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Building a Constraint Solver that Learns. In Proceedings of the AAAI
Building a Constraint Solver that Learns. In Proceedings of the AAAI

... architecture for the rapid development of expertise (Epstein, 1992). To produce an adaptive, robust problem solver, FORR exploits many techniques observable in human learners. FORR itself is domain independent; a FORR-based application requires a set of domain-specific state representations and heur ...
Abstraction in Situation Calculus Action Theories
Abstraction in Situation Calculus Action Theories

... each high-level primitive action to a (possibly nondeterministic) ConGolog program defined over the low-level action theory that “implements it”. Moreover, it maps each highlevel fluent to a state formula in the low-level language that characterizes the concrete conditions under which it holds. In t ...
Application of Artificial Intelligence in Finance
Application of Artificial Intelligence in Finance

... from memory for decision making. This is the principle underlying case-based reasoning technologies [4]. Case-based decision support can also analyze cases to extract patterns and discover knowledge hidden in data. Case-based information system helps to exploit data so that smarter business decision ...
s 1 - UNL CSE
s 1 - UNL CSE

... Gottlob, G., Leone, N., Scarcello, F. : On Tractable Queries and Constraints. In: 10th International Conference and Workshop on Database and Expert System Applications (DEXA 1999). (1999) Decther, R.: Constraint Processing. Morgan Kaufmann (2003) Freuder, E.C.: A Sufficient Condition for Backtrack-B ...
Artificial Intelligence and Expert Systems
Artificial Intelligence and Expert Systems

... A procedure developed by Newell and Simon [1973] from their Logic Theory Machine – ...
Two Paradigms Are Better Than One, And Multiple
Two Paradigms Are Better Than One, And Multiple

... whose intelligence is at the level of ants. A society of such agents can cooperate in defending the colony, searching for food, and caring for the eggs and larvae. But no one has shown how a colony of ants could understand language or do complex reasoning and planning. Complex rational agents and s ...
The Promise and Perils of Artificial Intelligence
The Promise and Perils of Artificial Intelligence

... • Robotics and AI are cousins. • There is some intelligence required to recognize and manipulate objects; • There is intelligence required to move in a new environment after identifying its own location a target place and planning the movement ...
Connecting Conscious and Unconscious - Axel Cleeremans
Connecting Conscious and Unconscious - Axel Cleeremans

... input/output systems. Knowledge (either “programs” or “data”) is represented symbolically. Bates and Elman (1993) dubbed this perspective on cognition “The First Computer Metaphor of Cognition” and characterized it as follows (p. 630): At its core, the serial digital computer is a machine that manip ...
What are Expert Systems?
What are Expert Systems?

... • Provide rigid control and top-down-design. • Not efficient for directly implementing expert systems. Expert Systems: Principles and Programming, Fourth Edition ...
CS6659-ARTIFICIAL INTELLIGENCE
CS6659-ARTIFICIAL INTELLIGENCE

... Knowing about the current state of the environment is not always enough to decide what to do. For example, at a road junction, the taxi can turn left, turn right, or go straight on. The correct decision depends on where the taxi is trying to get to. In other words, as well as a current state descrip ...
View PDF - Advances in Cognitive Systems
View PDF - Advances in Cognitive Systems

... Although such agents produce intelligent behavior with less human knowledge engineering than before, there remains a non-trivial element of knowledge engineering in the encoding of the prior domain knowledge. Such prior knowledge includes encoding how to extract a coefficient from an expression, how ...
Tarek R. Besold, Kai
Tarek R. Besold, Kai

Rīgas Tehniskā universitāte
Rīgas Tehniskā universitāte

Document
Document

x. reference
x. reference

... ALBERT GERARD BOULANGER B.S., University or Florida., 1978 THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science in the Graduate College of the University, Ulinois at ...
Robotic Process Automation
Robotic Process Automation

... that form a large chunk of BPM jobs in human resources, finance and accounting, ...
Introduction to Jess: Rule Based Systems In Java
Introduction to Jess: Rule Based Systems In Java

... When to Use Rule-Based Systems • Problem Domain = narrow, wellunderstood domain theory • Knowledge Representation = facts and rules • Output = recommendation • Explanation = rule firing trace • Learning Ability = generally no (but…) ...
CS 561a: Introduction to Artificial Intelligence
CS 561a: Introduction to Artificial Intelligence

... • Provides the most general view of AI because it includes: ...
Expert Systems
Expert Systems

... Roles of an expert system • Assisting expert – Assisting an expert is the most commonly found role of an ES. – The goal is to aid an expert in a routine tasks to increase productivity, or to aid in managing a complex situation by using an expert system that may itself draw on experience of other (p ...
Decision Making and Business Processes
Decision Making and Business Processes

SRM UNIVERSITY FACULTY OF ENGINEERING AND
SRM UNIVERSITY FACULTY OF ENGINEERING AND

... Introduction To Knowledge Engineering: Introduction To Knowledge Engineering : The Human Expert And An Artificial Expert – Knowledge Base And Inference Engine – Knowledge Acquisition And Knowledge Representation Sessi on No. ...
Rule-Based Expert Systems
Rule-Based Expert Systems

... declarative manner that is close to how experts explain their own reasoning. ...
A Robot Exploration and Mapping Strategy Based on a Semantic
A Robot Exploration and Mapping Strategy Based on a Semantic

... place is based on distinctiveness of a location within its neighborhood. Their methods are most appropriate in environments where remote point-like landmarks are easily observable. Little of the literature discussing movement control strategy explicitly relates it to the topological model. Most rese ...
Deploying Softbots on the World Wide Web
Deploying Softbots on the World Wide Web

... on many of the softbots described above, for his numerous contributions to the softbots project and its vision; he cannot be held responsible for the polemic tone and subversive methodological ideas of this piece. I would also like to thank my co-softbotists David Christianson, Bob Doorenbos, Marc F ...
A Comparative Illustration of AI Planning-based
A Comparative Illustration of AI Planning-based

... Kepler [Altintas et al. 2004] are examples of this approach. When the scale or complexity of the WSC problem increases, automatic composition gets more desirable. The automatic composition approach can be complementary to the manual approach such that a few feasible workflows generated from the auto ...
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Ecological interface design

Ecological interface design (EID) is an approach to interface design that was introduced specifically for complex sociotechnical, real-time, and dynamic systems. It has been applied in a variety of domains including process control (e.g. nuclear power plants, petrochemical plants), aviation, and medicine.EID differs from some interface design methodologies like User-Centered Design (UCD) in that the focus of the analysis is on the work domain or environment, rather than on the end user or a specific task. The goal of EID is to make constraints and complex relationships in the work environment perceptually evident (e.g. visible, audible) to the user. This allows more of users' cognitive resources to be devoted to higher cognitive processes such as problem solving and decision making. EID is based on two key concepts from cognitive engineering research: the Abstraction Hierarchy (AH) and the Skills, Rules, Knowledge (SRK) framework.By reducing mental workload and supporting knowledge-based reasoning, EID aims to improve user performance and overall system reliability for both anticipated and unanticipated events in a complex system.
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