scholarly journals Solving Mathematical Puzzles: A Challenging Competition for AI

AI Magazine ◽  
2017 ◽  
Vol 38 (3) ◽  
pp. 83-96 ◽  
Author(s):  
Federico Chesani ◽  
Paola Mello ◽  
Michela Milano
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Scientific Community ◽  
Deep Understanding ◽  
Human Intervention ◽  
Foreseeable Future ◽  
Comprehensive Knowledge ◽  
Problem Solving Process ◽  
Significant Attention

Recently, a number of noteworthy results have been achieved in various fields of artificial intelligence, and many aspects of the problem solving process have received significant attention by the scientific community. In this context, the extraction of comprehensive knowledge suitable for problem solving and reasoning, from textual and pictorial problem descriptions, has been less investigated, but recognized as essential for autonomous thinking in Artificial Intelligence. In this work we present a challenge where methods and tools for deep understanding are strongly needed for enabling problem solving: we propose to solve mathematical puzzles by means of computers, starting from text and diagrams describing them, without any human intervention. We are aware that the proposed challenge is hard and of difficult solution nowadays (and in the foreseeable future), but even studying and solving only single parts of the proposed challenge would represent an important step forward for artificial intelligence.

Abduction and Hypothesis Withdrawal in Science

1998 ◽  
pp. 180-187 ◽  
Author(s):  
Lorenzo Magnani
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Scientific Reasoning ◽  
Scientific Discovery ◽  
Data Driven ◽  
Negation As Failure ◽  
Weak Hypothesis ◽  
The Subject ◽  
Epistemological Model ◽  
Problem Solving Process

This paper introduces an epistemological model of scientific reasoning which can be described in terms of abduction, deduction and induction. The aim is to emphasize the significance of abduction in order to illustrate the problem-solving process and to propose a unified epistemological model of scientific discovery. The model first describes the different meanings of the word abduction (creative, selective, to the best explanation, visual) in order to clarify their significance for epistemology and artificial intelligence. In different theoretical changes in theoretical systems we witness different kinds of discovery processes operating. Discovery methods are "data-driven," "explanation-driven" (abductive), and "coherence-driven" (formed to overwhelm contradictions). Sometimes there is a mixture of such methods: for example, an hypothesis devoted to overcome a contradiction is found by abduction. Contradiction, far from damaging a system, help to indicate regions in which it can be changed and improved. I will also consider a kind of "weak" hypothesis that is hard to negate and the ways for making it easy. In these cases the subject can "rationally" decide to withdraw his or her hypotheses even in contexts where it is "impossible" to find "explicit" contradictions and anomalies. Here, the use of negation as failure (an interesting technique for negating hypotheses and accessing new ones suggested by artificial intelligence and cognitive scientists) is illuminating

scholarly journals A Logic Framework for Non-Conscious Reasoning

2017 ◽  
Author(s):  
Felipe Lara-Rosano
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Fuzzy Sets ◽  
Object Oriented ◽  
Knowledge Based Systems ◽  
Multivalued Logic ◽  
Knowledge Based ◽  
Problem Solving Process ◽  
Everyday Problems

Human non-conscious reasoning is one of the most successful procedures developed to solve everyday problems in an efficient way. This is why the field of artificial intelligence should analyze, formalize and emulate the multiple ways of non-conscious reasoning with the purpose of applying them in knowledge based systems, neurocomputers and similar devices for aiding people in the problem-solving process. In this paper, a framework for those non-conscious ways of reasoning is presented based on object-oriented representations, fuzzy sets and multivalued logic.

scholarly journals Problem Solving Processes and Group Effectiveness on a Creative Task: A case study in Physics Education

2019 ◽  
Author(s):  
Javier Pulgar ◽  
Alexis Spina
Keyword(s):  
Problem Solving ◽  
Physics Education ◽  
Group Performance ◽  
Deep Understanding ◽  
School Level ◽  
Learning Activity ◽  
Group Effectiveness ◽  
High School Level ◽  
School Students ◽  
Problem Solving Process

We investigated a group of physics majors solving a creative problem in the context of a course on conceptual physics and children's thinking adapted from the Physics and Everyday Thinking (PET) curriculum. In addition to learning concep- tual physics, course participants discussed the ways that elementary and middle school students learn physics and their common pre-instructional ideas and models of physics phenomena. To explore group performance, research participants were asked to collaboratively design a physics learning activity with at least two questions either for elementary, middle or high school level. Participants' discussion was audio recorded, and analyzed with attention to emergent themes of the problem solving process. Next, we used a model of group effectiveness to identi?ed the degree to which the group met the conditions for effective performance. Results suggest the group decided the content and questions for the task following a creative process where they generated ideas on the structure of the problem and its scienti?c narrative, while also making decision regarding targeted students age, what these would do when facing the problem, and engaged in the process of requesting ideas and information. These processes shaped the conditions for e?ectiveness, which enabled a deep understanding on the team's dynamic.

scholarly journals The Situational Rationality of Task Performance: Artifacts of Belief in Human Problem-Solving and Artificial Intelligence

2021 ◽  
Author(s):  
Michael W. Raphael
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Task Performance ◽  
Human Intervention ◽  
Cognitive Artifacts ◽  
Symbolic Model ◽  
The Third ◽  
The Relationship ◽  
Human Problem ◽  
Machine Problem

Understanding the artificiality of belief is crucial for the image it generates regarding task performance in the course of problem-solving. This paper examines the contribution of the artificial as a model of human intervention and its relationship to a model of human participation. Specifically, it details the logical differences underlying how belief operationalizes the perception of complexity and its effects on task performance in human and machine problem-solving. Three configurations of artificiality are presented to explain these differences and their effects on the relationship between representation and computation in problem-solving. The first describes the “natural artifact” that arises from a symbolic model of intelligence and the design of a maze. The second describes the “natural artifact” that arises from a sub-symbolic model of intelligence and the design of a mold. The third examines how a hybrid model of intelligence requires “socio-cognitive artifacts” in which a means of adaptation is primarily mediated by discourse rather than design. In doing so, the paper examines how momentary beliefs explain the situational rationality of task performance. The paper concludes with a commentary on the requisites of artificial intelligibility in machine problem-solving.

Moving to Solution

2013 ◽  
Vol 60 (6) ◽  
pp. 403-409 ◽  
Author(s):  
K. Werner ◽  
M. Raab
Keyword(s):  
Problem Solving ◽  
Embodied Cognition ◽  
Cognitive Functions ◽  
Problem Space ◽  
Arm Swing ◽  
String Problem ◽  
Problem Solving Process ◽  
Problem Solving Task ◽  
Bodily Movements

Embodied cognition theories suggest a link between bodily movements and cognitive functions. Given such a link, it is assumed that movement influences the two main stages of problem solving: creating a problem space and creating solutions. This study explores how specific the link between bodily movements and the problem-solving process is. Seventy-two participants were tested with variations of the two-string problem (Experiment 1) and the water-jar problem (Experiment 2), allowing for two possible solutions. In Experiment 1 participants were primed with arm-swing movements (swing group) and step movements on a chair (step group). In Experiment 2 participants sat in front of three jars with glass marbles and had to sort these marbles from the outer jars to the middle one (plus group) or vice versa (minus group). Results showed more swing-like solutions in the swing group and more step-like solutions in the step group, and more addition solutions in the plus group and more subtraction solutions in the minus group. This specificity of the connection between movement and problem-solving task will allow further experiments to investigate how bodily movements influence the stages of problem solving.

Proses Berpikir Logis Siswa Sekolah Dasar Bertipe Kecerdasan Logis Matematis dalam Memecahkan Masalah Matematika

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Liska Yanti Pane ◽  
Kamid Kamid ◽  
Asrial Asrial
Keyword(s):  
Problem Solving ◽  
Learning Process ◽  
Qualitative Method ◽  
Logical Thinking ◽  
Think Aloud ◽  
Process Data ◽  
Thinking Process ◽  
Problem Solving Process ◽  
Problem Solving Strategy ◽  
Solving Strategy

This research aims to describe logical thinking process of a logical-mathematical intelligence student. We employ qualitative method to disclose the subject’s learning process. Data are collected by interview and modified think aloud methods. The results show that subject has capability to find and organize problems and data correctly. Subject describes conditions that are needed to do the steps of problem solving strategy. The steps are done systematically until the end of problem solving process.

Proses Berpikir Kreatif Siswa Tipe Linguistik dalamPemecahan Masalah Biologi

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Imelda Aisah Sarip ◽  
Kamid Kamid ◽  
Bambang Hariyadi
Keyword(s):  
Problem Solving ◽  
Data Collection ◽  
Creative Thinking ◽  
Research Data ◽  
Think Aloud ◽  
The Subject ◽  
Thinking Process ◽  
Problem Solving Process ◽  
The Given

The aim of this research is to describe creative thinking process of linguistic type student in biology problem solving. This research is conducted to linguistic intelligence type of subject at SMPN 6 Kota Jambi. SL the subject was selected based on the aim of the research. Data collection is conducted by interview and a modified think aloud method. Data is analyzed based on creative thinking process purposed by Polya.The result of this research shows that SL could find and arrange the given problems and collect data correctly and appropriately. The problem solving steps is done systematically to the end of problem solving process. The last steps problem solving, SL does checking while doing scratching to make sure that the written answers meet her need.

Spoken Natural Language Dialog Systems

1995 ◽  
Author(s):  
Ronnie W. Smith ◽  
D. Richard Hipp
Keyword(s):  
Problem Solving ◽  
Natural Language ◽  
Error Correction ◽  
User Model ◽  
Dialog Systems ◽  
Problem Solving Process ◽  
Processing Architecture ◽  
Dialog Processing

As spoken natural language dialog systems technology continues to make great strides, numerous issues regarding dialog processing still need to be resolved. This book presents an exciting new dialog processing architecture that allows for a number of behaviors required for effective human-machine interactions, including: problem-solving to help the user carry out a task, coherent subdialog movement during the problem-solving process, user model usage, expectation usage for contextual interpretation and error correction, and variable initiative behavior for interacting with users of differing expertise. The book also details how different dialog problems in processing can be handled simultaneously, and provides instructions and in-depth result from pertinent experiments. Researchers and professionals in natural language systems will find this important new book an invaluable addition to their libraries.

Systems thinking, a consilience of values and logic

2005 ◽  
Vol 24 (4) ◽  
pp. 259-274
Author(s):  
Sameer Kumar ◽  
Thomas Ressler ◽  
Mark Ahrens
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Systems Thinking ◽  
Qualitative Reasoning ◽  
Colleges And Universities ◽  
Decision Makers ◽  
Modern World ◽  
Problem Solving Process ◽  
Problem Solvers

This article is an appeal to incorporate qualitative reasoning into quantitative topics and courses, especially those devoted to decision-making offered in colleges and universities. Students, many of whom join professional workforce, must become more systems thinkers and decision-makers than merely problem-solvers. This will entail discussion of systems thinking, not just reaching “the answer”. Managers will need to formally and forcefully discuss objectives and values at each stage of the problem-solving process – at the start, during the problem-solving stage, and at the interpretation of the results stage – in order to move from problem solving to decision-making. The authors suggest some methods for doing this, and provide examples of why doing so is so important for decision-makers in the modern world.

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