Some Assumptions about Problem Solving Representation in Turing’s Model of Intelligence

1970 ◽  
Vol 4 (2) ◽  
pp. 136-142 ◽  
Author(s):  
Raymundo Morado ◽  
Francisco Hernández-Quiroz
Keyword(s):  
Problem Solving ◽  
Computational Models ◽  
Turing Machines

Turing machines as a model of intelligence can be motivated under some assumptions, both mathematical and philosophical. Some of these are about the possibility, the necessity, and the limits of representing problem solving by mechanical means. The assumptions about representation that we consider in this paper are related to information representability and availability, processing as solving, nonessentiality of complexity issues, and finiteness, discreteness and sequentiality of the representation. We discuss these assumptions and particularly something that might happen if they were to be rejected or weakened. Tinkering with these assumptions sheds light on the import of alternative computational models.

scholarly journals Some Assumptions about Problem Solving Representation in Turing’s Model of Intelligence

1970 ◽  
Vol 4 (2) ◽  
pp. 136-142
Author(s):  
Raymundo Morado ◽  
Francisco Hernández-Quiroz
Keyword(s):  
Problem Solving ◽  
Computational Models ◽  
Turing Machines

Turing machines as a model of intelligence can be motivated under some assumptions, both mathematical and philosophical. Some of these are about the possibility, the necessity, and the limits of representing problem solving by mechanical means. The assumptions about representation that we consider in this paper are related to information representability and availability, processing as solving, nonessentiality of complexity issues, and finiteness, discreteness and sequentiality of the representation. We discuss these assumptions and particularly something that might happen if they were to be rejected or weakened. Tinkering with these assumptions sheds light on the import of alternative computational models.

scholarly journals Processing Information in the Clouds

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 25
Author(s):  
Mark Burgin ◽  
Eugene Eberbach ◽  
Rao Mikkilineni
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Human Brain ◽  
Incremental Learning ◽  
Computational Models ◽  
Higher Order ◽  
Turing Machines ◽  
New Paradigm ◽  
New Model ◽  
Processing Information

Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.

An Intelligent Agent-Based Cooperative Information Processing Model

2011 ◽  
pp. 1-25
Author(s):  
Li Yao ◽  
Weiming Zhang
Keyword(s):  
Information System ◽  
Problem Solving ◽  
Computational Models ◽  
Information Management System ◽  
Coarse Grained ◽  
Information Processing Model ◽  
Cooperative Problem Solving ◽  
Loosely Coupled ◽  
Basic Organization ◽  
Cooperative Methods

This chapter presents a Basic Organization Structure (BOS) model for building the large and complex distributed cooperative information system in large mutual networks. It argues that a large and complex cooperative information system and its subsystems in a LAN can be modeled by multi-agent organization and basic organization respectively. With the BOS model, such a cooperative information system can be developed easily and it is more manageable, effectively supporting the complicated cooperative methods under uncertain conditions. BOS is mainly used to support the cooperative problem solving among the coarse-grained, loosely coupled, and groups of semiautonomous agents. The essential characteristics, knowledge representations, and computational models of the BOS model are illuminated in this chapter. As an application example, we use the BOS model to realize the distributed Assumption-based Cooperative Problem Solving (ACPS) in the Distributed Traveling Information Management System prototype.

scholarly journals Revisiting the simulation of quantum Turing machines by quantum circuits

2019 ◽  
Vol 475 (2226) ◽  
pp. 20180767 ◽  
Author(s):  
Abel Molina ◽  
John Watrous
Keyword(s):  
Turing Machine ◽  
Polynomial Time ◽  
Computational Models ◽  
Circuit Complexity ◽  
Simulation Method ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Turing Machines ◽  
Generated Family ◽  
Quantum Turing Machine

Yao's 1995 publication ‘Quantum circuit complexity’ in Proceedings of the 34th Annual IEEE Symposium on Foundations of Computer Science , pp. 352–361, proved that quantum Turing machines and quantum circuits are polynomially equivalent computational models: t ≥ n steps of a quantum Turing machine running on an input of length n can be simulated by a uniformly generated family of quantum circuits with size quadratic in t , and a polynomial-time uniformly generated family of quantum circuits can be simulated by a quantum Turing machine running in polynomial time. We revisit the simulation of quantum Turing machines with uniformly generated quantum circuits, which is the more challenging of the two simulation tasks, and present a variation on the simulation method employed by Yao together with an analysis of it. This analysis reveals that the simulation of quantum Turing machines can be performed by quantum circuits having depth linear in t , rather than quadratic depth, and can be extended to variants of quantum Turing machines, such as ones having multi-dimensional tapes. Our analysis is based on an extension of method described by Arright, Nesme and Werner in 2011 in Journal of Computer and System Sciences 77 , 372–378. ( doi:10.1016/j.jcss.2010.05.004 ), that allows for the localization of causal unitary evolutions.

INTEGRATING DISTRIBUTED EXPERTISE

1992 ◽  
Vol 01 (03n04) ◽  
pp. 393-410 ◽  
Author(s):  
EVANGELOS SIMOUDIS ◽  
MARK ADLER
Keyword(s):  
Problem Solving ◽  
Expert Systems ◽  
Real World ◽  
Computational Models ◽  
Knowledge Based Systems ◽  
Computing Environment ◽  
The Past ◽  
Knowledge Based ◽  
Narrow Scope ◽  
Distributed Expertise

Over the past ten years a myriad of knowledge-based expert systems have been developed and deployed. These systems have a narrow scope and usually operate in stand-alone mode. They also follow different implementation philosophies and use a variety of reasoning methods. To address problems of wider scope, researchers have developed systems that utilize either centralized or distributed computational models. Each of these systems is homogeneous, and due to the way developed, prohibitively expensive for real-world settings. In this paper we present OMNI, a framework for integrating existing knowledge-based systems in a way that they can cooperate during problem-solving while they remain distributed over a computing environment.

scholarly journals Examining Student Regulation of Collaborative, Computational, Problem-Solving Processes in Open-Ended Learning Environments

2021 ◽  
Vol 8 (1) ◽  
pp. 49-74
Author(s):  
Mona Emara ◽  
Nicole Hutchins ◽  
Shuchi Grover ◽  
Caitlin Snyder ◽  
Gautam Biswas
Keyword(s):  
Problem Solving ◽  
Language Processing ◽  
Computational Models ◽  
Model Building ◽  
Computational Modelling ◽  
Computational Thinking ◽  
Collaborative Problem Solving ◽  
Model Errors ◽  
Systematic Analysis ◽  
K 12

The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively construct computational models. While this approach has produced significant learning gains for students in both science and CT in K–12 settings, the collaborative learning processes students use, including learner regulation, are not well understood. In this paper, we present a systematic analysis framework that combines natural language processing (NLP) of collaborative dialogue, log file analyses of students’ model-building actions, and final model scores. This analysis is used to better understand students’ regulation of collaborative problem solving (CPS) processes over a series of computational modelling tasks of varying complexity. The results suggest that the computational modelling challenges afford opportunities for students to a) explore resource-intensive processes, such as trial and error, to more systematic processes, such as debugging model errors by leveraging data tools, and b) learn from each other using socially shared regulation (SSR) and productive collaboration. The use of such SSR processes correlated positively with their model-building scores. Our paper aims to advance our understanding of collaborative, computational modelling in K–12 science to better inform classroom applications.

Design and Implementation of Computational Modeling for Learning Mathematical Concepts

2013 ◽  
pp. 128-146
Author(s):  
Shelby P. Morge ◽  
Mahnaz Moallem ◽  
Chris Gordon ◽  
Gene Tagliarini ◽  
Sridhar Narayan
Keyword(s):  
Problem Solving ◽  
Computational Models ◽  
Web Search ◽  
Mathematics Teacher Education ◽  
Design Development ◽  
Mathematical Concepts ◽  
Mathematical Practices ◽  
Technology Project ◽  
Information Technology Project ◽  
Support Students

The Common Core State Standards (CCSS) call for a change in the way mathematics is taught. The mathematical practices outlined by the CCSS call for mathematics as a problem-solving endeavor, rather than routine exercises and practice. A quick Web search can provide mathematics teachers with an abundance of workshops and courses, examples, and videos of the different mathematical practices to help them understand what they mean and look like in practice. However, those examples do not go far in changing the current culture of mathematics instruction. In this chapter, the authors discuss current US mathematics instructional practices and how the CCSS are asking for distinctly different teaching practices. In addition, the authors share how the innovative Using Squeak to Infuse Information Technology Project (USeIT) sidestepped traditional mathematics instructional approaches and utilized problem-solving activities and the development of computational models to support students’ learning of STEM concepts. The authors illustrate how the design, development, and implementation of a Squeak Etoys and Problem-Based Learning (PBL) activity addresses the CCSS expectations for mathematics content, practice for learning, and assessment, and discuss what this means for mathematics teacher education and professional development.

scholarly journals KABOOM: An Agent-Based Model for Simulating Cognitive Sale in Team Problem Solving

2019 ◽  
Author(s):  
Christopher McComb ◽  
Kathryn Jablokow ◽  
Samuel Lapp
Keyword(s):  
Problem Solving ◽  
Cognitive Style ◽  
Computational Models ◽  
Cognitive Styles ◽  
Small Scale ◽  
Design Team ◽  
Agent Based Model ◽  
Behavioral Experiments ◽  
Agent Based ◽  
Team Organization

The performance of a design team is influenced by each team member's unique cognitive style - i.e., their preferred manner of managing structure as they solve problems, make decisions, and seek to bring about change. Cognitive style plays an important role in how teams of engineers design and collaborate, but the interactions of cognitive style with team organization and processes have not been well studied. The limitations of small-scale behavioral experiments have led researchers to develop computational models for simulating teamwork; however, none have modeled the effects of individuals' cognitive styles. This paper presents KABOOM (KAI Agent-Based Organizational Optimization Model), the first agent-based model of teamwork to incorporate cognitive style. In KABOOM, heterogeneous agents imitate the diverse problem-solving styles described by Kirton's Adaption-Innovation construct, which places each individual somewhere along the spectrum of cognitive style preference. Using the model, we investigate the interacting effects of a team's communication patterns, specialization, and cognitive style composition on design performance. By simulating cognitive style in the context of team problem solving, KABOOM lays the groundwork for the development of team simulations that reflect humans' diverse problem-solving styles.

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