Look Once (YOLO). Pada penelitian ini, penulis membuat sistem yang masih dalam bentuk pengembangan, sehingga menjalankannya masih menggunakan command prompt. Keywords : Automated Teller Machine (ATM), Kecerdasan Buatan, Pendeteksi Obyek, You Only Look Once (YOLO)  

Europe

2020 ◽  
pp. 649-666
Author(s):  
Andrea Renda
Keyword(s):  
Artificial Intelligence ◽  
Digital Technology ◽  
Policy Framework ◽  
Expert Group ◽  
Ethical Guidelines ◽  
Ethical Implications ◽  
Strong Focus ◽  
Ethics Guidelines ◽  
Definition Of ◽  
High Level

This chapter assesses Europe’s efforts in developing a full-fledged strategy on the human and ethical implications of artificial intelligence (AI). The strong focus on ethics in the European Union’s AI strategy should be seen in the context of an overall strategy that aims at protecting citizens and civil society from abuses of digital technology but also as part of a competitiveness-oriented strategy aimed at raising the standards for access to Europe’s wealthy Single Market. In this context, one of the most peculiar steps in the European Union’s strategy was the creation of an independent High-Level Expert Group on AI (AI HLEG), accompanied by the launch of an AI Alliance, which quickly attracted several hundred participants. The AI HLEG, a multistakeholder group including fifty-two experts, was tasked with the definition of Ethics Guidelines as well as with the formulation of “Policy and Investment Recommendations.” With the advice of the AI HLEG, the European Commission put forward ethical guidelines for Trustworthy AI—which are now paving the way for a comprehensive, risk-based policy framework.

Sign in / Sign up

Export Citation Format

Share Document