scholarly journals AAAI 2008 Spring Symposia Reports

AI Magazine ◽  
2008 ◽  
Vol 29 (3) ◽  
pp. 107
Author(s):  
Marcello Balduccini ◽  
Chitta Baral ◽  
Boyan Brodaric ◽  
Simon Colton ◽  
Peter Fox ◽  
...  
Keyword(s):  
Information Processing ◽  
Social Behavior ◽  
Semantic Web ◽  
Computer Science ◽  
Scientific Knowledge ◽  
Intelligent Systems ◽  
Process Management ◽  
Knowledge Engineering ◽  
Business Rules ◽  
Symbiotic Relationships

The Association for the Advancement of Artificial Intelligence (AAAI) was pleased to present the AAAI 2008 Spring Symposium Series, held Wednesday through Friday, March 26–28, 2008 at Stanford University, California. The titles of the eight symposia were as follows: (1) AI Meets Business Rules and Process Management, (2) Architectures for Intelligent Theory-Based Agents, (3) Creative Intelligent Systems, (4) Emotion, Personality, and Social Behavior, (5) Semantic Scientific Knowledge Integration, (6) Social Information Processing, (7) Symbiotic Relationships between Semantic Web and Knowledge Engineering, (8) Using AI to Motivate Greater Participation in Computer Science The goal of the AI Meets Business Rules and Process Management AAAI symposium was to investigate the various approaches and standards to represent business rules, business process management and the semantic web with respect to expressiveness and reasoning capabilities. The focus of the Architectures for Intelligent Theory-Based Agents AAAI symposium was the definition of architectures for intelligent theory-based agents, comprising languages, knowledge representation methodologies, reasoning algorithms, and control loops. The Creative Intelligent Systems Symposium included five major discussion sessions and a general poster session (in which all contributing papers were presented). The purpose of this symposium was to explore the synergies between creative cognition and intelligent systems. The goal of the Emotion, Personality, and Social Behavior symposium was to examine fundamental issues in affect and personality in both biological and artificial agents, focusing on the roles of these factors in mediating social behavior. The Semantic Scientific Knowledge Symposium was interested in bringing together the semantic technologies community with the scientific information technology community in an effort to build the general semantic science information community. The Social Information Processing's goal was to investigate computational and analytic approaches that will enable users to harness the efforts of large numbers of other users to solve a variety of information processing problems, from discovering high-quality content to managing common resources. The goal of the Symbiotic Relationships between the Semantic Web and Software Engineering symposium was to explore how the lessons learned by the knowledge-engineering community over the past three decades could be applied to the bold research agenda of current workers in semantic web technologies. The purpose of the Using AI to Motivate Greater Participation in Computer Science symposium was to identify ways that topics in AI may be used to motivate greater student participation in computer science by highlighting fun, engaging, and intellectually challenging developments in AI-related curriculum at a number of educational levels. Technical reports of the symposia were published by AAAI Press.

Cognitive Informatics

2011 ◽  
pp. 104-111 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Information Processing ◽  
Computer Science ◽  
Knowledge Engineering ◽  
Systems Science ◽  
Science And Engineering ◽  
Cognitive Informatics ◽  
Knowledge Based ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain

Cognitive informatics (CI) is an emerging discipline that studies the natural intelligence and internal information processing mechanisms of the brain, as well as the processes involved in perception and cognition. CI provides a coherent set of fundamental theories and contemporary mathematics that form the foundation for most information- and knowledge-based science and engineering disciplines, such as computer science, cognitive science, neuropsychology, systems science, cybernetics, software engineering, and knowledge engineering.

On the Essential Integration of Nursing and Informatics

1992 ◽  
Vol 3 (4) ◽  
pp. 797-803 ◽  
Author(s):  
Patricia Flatley Brennan ◽  
Joyce J. Fitzpatrick
Keyword(s):  
Critical Care ◽  
Information Processing ◽  
Computer Science ◽  
Nursing Practice ◽  
Knowledge Engineering ◽  
Critical Care Nurses ◽  
Human Information Processing ◽  
Decision Science ◽  
Nursing Informatics ◽  
Nursing Knowledge

This paper asserts that nursing knowledge is fundamentally inseparable from the strategies and structures that represent it. Nursing informatics comprises a new disciplinary focus that results from a blend of nursing and informatics. The technologies of informatics, communications, computer science, decision science, human information processing, and knowledge engineering, provide critical care nurses with the support necessary for contemporary nursing practice. Informatics technologies enable nurses to communicate, process knowledge in new and more efficient ways, and better understand the nature of nursing thinking

A theory of contingent business process management

2019 ◽  
Vol 25 (6) ◽  
pp. 1291-1316 ◽  
Author(s):  
Sarah Zelt ◽  
Jan Recker ◽  
Theresa Schmiedel ◽  
Jan vom Brocke
Keyword(s):  
Information Processing ◽  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
Empirical Studies ◽  
Theory Development ◽  
Process Performance ◽  
Research Strategies ◽  
Content Type

Purpose Many researchers and practitioners suggest a contingent instead of a “one size fits all” approach in business process management (BPM). The purpose of this paper is to offer a contingency theory of BPM, which proposes contingency factors relevant to the successful management of business processes and that explains how and why these contingencies impact the relationships between process management and performance. Design/methodology/approach The authors develop the theory by drawing on organizational information processing theory (OIPT) and applying an information processing (IP) perspective to the process level. Findings The premise of the model is that the process management mechanisms such as documentation, standardization or monitoring must compensate for the uncertainty and equivocality of the nature of the process that has to be managed. In turn, managing through successful adaptation is a prerequisite for process performance. Research limitations/implications The theory provides a set of testable propositions that specify the relationship between process management mechanisms and process performance. The authors also discuss implications of the new theory for further theorizing and outline empirical research strategies that can be followed to enact, evaluate and extend the theory. Practical implications The theory developed in this paper allows an alternative way to describe organizational processes and supports the derivation of context-sensitive management approaches for process documentation, standardization, monitoring, execution and coordination. Originality/value The theoretical model is novel in that it provides a contextualized view on BPM that acknowledges different types of processes and suggests different mechanisms for managing these. The authors hope the paper serves as inspiration both for further theory development as well as to empirical studies that test, refute, support or otherwise augment the arguments.

scholarly journals Failure Analysis in University and Computer Science Contexts With Data Mining

2020 ◽  
Author(s):  
Daniela De Souza Gomes ◽  
Marcos Henrique Fonseca Ribeiro ◽  
Giovanni Ventorim Comarela ◽  
Gabriel Philippe Pereira
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Failure Analysis ◽  
Computer Science ◽  
Educational Administration ◽  
Intelligent Systems ◽  
Data Set ◽  
Data Mining Techniques ◽  
Study Case ◽  
Support Students

High failure rates are a worrying and relevant problem in Brazilian universities. From a data set of student transcripts, we performed a study case for both general and Computer Science contexts, in which Data Mining Techniques were used to find patterns concerning failures. The knowledge acquired can be used for better educational administration and also build intelligent systems to support students’ decision making.

scholarly journals AN OVERVIEW OF SWARM INTELLIGENCE IN ARTIFICIAL INTELLIGENT SYSTEMS

2021 ◽  
Vol 9 (08) ◽  
pp. 673-675
Author(s):  
Kalpana C. Dalwai ◽  
Keyword(s):  
Information Processing ◽  
Problem Solving ◽  
Swarm Intelligence ◽  
Intelligent Systems ◽  
Optimization Problems ◽  
Artificial Intelligent

Swarm intelligence refers to a kind of problem-solving ability that emerges in the interactions of simple information-processing units. The concept of a swarm suggests multiplicity, stochasticity, randomness, and messiness. Advancement of technology has led to problems that are complex and more challenging.Swarm intelligence techniques were mostly developed for solving optimization problems.

Sign in / Sign up

Export Citation Format

Share Document