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.

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.

Computational Complexity and Philosophical Dualism

1998 ◽  
pp. 61-66
Author(s):  
Joao Teixeira
Keyword(s):  
Artificial Intelligence ◽  
Computational Complexity ◽  
Complexity Theory ◽  
A Priori ◽  
Mathematical Intuition ◽  
The Third ◽  
Fundamental Limit ◽  
The Mind ◽  
The Relationship ◽  
Computing Machines

I examine some recent controversies involving the possibility of mechanical simulation of mathematical intuition. The first part is concerned with a presentation of the Lucas-Penrose position and recapitulates some basic logical conceptual machinery (Gödel's proof, Hilbert's Tenth Problem and Turing's Halting Problem). The second part is devoted to a presentation of the main outlines of Complexity Theory as well as to the introduction of Bremermann's notion of transcomputability and fundamental limit. The third part attempts to draw a connection/relationship between Complexity Theory and undecidability focusing on a new revised version of the Lucas-Penrose position in light of physical a priori limitations of computing machines. Finally, the last part derives some epistemological/philosophical implications of the relationship between Gödel's incompleteness theorem and Complexity Theory for the mind/brain problem in Artificial Intelligence and discusses the compatibility of functionalism with a materialist theory of the mind.

scholarly journals Connectors of smart design and smart systems

2021 ◽  
pp. 1-19
Author(s):  
Imre Horváth
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Real Life ◽  
Exploratory Research ◽  
Smart Systems ◽  
The Third ◽  
Recent Developments ◽  
Creative Problem ◽  
Application Specific ◽  
System Knowledge

AbstractThough they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them: (i) What way are smart design and smart systems enabled by artificial narrow, general, or super intelligence? and (ii) How can smart design be used in the realization of smart systems? and How can smart systems contribute to smart designing? A difficulty is that there are no exact definitions for these novel concepts in the literature. The endeavor to analyze the current situation and to answer the above questions stimulated an exploratory research whose first findings are summarized in this paper. Its first part elaborates on a plausible interpretation of the concept of smartness and provides an overview of the characteristics of smart design as a creative problem solving methodology supported by artificial intelligence. The second part exposes the paradigmatic features and system engineering issues of smart systems, which are equipped with application-specific synthetic system knowledge and reasoning mechanisms. The third part presents and elaborates on a conceptual model of AI-based couplings of smart design and smart systems. The couplings may manifest in various concrete forms in real life that are referred to as “connectors” in this paper. The principal types of connectors are exemplified and discussed. It has been found that smart design tends to manifest as a methodology of blue-printing smart systems and that smart systems will be intellectualized the enablers of implementation of smart design. Understanding the affordances of and creating proper connectors between smart design and smart systems need further explorative research.

scholarly journals DARPA’s Explainable Artificial Intelligence (XAI) Program

AI Magazine ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 44-58 ◽  
Author(s):  
David Gunning ◽  
David Aha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Task Performance ◽  
Evaluation Framework ◽  
Interaction Techniques ◽  
New Wave ◽  
The Third ◽  
Transportation Security ◽  
Explainable Artificial Intelligence ◽  
User Trust

Dramatic success in machine learning has led to a new wave of AI applications (for example, transportation, security, medicine, finance, defense) that offer tremendous benefits but cannot explain their decisions and actions to human users. DARPA’s explainable artificial intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. Realizing this goal requires methods for learning more explainable models, designing effective explanation interfaces, and understanding the psychologic requirements for effective explanations. The XAI developer teams are addressing the first two challenges by creating ML techniques and developing principles, strategies, and human-computer interaction techniques for generating effective explanations. Another XAI team is addressing the third challenge by summarizing, extending, and applying psychologic theories of explanation to help the XAI evaluator define a suitable evaluation framework, which the developer teams will use to test their systems. The XAI teams completed the first of this 4-year program in May 2018. In a series of ongoing evaluations, the developer teams are assessing how well their XAM systems’ explanations improve user understanding, user trust, and user task performance.

scholarly journals “Die siel van die mier”: Reflections on the battle for ‘scholarly’ intelligence

2015 ◽  
Vol 11 (2) ◽  
Author(s):  
Martin Olivier
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
20Th Century ◽  
Tertiary Education ◽  
The Other ◽  
Problem Solving Skills ◽  
Research Programmes ◽  
The Individual ◽  
Human Problem ◽  
Current Emphasis

This essay traces two research programmes in broad strokes. Both programmes start from the same observation — the behaviour of an ant (or termite) colony and the ability of the ant colony to act in a collective manner to achieve goals that the individual ant cannot. For one programme such behaviour is indicative of intelligence; for the other it is indicative of (collective) instinct. The primary intention of the essay is not to assess the claims of intelligence found, but to consider the rationale of the researchers involved in the two programmes for doing such research. It is observed that virtue in one programme is understanding (with the concomitant ability to explain — and, hence, teach), while the primary virtue in the other programme is the utility — and ultimately efficiency — that this may add to human problem solving skills. The two programmes used as illustration are Eugène Marais’s study of termites in the first half of the 20th century and the emergence of artificial intelligence projects that are inspired by ant behaviour in the second half of the 20th century. The essay suggests that the current emphasis of inquiry at tertiary education institutions embraces utility to the extent that it displaces pure insight — and hence the ability to explain and, ultimately, the ability to teach.

SPATIAL KNOWLEDGE REPRESENTATION

1989 ◽  
Vol 03 (01) ◽  
pp. 67-84 ◽  
Author(s):  
SARGUR N. SRIHARI ◽  
ZHIGANG XIANG
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Intelligent Systems ◽  
Image Interpretation ◽  
Spatial Knowledge ◽  
Robot Motion ◽  
Language Understanding ◽  
The Third ◽  
Analogical Representation ◽  
Propositional Representation

The use of spatial knowledge is necessary in a variety of artificial intelligence and expert systems applications. The need is not only in tasks with spatial goals such as image interpretation and robot motion, but also in tasks not involving spatial goals, e.g. diagnosis and language understanding. The paper discusses methods of representing spatial knowledge, with particular focus on the broad categories known as analogical and propositional representations. The problem of neurological localization is considered in some detail as an example of intelligent problem-solving that requires the use of spatial knowledge. Several solutions for the problem are presented: the first uses an analogical representation only, the second uses a propositional representation and the third uses an integrated representation. Conclusions about the different representations for building intelligent systems are drawn.

Module 3: Doing Tools

2009 ◽  
pp. 97-116
Author(s):  
Dolores Gallagher-Thompson ◽  
Larry W. Thompson
Keyword(s):  
Problem Solving ◽  
Behavioral Activation ◽  
Late Life ◽  
Activity Monitoring ◽  
Activity Scheduling ◽  
Late Life Depression ◽  
The Third ◽  
The Relationship ◽  
Monitoring Activity ◽  
Pleasant Events

This chapter describes the third module of CBT for late-life depression, which focuses on activity tools, including behavioral activation, activity monitoring, activity scheduling, the importance of pleasant activities, the California Older Person’s Pleasant Events Schedule (COPPES), and graphing the relationship between pleasant events and mood, as well as problem-solving steps and techniques.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

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