4 edition of Constraint Satisfaction Techniques for Agent-Based Reasoning (Whitestein Series in Software Agent Technologies and Autonomic Computing) found in the catalog.
March 24, 2005
by Birkhäuser Basel
Written in English
|The Physical Object|
|Number of Pages||157|
Techniques in Artificial Intelligence. This note provides an introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or represent the entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. CSPs are the subject of intense research in both artificial .
Introduction to Artificial Intelligence by Cristina Conati. This note provides an introduction to the field of artificial intelligence. Major topics covered includes: reasoning and representation, search, constraint satisfaction problems, planning, logic, reasoning under uncertainty, and planning under uncertainty. Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how.
This paper describes our preliminary results in applying constraint satisfaction techniques in a system we call TRANS-FORM for designing automatic automobile power transmissions. The work is being conducted in collaboration with the Ford Motor Company Advanced Transmission Design Department in Livonia, by: Using Constraint Satisfaction Techniques to Check Hamiltonicity for “Hard” Graphs? Bernd Schroder¨ Bernd Schroder¨ Department of Mathematics, The University of Southern Mississippi Using Constraint Satisfaction Techniques to Check Hamiltonicity for “Hard” Graphs?
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About this book Constraint satisfaction problems are significant in the domain of automated reasoning for artificial intelligence. They can be applied to the modeling and solving of a wide range of combinatorial applications such as planning, scheduling and resource sharing in a variety of practical domains such as transportation, production, supply-chains, network management and human resource : Birkhäuser Basel.
Constraint satisfaction problems are significant in the domain of automated reasoning for artificial intelligence. They can be applied to the modeling and solving of a wide range of combinatorial applications such as planning, scheduling and resource sharing in a variety of practical domains such as transportation, production, supply-chains, network management and human resource management.
Constraint-based reasoning is used to solve a wide field of problems, and recently constraint techniques have been incorporated into logic programming languages, yielding a whole new field of research and application: constraint logic programming.
Constraint satisfaction techniques have become part of almost all introductory books on AI. This Cited by: Constraint satisfaction techniques for agent-based reasoning.
[Nicoleta Neagu] -- "In this book we study new techniques for solving constraint satisfaction problems, with a special focus on solution adaptation applied to agent reasoning.
Constraint Satisfaction is unique and bound to inspire new synergies between Databases and Constraint Processing. The depth and rigor at which advanced topics are addressed (e.g., advanced consistency methods, tree decomposition techniques, and temporal reasoning networks) are a remarkable achievement, possible only given the wealthCited by: Constraint-Based Reasoning presents current work in the field at several levels: theory, algorithms, languages, applications, and hardware.
Constraint-based reasoning has connections to a wide variety of fields, including formal logic, graph theory, relational databases, combinatorial algorithms, operations research, neural networks, truth maintenance, and logic programming.
Bringing artificial intelligence planning and scheduling applications into the real world is a hard task that is receiving more attention every day by researchers and practitioners from many fields. In many cases, it requires the integration of several underlying techniques like planning, scheduling, constraint satisfaction, mixed-initiative planning and scheduling, temporal reasoning.
The constraint satisfaction problems (CSPs) appear in many areas, for instance, vision, resource allocation in scheduling, and temporal reasoning. The CSPs are worth studying in isolation because it is a general problem that has unique features that can be. Constraint Loggg gic Programming • A constraint logic program is a logic program that contains constraints in the body of clauses A(X,Y): X+Y>0, B(X), C(Y) Constraints are stored in a constraint store and evaluated using a CSP Size: 1MB.
constraint satisfaction problem. • A Constraint Satisfaction Problem consists of 3 components 1. A set of variables. A set of values for each of the variables. A set of constraints between various collections of variables. We must find a value for each of the variables that satisfies all of the constraints.
Constraint-Based Reasoning presents current work in the field at several levels: theory, algorithms, languages, applications, and hardware. Formally, a constraint satisfaction problem is defined as a triple, where is a set of variables, is a set of the respective domains of values, and is a set of constraints.
Each variable can take on the values in the nonempty domain. Constraint-based reasoning is used to solve a wide field of problems, and recently constraint techniques have been incorporated into logic programming languages, yielding a whole new field of research and application: constraint logic programming.
Constraint satisfaction techniques have become part of almost all introductory books on AI. In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution to a set of constraints that impose conditions that the variables must satisfy.
A solution is therefore a vector of variables that satisfies all constraints. [Show full abstract] out that constraint satisfaction has an intimate connection with database theory: constraint-satisfaction problems can be recast as database problems and database problems can.
Request PDF | On Constraint-Based Reasoning in e-Negotiation Agents | Negotiation typically involves a number of parties with different criteria, constraints and preferences that. These variables need to be solved by constraint satisfaction methods.
These problems require a combination of heuristics and other search techniques to be solved in a reasonable amount of time. In this case, we will use constraint satisfaction techniques to solve problems on finite domains. A finite domain consists of a finite number of elements.
Representation and Reasoning Techniques particularly amenable to constraint satisfaction techniques. In addition, it was shown that program understanding as a process of constraint-based concept recovery. This book brings together the entire range of material published by the authors between and.
In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution to a set of constraints that impose conditions that the variables must satisfy.
A solution is therefore a set of values for the variables that satisfies all constraints—that is, a point in the feasible region. The techniques used in constraint satisfaction depend on the kind of. Since unary constraints are dealt with by preprocessing the domains of the affected variables, they can be ignored thereafter.
If all the constraints of a CSP are binary, the variables and constraints can be represented in a constraint graph and the constraint satisfaction algorithm can exploit the graph search techniques.
The techniques considered cover general areas such as search, rule-based systems, and truth maintenance, as well as constraint satisfaction and uncertainty management.
Specific application domains such as temporal reasoning, machine learning, and natural language are also discussed.Constraint Satisfaction Techniques for Agent-Based Reasoning pp () Interchangeability in Dynamic Environments. In: Constraint Satisfaction Techniques for Agent-Based Reasoning.
Whitestein Series in Software Agent Technologies. Birkhäuser Basel.Solving Constraint Satisfaction Problems (CSPs) using Search Alan Mackworth UBC CS – CSP 2 Janu Textbook § File Size: KB.