Research on constraints and agents is emerging at the intersection of the communities studying constraint computation and software agents. Constraint-based reasoning systems can be enhanced by using agents with multiple problem-solving approaches or diverse problem representations. The constraint computation paradigm can be used to model agent consultation, cooperation, and competition. An interesting theme in agent interaction, which is studied here in constraint-based terms, is confronting ignorance: the agent's own ignorance or its ignorance of other agents. On the one hand, agent behavior, for example, negotiation, can be modeled as constraint satisfaction and optimization. On the other hand, agents can be used to accomplish constraint satisfaction and optimization, for example, to solve distributed scheduling problems. Agents offer opportunities to apply the constraint computation paradigm and present challenges to extend the paradigm. Constraint computation provides a general ...

We report on random stimuli generation for hardware verification at IBM as a major application of various artificial intelligence technologies, including knowledge representation, expert systems, and constraint satisfaction. For more than a decade we have developed several related tools, with huge payoffs. Research and development around this application are still thriving, as we continue to cope with the everincreasing complexity of modern hardware systems and demanding business environments. BM estimates that it has saved more than $100 million during the last decade in direct development costs and reduced time to market by using artificial intelligence (AI) technology for the verification of its processors and systems. The technology is used to generate tests, or stimuli, for simulating hardware designs prior to their casting in silicon.

To effectively generate a fast and consistent apartment construction project network, Hyundai Engineering and Construction and Korea Advanced Institute of Science and Technology developed a case-and constraint-based project-planning expert system for an apartment domain. The reason we chose the case-and constraintbased approach is intuitive. Second, during system development, crosschecking of cases with constraints improves the quality of both of them. Through the crosschecking process, the system developers can refine the previous cases to the high-quality referential cases and simultaneously validate and verify the domain constraints. In the area of project management, there has been a lot of research and development of network-based project-planning methods and management techniques, assuming that a project network is given to the project manager (Bent and Thumann 1994).

A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions.

Artificial intelligence (AI), on the 50th anniversary of its naming, is an autonomous discipline. The field has an established record of success, as exemplified by three recent achievements presented at AAAI-06/IAAI-06. It is now mature enough to collaborate productively with its sister disciplines, realizing the dream of ubiquitous computational intelligence. AI, a field still young as sciences go, is golden in achievement and promise. The 50th anniversary of the naming of our field, at Dartmouth College in 1956, is a time for reminiscence, celebration, and prognostication.

Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge describes 15 years of research in the qualitative physics field of AI by the author and his collaborators. Qualitative physics seeks to automate human reasoning about the physical world. The original focus was on the commonsense reasoning that underlies everyday life, such as cooking with stoves, pouring coffee, parking cars, crossing streets, and playing ball. Recent work focuses on expert reasoning about scientific and engineering domains, including circuits, thermodynamics, power plants, chemical plants, and botany. Qualitative physics hypothesizes that commonsense reasoning and expert reasoning are similar enough to justify a unified treatment.

Artificial Intelligence (AI) is the study of computer science focusing on developing software or machines that exhibit human intelligence. This article is about How to start learning Artificial Intelligence in Six Easy Steps which will give you a comprehensive guide that you can use as a starting point towards learning artificial intelligence. AI is used to solve real-world problems including search, games, machine learning, logic, understanding natural language, computer vision, expert systems, heuristic classification, constraint satisfaction problems etc. We can divide AI into 3 different categories based on it's capabilities: The idea behind Strong AI is that the machines could represent human minds in the future. If that is the case, those machines will have the ability to reason, think and do all functions that a human is capable of doing.

The American Association for 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. For example, in rhetoric, the groundswarrant-claim model has been used to analyze the structure of arguments. In psycholinguistics, researchers have analyzed the discourse structure of expository text by applying theories of discourse and schema coherency. In the field of logic, the emphasis has been on establishing axiomatic systems for deducing consistent beliefs.

MiniZinc provides a solver-independent modeling language that is now supported by constraint-programming solvers, mixed integer programming solvers, SAT and SAT modulo theory solvers, and hybrid solvers. Every year since 2008 we have run the MiniZinc Challenge, which compares and contrasts the different strengths of different solvers and solving technologies on a set of MiniZinc models. Here we report on what we have learned from running the competition for 6 years. MiniZinc is high level enough to express most combinatorial optimization problems easily and in a largely solverindependent way; for example, it supports sets, arrays, and user-defined predicates, some overloading, and some automatic coercions. However, MiniZinc is low level enough that it can be mapped easily onto many solvers.

There is a fundamental mismatch between the computational basis of spreadsheets and our knowledge of the real world. In spreadsheets, numeric data are represented as exact numbers and their mutual relations as functions, whose values (output) are computed from given argument values (input). However, in the real world, data are often inexact and uncertain in many ways, and the relationships, that is, constraints, between input and output are far more complicated. This article shows that interval constraint solving, an emerging AIbased technology, provides a more versatile and useful foundation for spreadsheets. The new computational basis is 100-percent downward compatible with the traditional spreadsheet paradigm.