In Shapiro, S. (Ed.), Encyclopedia of Artificial Intelligence., Vol. 1, pp. 285-293. Wiley. Links to a variety of constraint satisfaction articles. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, Volume 25, Issue 1, January 1985, Pages 65–74 (http://www.sciencedirect.com/science/article/pii/0004370285900414). Constraint Satisfaction. Technical Report, University of British Columbia, 1985 (http://dl.acm.org/citation.cfm?id=901711). The logic of constraint satisfaction. Artificial Intelligence, Volume 58, Issues 1–3, December 1992, Pages 3–20 (http://www.sciencedirect.com/science/article/pii/000437029290003G). The complexity of constraint satisfaction revisited. Artificial Intelligence, Volume 59, Issues 1–2, February 1993, Pages 57–62 (http://www.sciencedirect.com/science/article/pii/000437029390170G). Parallel and distributed algorithms for finite constraint satisfaction problems. Proceedings of the Third IEEE Symposium on Parallel and Distributed Processing, 1991 (https://ieeexplore.ieee.org/document/218214). Hierarchical arc consistency: exploiting structured domains in constraint satisfaction problems. Computational Intelligence, Volume 1, Issue 1, pages 118–126, January 1985 (https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.1985.tb00064.x). Knowledge structuring and constraint satisfaction: the Mapsee approach. IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume:10, Issue: 6) (https://ieeexplore.ieee.org/abstract/document/9108?section=abstract). Chapter 2 – Constraint Satisfaction: An Emerging Paradigm. Foundations of Artificial Intelligence, Volume 2, 2006, Pages 13–27. Handbook of Constraint Programming (http://www.sciencedirect.com/science/article/pii/S1574652606800064).

The CLP( ℛ) programming language is defined, its underlying philosophy and programming methodology are discussed, important implementation issues are explored in detail, and finally, a prototype interpreter is described. CLP( ℛ) is designed to be an instance of the Constraint Logic Programming Scheme, a family of rule-based constraint programming languages defined by Jaffar and Lassez. The domain of computation ℛ of this particular instance is the algebraic structure consisting of uninterpreted functors over real numbers. An important property of CLP( ℛ)is that the constraints are treated uniformly in the sense that they are used to specify the input parameters to a program, they are the only primitives used in the execution of a program, and they are used to describe the output of a program. Implementation of a CLP language, and of CLP( ℛ) in particular, raises new problems in the design of a constraint-solver.

The Association for the Advancement of Artificial Intelligence held its 1991 Spring Symposium Series on March 26-28 at Stanford University, Stanford, California. This article contains short summaries of the eight symposia that were conducted: Argumentation and Belief, Composite System Design, Connectionist Natural Language Processing, Constraint-Based Reasoning, Implemented Knowledge Representation and Reasoning Systems, Integrated Intelligent Architectures, Logical Formalizations of Commonsense Reasoning, and Machine Learning of Natural Language and Ontology.

The Association for the Advancement of Artificial Intelligence held its 1991 Spring Symposium Series on March 26-28 at Stanford University, Stanford, California. This article contains short summaries of the eight symposia that were conducted: Argumentation and Belief, Composite System Design, Connectionist Natural Language Processing, Constraint-Based Reasoning, Implemented Knowledge Representation and Reasoning Systems, Integrated Intelligent Architectures, Logical Formalizations of Commonsense Reasoning, and Machine Learning of Natural Language and Ontology.

Fields 3-8 of table 1 of the survey and general results, a discussion represent purposes, specifically, to define of the four hypotheses, and two sections models (field 3), prove theorems about the at the end of the article that contain details of models (field 4), present algorithms (field 5), the survey and statistical analyses. The next analyze algorithms (field 6), present systems section (The Survey) briefly describes the 16 or architectures (field 7), and analyze them substantive questions I asked about each (field 8). These purposes are not mutually paper. One of the closing sections (An Explanation exclusive; for example, many papers that of the Fields in Table 1) discusses the present models also prove theorems about criteria for answering the survey questions the models.

It is well known that for many NPcomplete problems, such as K-Sat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P NP). This paper shows that NPcomplete problems can be summarized by at least one "order parameter", and that the hard problems occur at a critical value of such a parameter.

I concentrate on this class of design 1989) that lays out the relation problems in this article. An example of an implicit function mapping from behavior to structure), typically in many engineering devices is safety: For conducted by means of a search or exploration example, a subsystem's role might only be in the space of possible subassemblies explained as something that prevents the of components. This accent on assembly is in leakage of a potentially hazardous substance, fact the origin of the frequent suggestion that and this function might never be explicitly design is a synthetic task. Only a vanishingly design specifications will usually mention a small number of objects in this space constitute number of constraints. The distinction even satisficing, not to mention optimal, between functions and constraints is hard to solutions. What is needed to make design formally pin down; functions are constraints practical are strategies that radically shrink on the behavior or properties of the device. However, it is useful to distinguish functions Set against the view of design as a deliberative from other constraints because functions are problem-solving process is the view of the primary reason that the device is desired. Artistic creations and weigh more than..."), the process of making scientific theories are often said by their creators the artifact from its description (manufacturability to have occurred to them in this Even when a plausible solution itself (for example, "I want a design within a occurs in this way, the proposal still needs to week"), and so on.

In this article, we present Spar (simultaneous planner for assembly robots), an implemented system that reasons about high-level operational goals, geometric goals, and uncertainty-reduction goals to create task plans for an assembly robot. These plans contain manipulations to achieve the assembly goals and sensory operations to cope with uncertainties in the robot's environment. High-level goals (which we refer to as operational goals) are satisfied by adding operations to the plan using a nonlinear, constraint-posting method. Geometric goals are satisfied by placing constraints on the execution of these operations. If the geometric configuration of the world prevents this, Spar adds new operations to the plan along with the necessary set of constraints on the execution of these operations. When the uncertainty in the world description exceeds that specified by the uncertainty-reduction goals, Spar introduces either sensing operations or manipulations to reduce this uncertainty to acceptable levels. If Spar cannot find a way to sufficiently reduce uncertainties, it augments the plan with sensing operations to be used to verify the execution of the action and, when possible, posts possible error-recovery plans, although at this point, the verification operations and recovery plans are predefined.

This paper describes the historical evolution of the isis/opis/cortes family of knowledge-based scheduling systems developed at Carnegie Mellon University. At the core of the isis/opis/cortes family is an approach to automatic scheduling that provides a framework for incorporating the full range of real-world constraints. Given the conflicting nature of the domain's constraints, the problem differs from typical constraint satisfaction problems. One cannot rely solely on propagation techniques to arrive at an acceptable solution, since no feasible solution may exist. Rather, constraints must be selectively relaxed in which case the problem solving strategy becomes one of finding a solution that best satisfies the constraints.

Proc. AAAI-90.