Contents
Decision Support Systems
Rationale
A Decision is a final product of the specific mental/cognitive process of an individual or a group of persons/organizations which is called decision making, therefore it is a subjective concept. It is a mental object and can be an opinion, a rule or a task for execution/application.
More precisely speaking, a mental object D is a decision if it was obtained by a conscious choice of only one opinion or one action (from a known set called alternatives), and it is designated for an application.
Decisions at times are influenced by factors that have almost nothing to do with surrounding reality or material facts. For example superstition may play a significant role in decision making, and so are other beliefs such as numerology, astrology, and many other forms of similar unscientific and perhaps even spiritual standards. Decisions can also be made by AI computer programs and autonomous robots. That what essentially influence a decision is the set of alternatives available for a decision-maker and choice criteria applied. These are often addressed using decision support systems
In different human activity domains, decision has many local meanings, for example,
in law,
- a decision is the outcome of a legal case
- a per curiam decision by a court with multiple judges
- a landmark decision is the outcome of a case which sets a legal precedent
- a type of European Union legislation
in statistics and economics, the attempt to make an optimal decision is called decision theory
in boxing, a result arrived at by the judges
in professional wrestling, a decision is a means by which a wrestler scores a point against his opponent.
Specially important decisions are those which have normative long-term or order/task character, and intend to influence different human communities, for example, political decisions.
Many decisions can be connected in decision trees.
Decision Making is the cognitive process leading to the selection of a course of action among alternatives. Every decision making process produces a final choice. It can be an action or an opinion. It begins when we need to do something but we do not know what. Therefore, decision making is a reasoning process which can be rational or irrational, and can be based on explicit assumptions or tacit assumptions.
Common examples include shopping, deciding what to eat, when to sleep, and deciding whom or what to vote for in an election or referendum.
Decision making is said to be a psychological construct. This means that although we can never "see" a decision, we can infer from observable behaviour that a decision has been made. Therefore, we conclude that a psychological event that we call "decision making" has occurred. It is a construction that imputes commitment to action. That is, based on observable actions, we assume that people have made a commitment to affect the action.
Structured rational decision making is an important part of all science-based professions, where specialists apply their knowledge in a given area to making informed decisions. For example, medical decision making often involves making a diagnosis and selecting an appropriate treatment. Some research using naturalistic methods shows, however, that in situations with higher time pressure, higher stakes, or increased ambiguities, experts use intuitive decision making rather than structured approaches, following a recognition primed decision approach to fit a set of indicators into the expert's experience and immediately arrive at a satisfactory course of action without weighing alternatives.
Due to the large number of considerations involved in many decisions, computer-based decision support systems have been developed to assist decision makers in considering the implications of various courses of thinking. They can help reduce the risk of human errors.
- Decision making style
- Cognitive and personal biases in decision making
- Cognitive neuroscience of decision making
- Decision making in groups
- Decision making in one's personal life
- Decision making in healthcare
- Decision making models
- Path dependency
- Decision making in business and management
- Decision-makers and influencers
- Styles and methods of decision making
- Decision making software
- References
Today's Videos
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Teaching and Learning Resources
Introduction to Decision Support Systems. Decisions and Decision Makers. Decision in the Organization
Decision Support Systems are a class of computer-based information systems including knowledge based systems that support decision making activities.
- Definitions
- Taxonomies
- Architectures
- Applications
- Characteristics and Capabilities of DSS
- References
- Department of Social and Decision Sciences - Carnegie Mellon University
- http://www.elsevier.com/
- wps/find/journaldescription.cws_home/505540/
- description#description
- DSSAT4 - the University of Hawaii.
Enterprise Decision Management, commonly abbreviated "EDM", entails all aspects of managing automated decision design and deployment that an organization uses to manage its interactions with customers, employees and suppliers. Computerization has changed the way organizations are approaching their decision-making because it has enabled "information-based decisions" - decisions based on analysis of historical behavioral data, prior decisions, and their outcomes.
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Enterprise Decision Management is described by the Cutter Consortium: "Enterprise decision management (EDM) is emerging as an important discipline, due to an increasing need to automate high-volume decisions across the enterprise and to impart precision, consistency, and agility in the decision-making process." and goes on to say that EDM is implemented "via the use of rule-based systems and analytic models for enabling high-volume, automated decision making." [1] Organizations seek to improve their Decision Yield (the value created through each decision) by deploying business processes and software solutions that better manage the tradeoffs between precision, consistency, agility, speed, and cost of decision-making within organizations. The concept of Decision Yield focuses on five key attributes of decision-making: more targeted decisions (Precision); in the same way, over and over again (Consistency); while being able to adapt “on-the-fly” (Agility) while reducing cost and improving speed, is an overall metric for how well an organization is making a particular decision. Decision Yield is described in this 'Harvard Business Review' article Little Decisions Add Up. |
Organizations are adopting EDM technology and business processes because they need a higher return from previous infrastructure investments, are dealing with increasing business decision complexity, face competitive pressure for more sophisticated decisions and because increasingly short windows of competitive advantage means that the speed of business is outpacing speed of Information Technology to react.
- Fair Isaac's EDM page
- Fair Isaac's EDM Blog
- ebizQ Decision Management Blog
- B-eye Blogs Enterprise Decision Management Blog
- Chordiant Decision Management
Modeling Decision Processes. Group Decision Support and Groupware Technologies.
Tutorials
Readings
A Decision Method is an axiomatic system that contains at least one action axiom.
Formulation is the first and often most challenging stage in using formal decision methods (and in decision analysis in particular). The objective of the formulation stage is to develop a formal model of the given decision.
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Evaluation is the second and most algorithmic stage in using formal decision methods. The objective of the evaluation stage is to produce a formal recommendation (and its associated sensitivities) from a formal model of the decision situation. Appraisal is the third and last stage in using formal decision methods. The objective of the appraisal stage is for the decision maker to develop insight into the decision and determine a clear course of action. Much of the insight developed in this stage results from exploring the implications of the formal decision model developed during the formulation stage (i.e., from mining the model). Central to these implications is the formal recommendation for action calculated during the evaluation stage. Other implications include various forms of sensitivity of the recommendation to selected variables in the model. Additional insight is obtained by exposing key aspects of the reasoning that led to the formal decision model (i.e., by justifying the model). Possible actions following the appraisal stage include implementing the recommended course of action, revising the formal model and reevaluating it, or abandoning the analysis and doing something else. |
Justifying a decision model is the action of exploring and explaining the reasoning that led to the formulation of a particular decision model.
Mining a decision model is the action of extracting information (e.g., sensitivity, value of information, and value of control) from a given decision model.
See also
In operations research, specifically in decision analysis, a Decision Tree is a decision support tool that uses a graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. A decision tree is used to identify the strategy most likely to reach a goal. Another use of trees is as a descriptive means for calculating conditional probabilities.
In data mining and machine learning, a decision tree is a predictive model; that is, a mapping from observations about an item to conclusions about its target value. More descriptive names for such tree models are classification tree or reduction tree. In these tree structures, leaves represent classifications and branches represent conjunctions of features that lead to those classifications [1]. The machine learning technique for inducing a decision tree from data is called decision tree learning, or (colloquially) decision trees.
Executive
Information Systems. Expert
Systems and Artificial Intelligence
Tutorials
Readings
An Executive Information System (EIS) is a computer-based system intended to facilitate and support the information and decision making needs of senior executives by providing easy access to both internal and external information relevant to meeting the strategic goals of the organization. It is commonly considered as a specialized form of Decision Support System (DSS).
The emphasis of EIS is on graphical displays and easy-to-use user interfaces. They offer strong reporting and drill-down capabilities. In general, EIS are enterprise-wide DSS that help top-level executives analyze, compare, and highlight trends in important variables so that they can monitor performance and identify opportunities and problems. EIS and data warehousing technologies are converging in the marketplace.
For the past few years, the term EIS has been little-used. A more common term used to describe this domain area is Business Intelligence (sub areas of reporting, analyitics, dashboards).
- History of EIS
- EIS Components
- EIS Applications
- Advantages and Disadvantages of EIS
- Future Trends in EIS
- References
The term Artificial Intelligence (AI) was first used by John McCarthy who considers it to mean "the science and engineering of making intelligent machines".[1] It can also refer to intelligence as exhibited by an artificial (man-made, non-natural, manufactured) entity. The terms strong and weak AI can be used to narrow the definition for classifying such systems. AI is studied in overlapping fields of computer science, psychology, philosophy, neuroscience and engineering, dealing with intelligent behavior, learning and adaptation and usually developed using customized machines or computers.
Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, natural language, speech and facial recognition. As such, the study of AI has also become an engineering discipline, focused on providing solutions to real life problems, knowledge mining, software applications, strategy games like computer chess and other video games. One of the biggest difficulties with AI is that of comprehension. Many devices have been created that can do amazing things, but critics of AI claim that no actual comprehension by the AI machine has taken place.
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- AI at the Open Directory Project (suggest site)
- AI-Tools, the Open Source AI community homepage
- Artificial Intelligence Directory, a directory of Web resources related to artificial intelligence
- The Association for the Advancement of Artificial Intelligence
- Freeview Video 'Machines with Minds' by the Vega Science Trust and the BBC/OU
- Heuristics and artificial intelligence in finance and investment
- John McCarthy's frequently asked questions about AI
- Generation5 - Large artificial intelligence portal with articles and news.
- Mindmakers.org, an online organization for people building large scale A.I. systems
- Ray Kurzweil's website dedicated to AI including prediction of future development in AI
Knowledge Engineering and Acquisition. Machines That Can Learn
Tutorials
Readings
Knowledge Engineering (KE) refers to the building, maintaining and development of knowledge-based systems. It has a great deal in common with software engineering, and is related to many computer science domains such as artificial intelligence, databases, data mining, expert systems, decision support systems and geographic information systems. Knowledge engineering is also related to mathematical logic, as well as strongly involved in cognitive science and socio-cognitive engineering where the knowledge is produced by socio-cognitive aggregates (mainly humans) and is structured according to our understanding of how human reasoning and logic works.
Various activities of KE specific for the development of a knowledge-based system:
- Assessment of the problem
- Development of a knowledge-based system shell/structure
- Implementation of the structured knowledge into knowledge-bases
- Acquisition and structuring of the related information, knowledge and specific preferences
- Testing and validation of the inserted knowledge
- Integration and maintenance of the system
- Revision and evaluation of the system.
Being still more art than engineering, KE is not as neat as the above list in practice. The phases overlap, the process might be iterative, and many challenges could appear. Recently, emerges meta-knowledge engineering as a new formal systemic approach to the development of an unified knowledge and intelligence theory.
Knowledge Engineering Principles
Since the mid-1980s, knowledge engineers have developed a number of principles, methods and tools that have considerably improved the process of knowledge acquisition and ordering. Some of the key principles are summarized as follows:
- Knowledge engineers acknowledge that there are different types of knowledge, and that the right approach and technique should be used for the knowledge required.
- Knowledge engineers acknowledge that there are different types of experts and expertise, such that methods should be chosen appropriately.
- Knowledge engineers recognize that there are different ways of representing knowledge, which can aid the acquisition, validation and re-use of knowledge.
- Knowledge engineers recognize that there are different ways of using knowledge, so that the acquisition process can be guided by the project aims (goal-oriented).
- Knowledge engineers use structured methods to increase the efficiency of the acquisition process.
Views of Knowledge Engineering
There are two main views to knowledge engineering:
Transfer View- This is the traditional view. In this view, the assumption is to apply conventional knowledge engineering techniques to transfer human knowledge into artificial intelligent systems.
Modeling View- This is the alternative view. In this view, the knowledge engineer attempts to model the knowledge and problem solving techniques of the domain expert into the artificial intelligent system.
Some methodologies to support the development of knowledge or intelligence-based systems:
See also
- Knowledge representation
- Knowledge management
- Knowledge level modeling
- CommonKADS Methodology
- Clinical decision support system
- Connectionist expert system
- Systemics
- Cognitive science
- Socio-cognitive engineering
The Data Warehouse. Data Mining and Data Visualization. Designing and Building the Data Warehouse
Tutorials
Readings
A Data Warehouse is the main repository of the organization's historical data, its corporate memory. For example, an organization would use the information that's stored in its data warehouse to find out what day of the week they sold the most widgets in May 1992, or how employee sick leave the week before Christmas differed between California and Quebec from 2001-2005. In other words, the data warehouse contains the raw material for management's decision support system. The critical factor leading to the use of a data warehouse is that a data analyst can perform complex queries and analysis (such as data mining) on the information without slowing down the operational systems.
While operational systems are optimized for simplicity and speed of modification (online transaction processing, or OLTP) through heavy use of database normalization and an entity-relationship model, the data warehouse is optimized for reporting and analysis (on line analytical processing, or OLAP). Frequently data in data warehouses is heavily denormalised, summarised and/or stored in a dimension-based model but this is not always required to achieve acceptable query response times.
More formally, Bill Inmon (one of the earliest and most influential practitioners) defined a data warehouse as follows:
Subject-oriented, meaning that the data in the database is organized so that all the data elements relating to the same real-world event or object are linked together;
Time-variant, meaning that the changes to the data in the database are tracked and recorded so that reports can be produced showing changes over time;
Non-volatile, meaning that data in the database is never over-written or deleted, once committed, the data is static, read-only, but retained for future reporting;
Integrated, meaning that the database contains data from most or all of an organization's operational applications, and that this data is made consistent.
- History of data warehousing
- Data warehouse architecture
- Data storage methods
- Advantages of using data warehouse
- Concerns in using data warehouses
- Business Intelligence (BI)
- Business Performance Management (BPM)
- Data integration
- Data mart
- Data mining
- Database Management System (DBMS)
- Executive Information System (EIS)
- Extract, transform, and load (ETL)
- Intelligent document
- Manufacturer Purchase Order Numbers (MANPONS)
- Master Data Management (MDM)
- On Line Analytical Processing (OLAP)
- On Line Transaction Processing (OLTP)
- Operational Data Store (ODS)
- Shadow system
- Snowflake schema
- Star schema
- Bitmap index
William H. Inmon, Richard D. Hackathorn: Using the Data Warehouse, John Wiley & Son's, ISBN 0-471-05966-8
Pyle, Dorian. Business Modeling and Data Mining. Morgan Kaufmann, 2003. ISBN 1-55860-653-X
Ralph Kimball, Margy Ross: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Second Edition), John Wiley & Sons, ISBN 0-471-20024-7
Stephen Haag, Maeve Cummings, Donald J. McCubbery, Alain Pinsonneault, Richard Donvan:Managements Information System for the Information Age, Third Canadian Edition, McGraw-Hill Ryerson, ISBN 0-07-095569-7
ODP - Data Warehouse Resources
Data Mining (DMM), also called Knowledge-Discovery in Databases (KDD) or Knowledge-Discovery and Data Mining, is the process of automatically searching large volumes of data for patterns using tools such as classification, association rule mining, clustering, etc. Data mining is a complex topic and has links with multiple core fields such as computer science and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning and pattern recognition.
- Example
- Use of the term
- Misuse of the term
- Related terms
- Data dredging
- Privacy concerns
- Combinatorial game data mining
- Notable uses of data mining
- Structured Data Mining
- Unstructured Data Mining
- Induction algorithms
- Dimensionality reduction
- Application areas
- Software
- References
- General References
- Books
Scientific- (or data-), and Information visualization are branches of computer graphics and user interface design that are concerned with presenting data to users, by means of interactive or animated digital images. The goal of this area is usually to improve understanding of the data being presented. For example, scientists interpret potentially huge quantities of laboratory or simulation data or the results from sensors out in the field to aid reasoning, hypothesis building and cognition. The field of data mining offers many abstract visualizations related to these visualization types. They are active research areas, drawing on theory in information graphics, computer graphics, human-computer interaction and cognitive science.
- Usage and distinction of the terms
- Overview
- In engineering
- In the medical and life sciences
- References
The Systems Perspective of a DSS. Designing and Building Decision Support Systems
Tutorials
Readings
System (from Latin systēma, in turn from Greek σύστημα systēma) is a set of entities, real or abstract, comprising a whole where each component interacts with or is related to at least one other component and they all serve a common objective. Any object which has no relation with any other element of the system is not part of that system but rather of the system environment. A subsystem then is a set of elements, which is a system itself, and a part of the whole system.
Every division or aggregation of real entities into systems is arbitrary, therefore it is a subjective abstract concept.
The scientific research field which is engaged in the transdisciplinary study of universal system-based properties of the world is general system theory, systems science and recently systemics. They investigate the abstract properties of the matter and mind, their organization, searching concepts and principles which are independent on the specific domain, independent of their substance, type, or spatial or temporal scales of existence.
- Systems theory
- Complex systems
- Complexity and organization
- Computer system
- Cybernetics
- Chaos theory
- Donella Meadows' twelve leverage points to intervene in a system
- Eco-map
- General semantics
- Geopolitical system
- Holarchy
- Meta-systems
- Morphological analysis
- Open Systems Interconnection
- Service system
- Socio-technical systems
- Sociocybernetics
- Solar system
- Systemic coaching
- Systems intelligence
- Systems ecology
- System of Systems
- System of Systems Engineering
- Viable System Model
- A system in human anatomy
- References
The term Information System has the following meanings:
1. In information systems, an information system consists of three components: human, task, application system. In this view, information is defined in terms of the three levels of semiotics. Data which can be automatically processed by the application system corresponds to the syntax-level. In the context of an individual who interprets the data they become information, which correspond to the semantic-level. Information becomes knowledge when an individual knows (understands) and evaluates the information (e.g., for a specific task). This corresponds to the pragmatic-level.
2. In general systems theory, an information system is a system, automated or manual, that comprises people, machines, and/or methods organized to collect, process, transmit, and disseminate data that represent user information.
3. In rough set theory, an information system is an attribute-value system.
4. In telecommunications, an information system is any telecommunications and/or computer related equipment or interconnected system or subsystems of equipment that is used in the acquisition, storage, manipulation, management, movement, control, display, switching, interchange, transmission, or reception of voice and/or data, and includes software, firmware, and hardware.
(Federal Standard 1037C, MIL-STD-188, and National Information Systems Security Glossary)
- In computer security, an information system is described by five objects (Canal 2004):
Structure:
- Repositories, which hold data permanent or temporarily, such as buffers, RAM, hard disks, cache, etc.
- Interfaces, which exchange information with the non-digital world, such as keyboards, speakers, scanners, printers, etc.
- Channels, which connect repositories, such as buses, cables, wireless links, etc. A Network is a set of logical or physical channels.
Behaviour:
- Services, which provide value to users or to other services via messages interchange.
- Messages, which carries a meaning to users or services.
In the mathematical area of domain theory, a Scott information system (after its inventor Dana Scott) is a mathematical structure that provides an alternative representation of Scott domains and, as a special case, algebraic lattices.
Information systems - for scholarly information on this subject one should refer to the works of Peter Checkland developer of SSM (Soft system metodology) and one of the leading information systems theorists and consultant's.
See also
Implementing and Integrating Decision Support Systems
Tutorials
Readings
Decision Analysis (DA) is the discipline comprising the philosophy, theory, methodology, and professional practice necessary to address important decisions in a formal manner.
Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing the important aspects of a decision situation, for prescribing the recommended course of action by applying the maximum expected utility action axiom to a well-formed representation of the decision, and for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker and other stakeholders.
The term decision analysis was coined in 1964 by Ronald A. Howard, who since then, as a professor at Stanford University, has been instrumental in developing much of the practice and professional application of DA.
Graphical representation of decision analysis problems commonly use influence diagrams and decision trees. Both of these tools represent the alternatives available to the decision maker, the uncertainty they face, and evaluation measures representing how well they achieve their objectives in the final outcome. Uncertainties are represented through probabilities and probability distributions. The decision maker's attitude to risk is represented by utility functions and their attitude to trade-offs between conflicting objectives can be made using multi-attribute value functions or multi-attribute utility functions (if there is risk involved).
Decision analytic methods are used in a wide variety of fields, including business (planning, marketing, and negotiation), environmental remediation, health care research and management, energy exploration, litigation and dispute resolution, etc. However, there is growing concern that these tools do not lead to real improvement in decision making. Some authors [Klien G, 2003. The Power of Intuition. Doubleday, New York.] point out that people don't make decisions this way and that the intuitive style of decision making needs to replace the disaggregated approaches commonly used by most decision analysts. Decision analysts point out that their approach is prescriptive, providing a prescription of what actions to take based on sound logic, rather than a descriptive approach, describing the flaws in the way people do make decisions. Overall a good decision maker should understand both approaches, understanding how people go wrong in making decisions and providing a sound basis for them to make better decisions.
See also
- Decision tree
- Choice
- Decision analysis cycle
- Decision model
- Decision theory
- Decision support
- Influence diagram
- Maximum expected utility
- Multi-criteria decision analysis (MCDA)
- INFORMS Institute for Operations Research and the Management Sciences
External links
Decision Analysis, a journal of the Institute for Operations Research and the Management Sciences
Decision Analysis Society, a subdivision of the Institute for Operations Research and the Management Sciences specializing in Decision Analysis
Decision Analysis in Health Care Online course from George Mason University providing free lectures and tools for decision analysis modeling in health care settings.
Creative Decision Making and Problem Solving
Tutorials
Readings
Creativity (or creativeness) is a mental process involving the generation of new ideas or concepts, or new associations between existing ideas or concepts. From a scientific point of view, the products of creative thought (sometimes referred to as divergent thought) are usually considered to have both originality and appropriateness. An alternative, more everyday conception of creativity is that it is simply the act of making something new. Although intuitively a simple phenomenon, it is in fact quite complex. It has been studied from the perspectives of behavioural psychology, social psychology, psychometrics, cognitive science, artificial intelligence, philosophy, history, economics, design research, business, and management, among others. The studies have covered everyday creativity, exceptional creativity and even artificial creativity. Unlike many phenomena in science, there is no single, authoritative perspective or definition of creativity. Unlike many phenomena in psychology, there is no standardized measurement technique. Creativity has been attributed variously to divine intervention, cognitive processes, the social environment, personality traits, and chance ("accident," "serendipity"). It has been associated with genius, mental illness and humour. Some say it is a trait we are born with; others say it can be taught with the application of simple techniques. |
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Although popularly associated with art and literature, it is also an essential part of innovation and invention and is important in professions such as business, economics, architecture, industrial design, science and engineering.
Despite, or perhaps because of, the ambiguity and multi-dimensional nature of creativity, entire industries have been spawned from the pursuit of creative ideas and the development of creativity techniques. This mysterious phenomenon, though undeniably important and constantly visible, seems to lie tantalizingly beyond the grasp of scientific investigation.
"Creativity, it has been said, consists largely of re-arranging what we know in order to find out what we do not know." George Kneller
- Definitions of creativity
- Distinguishing between creativity and innovation
- History of the term and the concept
- Creativity in psychology & cognitive science
- Psychological examples from science and mathematics
- Creativity and intelligence
- Neurobiology of creativity
- Creativity and mental health
- Measuring Creativity
- Creativity in various contexts
- Fostering creativity
- Enhancing the Creative Process with New Technologies
- Social attitudes to creativity
- Notes
- References
- Creativity techniques
- Creative problem solving
- Innovation
- Design
- Industrial design
- Creative engineering
- Flow
- Intelligence (trait)
- Artificial creativity
- Educational psychology
The following terms are sometimes used interchangeably with creativity, although each has slightly different meanings: creative problem solving, invention, ideation, ingenuity, imagination, inspiration, intuition, insight, originality.
Essays:
Other articles:
Intelligent Software Agents, Bots, Delegation, and Agency
Tutorials
Readings
In computer science, a Software Agent is a piece of software that acts for a user or other program in a relationship of agency[1]. Such "action on behalf of" implies the authority to decide when (and if) action is appropriate. The idea is that agents are not strictly invoked for a task, but activate themselves.
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Related and derived concepts include intelligent agents (in particular exhibiting some aspect of Artificial Intelligence, such as learning and reasoning), autonomous agents (capable of modifying the way in which they achieve their objectives), distributed agents (being executed on physically distinct machines), multi-agent systems (distributed agents that do not have the capabilities to achieve an objective alone and thus must communicate), and mobile agents (agents that can relocate their execution onto different processors). |
- Is it an Agent, or just a Program? - A Taxonomy for Autonomous Agents
- Foundations of Software Agent Technology
- Foundation for Intelligent Physical Agents
- Software Agent Technology List - an extensive and maintained collection of links
- European Co-ordination Action for Agent Based Computing
- Legal Aspects of Software Agents
- JADE - Java Agent DEvelopment Framework
- A Methodology for the Development of Multi-Agent Systems using JADE
- JACK by Agent Oriented Software
- Footnotes
- References
- Further reading
- Action selection
- Agent architectures
- Agent based model
- Agent environments
- GNUBrain - Implementation of a multi agent framework (GPL)
Decision Support in the Twenty-First Century
Tutorials
Readings
Virtual Reality (VR) is a technology which allows a user to interact with a computer-simulated environment, be it a real or imagined one. Most current virtual reality environments are primarily visual experiences, displayed either on a computer screen or through special stereoscopic displays, but some simulations include additional sensory information, such as sound through speakers or headphones. Some advanced, haptic systems now include tactile information, generally known as force feedback, in medical and gaming applications. Users can interact with a virtual environment or a virtual artifact (VA) either through the use of standard input devices such as a keyboard and mouse, or through multimodal devices such as a wired glove, the Polhemus boom arm, and omnidirectional treadmill. The simulated environment can be similar to the real world, for example, simulations for pilot or combat training, or it can differ significantly from reality, as in VR games. In practice, it is currently very difficult to create a high-fidelity virtual reality experience, due largely to technical limitations on processing power, image resolution and communication bandwidth. However, those limitations are expected to eventually be overcome as processor, imaging and data communication technologies become more powerful and cost-effective over time.
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- Information and commentary
- HoloVis International - World's Highest Resolution Stereoscopic Visualisation Walls & CAVE Solutions
- ImmersaView - Curved Screen Displays | High Resolution Displays | Stereoscopic Displays
- VRoot.org: Virtual Reality News/Resources by and for the VR Community
- Competence and Innovation Center for Virtual Reality
- Virtual Reality Software Solutions
- The Future of Virtual Reality
- The Web as a Digital Reflection of Reality on SSRN
- Virtual Reality (Springer) Editors-in-Chief: Daniel Ballin; Robert Macredie; John Vince; Suzanne Weghorst.
- Virtual Human Interaction Lab at Stanford Description of projects examining social interaction in VR.
- 3D Simulation, Computational Nanomechatronics Lab - CAN
- Articles on Virtual Reality from Science Daily.
- VResources: Virtual Reality News/Resources/Reviews for Virtual Reality, Simulation and 3D Visualization
- VR Lecture Materials by Professor Wayne E. Carlson, Ohio State
- Science@NASA Article:"Whatever Happened to...Virtual Reality?
- Virtual reality at Future Wiki - Speculation and overview of future developments.
- Virtual Reality Photography - Virtual Reality Photos, Austria.
- Atlantis Cyberspace - turn-key virtual reality system for military trainning.
- The International Society for Haptics
- Haptics-L: The mailing list for the international haptics community
- Haptics-L Newslog The latest news and views on haptics, by Gabriel Robles-De-La-Torre
- The Cutting Edge of Haptics An article in MIT's Technology review by Duncan Graham-Rowe.
- Visbox, Inc. - immersive 3D displays and turn-key VR systems.
- Next generation flight simulators
- [2] - amiga history/virtuality.
- [3] - electrorheological tactile displays
- [4] - Mechatronics Research Unit - virtual reality labs
- [5] botopia™ - utopia for virtual reality, A.I. and chatbots
Recommended Text
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Decision
Support Systems, 2/E George M. Marakas, Indiana University ISBN:
0-13-092206-4
Check the availability and buy your books from our Bookshop. |
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