Mark Stefik. Planning with constraints (MOLGEN: Part 1). Artificial Intelligence, 16(2):111--140, 1981.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....to generate task commands to the next lower level BG processes. Plans and Planning Df: plan a set of subtasks and subgoals that are designed to accomplish a task or job A plan consists of one or more paths through state space from an anticipated starting state to a desired goal state [16, 17, 18]. A plan also may include a list of required resources such as tools or materials, and a set of conditions, constraints, tolerances, and priorities that must be satisfied. Df: job plan a plan for a single agent to accomplish a job In general, a job plan can be defined by a state graph wherein ....

Stefik, M. (1981) "Planning with Constraints," Artificial. Intelligence., 16, pp. 111140

....to and describing plans and actions to specify that the only actions requested will be those that are in the plan and have the hearer as agent. Since the hearer is assumed cooperative, he or she will then adopt as a goal the 3These constraints should not be confused with the constraints of Stefik [25]. xvhich are dynamicall 5 formulated during hierarchical plan generation and represent the interactions between subproblems. 216 joint plan containing the action (i.e. the first effect) The second effect states that the action requested will be the next action performed in the introduced plan. ....

M. Stefik, Planning with Constraints (MOLGEN: Part 1), Artificial Intelligence 16, (1981), Ill-140.

....present the SmartClient concept. Next, we describe how to apply SmartClient to the travel domain and to more general networked information systems. 2.1. Constraint Satisfaction Problems Constraint Satisfaction Problems (CSPs) are ubiquitous in applications like con guration [12, 11] planning [14], resource allocation [2, 13] scheduling [4] and many others. A CSP is speci ed by a set of variables and constraints among them. A solution to a CSP is a set of value assignments to all variables such that all constraints are satis ed. There can be either many, 1 or no solutions to a given ....

Mark Ste k. Planning with constraints (MOLGEN: Part 1). Arti cial Intelligence, 16(2):111-140, 1981.

....Finally we describe the JCL shell, a tool to solve CSPs on the Internet by means of a Java applet, and present a resource allocation problem to illustrate the usage of JCL. 3 2. 1 Constraint Satisfaction Problems (CSPs) CSPs are ubiquitous in applications like configuration [10, 9] planning [12, 7, 3], resource allocation [1, 11] scheduling [8, 2] timetabling [6] and many others. A CSP is specified by a set of variables and constraints among them. A solution to a CSP is a set of value assignments to all variables such that all constraints are satisfied. There can be either many, 1 or no ....

Mark Stefik. Planning with constraints (molgen: Part 1). Artificial Intelligence, 16(2):111-- 140, 1981. 16

....briefly describe CSPs and then we present a concrete way to model our problem by identifying the main components of such formulation. 2. 1 Constraint Satisfaction Problems (CSPs) Constraint Satisfaction Problems (CSPs) see [4] are ubiquitous in applications like configuration [5, 6] planning [7], resource allocation [8, 9] scheduling [10] and many others. A CSP is specified by a set of variables and constraints among them. A solution to a CSP is a set of value assignments to all variables such that all constraints are satisfied. There can be either many, 1 or no solutions to a given ....

Mark Stefik. Planning with constraints (MOLGEN: Part 1). Artificial Intelligence, 16(2):111--140, 1981.

....we brieAEy describe CSPs and then we present a concrete way to model our problem by identifying the main components of such formulation. 2. 1 Constraint Satisfaction Problems (CSPs) Constraint Satisfaction Problems (CSPs) see [4] are ubiquitous in applications like conguration [5, 6] planning [7], resource allocation [8, 9] scheduling [10] and many others. A CSP is specied by a set of variables and constraints among them. A solution to a CSP is a set of value assignments to all variables such that all constraints are satised. There can be either many, 1 or no solutions to a given ....

Mark Stek. Planning with constraints (MOLGEN: Part 1). Articial Intelligence, 16(2):111140, 1981.

....can be found by simulated annealing. Constraint Satisfaction A special form of inference is realized by constraint satisfaction techniques, which has been applied successfully in many subfields of AI (such as computer vision [Waltz, 1972] circuit analysis [Stallman and Sussman, 1977] planning [Stefik, 1981], diagnosis [Davis, 1984] Geffner and Pearl, 1987] DeKleer and Williams, 1986] and logic programming [Jaffar and Lassez, 1987] and among which are connectionist approaches as well. Constraint satisfaction may be described as follows: given a set of variables and a set of constraining ....

M. Stefik. Planning with constraints (Molgen: part 1). Artificial Intelligence, 16:111--140, 1981.

....elements, and so on, until resulting subproblems are small enough to be solved by some simple means, such as using known solutions. This approach has been used successfully for a number of engineering design and configuration problems, where it is often referred to as a refinement method ( [6], 1] 4] 3] 7] For example, a task of designing a machine can be subdivided into tasks of designing its major modules (or major features) the task of designing a major module can be decomposed into tasks of designing its major assemblies; and so on until we reduce our problem down to a ....

Stefik, M. Planning with Constraints (MOLGEN: Part 1). Artificial Intelligence (16):111-141, 1981.

....by representing the extended goals inside the classical model, and considering all the non end goals as constraints on the final plan. This point of view is rather different to other uses of constraints in planning, as in Tate [19] where plans are seen as constrained objects, or in Stefik [18], whose planning system, MOLGEN, is mainly based on the activity of constraints posting. The main contribution of our work is showing that planning with constraints described in PCL 1, a language for defining planning constraints which will be introduced later, is equivalent to ordinary planning ....

M. Stefik. Planning with Constraints (MOLGEN: Part 1) in Artif. Intell. 16(2): 111-140, 1981.

....a new action and imposing an ordering constraint correspond to abducing facts in Delta. In CHICA, instantiating a variable is performed by extended unification, whereas in other planners this is done by a complex, often ill defined, matching algorithm. Constraint propagation as in MOLGEN [Stefik 81] and SIPE [Wilkins 88] is done in CHICA by means of finite domain constraint logic programming techniques. CHICA s goal directed search mechanism follows from the abductive proof procedure. For practical planning problems, CHICA s search can be controlled by heuristics defining computation, ....

Mark Jeffrey Stefik. Planning with constraints. Artificial Intelligence, 16, 1981.

....3.1. Detecting Conflicts The first service that DCSS assistants provide design agents is support in detecting conflicts among them as early as possible. DCSS supports this in two ways. First, it allows design agents to describe their design actions in terms of a least committment design model [43] that helps avoid unnecessary conflicts and allows early conflict detection. Second, it provides a range of tools for detecting conflicts once they occur. In the following sections we examine both. 3.1.1. Describing Least Committment Designs Describing design actions requires an effective ....

.... by constraining the value of module attributes, connecting module interfaces (to represent module interactions) decomposing modules into sub modules and specializing modules by refining their class (Figure 3) Specifications and attribute values are described using a constraint language [43], 24] Mark Klein Conflict Management 7 Constrain Attribute Value Module Module Module Module Module Module Module Specialize Connect Connect Specialize Decompose Decompose Figure 3: The design refinement process. If we were designing an airplane, for example, we might decompose ....

[Article contains additional citation context not shown here]

Stefik, M.J. Planning With Constraints (Molgen: Part 1 & 2). Artificial Intelligence 16, 2 (1981) Pps. 111-170.

.... notion plays a central role in the modern theory of data structures and recursive computation (see (Scott, 1982) It appears in work on deduction and planning under the names constraint based reasoning and constraint posting (see, for example, Stallman and Sussman, 1977; Saraswat, 1989; Stefik, 1981)) But the completeness of answers may be only one factor in their utility, so that a more complete partial answer may no better than (or even worse than) a less complete one. Accordingly, recent research has developed approximation methods in which the aim is that utility or probability of ....

Stefik, M. 1981. Planning with constraints (molgen: Part 1). Artificial Intelligence, 16:111--140.

....described by a set of objects, together with some relationships among them. The recognition of this fact has lead to much work in the fields of AI and of Logic Programming. In the last twenty years a number of AI researchers have explored the use of constraints to solve di#cult problems [8, 20, 21, 22]. Most of the proposed systems were based on the technique of constraint propagation over a declarative structure called constraint network [7] A constraint network consists of a number of nodes connected by constraints. A node represents an individual parameter of the problem at hand, while a ....

M. Stefik. Planning with Constraints (molgen: Part 1). Artificial Intelligence, 16:111--139, 1981.

....and distributed problem solving multiple, distributed agents solving a problem by searching through a hierarchy of problem spaces. Hierarchical Problem Solving was first used in GPS [7] and ABSTRIPS [10] and has since been used in a number of problem solvers, including NOAH [11] MOLGEN [12], NONLIN [13] SIPE [15] and PRODIGY [1] Given a hierarchy of abstraction spaces, hierarchical problem solving proceeds as follows: First, the problem solver maps the given problem into the most abstract space by deleting literals from the initial state and goal that are no relevant to the ....

M. Stefik. Planning with constraints (MOLGEN: Part 1). Artificial Intelligence, 16:111--140, 1981.

....alternatives to the one step at a time process of the SMs described above: any notion of planning and problem decomposition can be turned into a PM. Some of the ones we have been considering are abstraction hierarchies [70] 71] 72] case based planning [28] 29] 37] constraint based planners [80] [81] plan reuse [20] mixedinitiative planning [65] agent planning [2] 59] 67] and others [6] 23] 84] The one we describe here is a distributed planning PM called the Horde Planner (HP) The HP tries to collect together a complete set of resources that can solve a problem (or a set of ....

Mark J. Stefik, "Planning with Constraints", Artificial Intelligence Journal, Volume 16, pp. 111-140 (1981) 21

....There are a set of abstractions for each operator, and each instance of an operator in an abstract plan can be expanded to a different level of detail during the refinement of a plan. Operator abstractions have been used extensively in least commitment problem solvers such as noah [54] molgen [58], nonlin [59] and sipe [64] The difference between operator abstraction and state abstraction is small since operator abstraction can be used to implement state abstraction by imposing constraints on the order in which the operator abstractions are expanded. This is the approach taken in sipe ....

Mark Stefik. Planning with constraints (MOLGEN: Part 1). Artificial Intelligence, 16(2), 1981, 111--140.

....while lower priority goals are attempted. A higher level goal completely determines the lowerlevel goal; if a lower level goal fails, replanning is performed at the higher level node. A second variety of hierarchical plans is seen in systems such as Tate s NONLIN [Tate, 1977] and Stefik s MOLGEN [Stefik, 1981a; Stefik, 1981b] Here the abstract plan is viewed as a skeletal [Friedland and Iwasaki, 1985] or partial solution to the problem; by solving the higher level, abstract goals first, the search space for possible solutions is delimited. Another difference is that replanning in these systems is ....

Mark Stefik. Planning With Constraints (Molgen: Part 1). Artificial Intelligence, 16(2):111--140, May 1981. 173

....not be the same person (for obvious reasons) and not both teach with video projectors (because the school only has one) Abstraction has a long history in AI; we restrict our references here to those that address constraint satisfaction specifically. An early example is Stefik s work on Molgen (Stefik 1981). Ellman provides a general framework for abstraction for constraint satisfaction using various forms of symmetry (Ellman 1993) In more specifically CSP terms, Schrag and Miranker abstract domains for determining unsatisfiability of CSPs (Schrag Miranker 1996) Choueiry and Faltings relate ....

Stefik, M. 1981. Planning with constraints (MOLGEN: Part 1). Artificial Intelligence 16(2):111--140.

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Mark Stefik. Planning with constraints (MOLGEN: Part 1). Artificial Intelligence, 16(2):111--140, 1981.

No context found.

Mark Stefik. Planning with constraints (MOLGEN: Part 1). Artificial Intelligence, 16(2):111--140, 1981.

No context found.

Stefik, M. J., 1980. Planning with constraints, Stanford University, Computer Science Department, Report STAN-CS-80-784.

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Stefik M, Planning with constraints (MOLGEN: Part 1). Artificial Intelligence, Vol. 16(2), 1981, pp. 111-140.

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Stefik, M. (1981). Planning with Constraints (MOLGEN: PART-I). Artificial Intelligence 16, 111139.

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Stefik, M. Planning with Constraints (MOLGEN: Part 1). Artificial Intelligence 16:111-140, 1981.

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Stefik M. Planning with constraints (MOLGEN: Part 1). Artif Intell. 1981;16(2):111-40.

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