This paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a value-ordering heuristic, the min-conflicts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of different search strategies. We demonstrate empirically that on the n-queens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. We also describe a scheduling application where the approach has been used successfully. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be most effective.

A theory of analogical mapping between source and target analogs based upon Interacting structural, semantic, and pragmatic constraints is proposed here. The structural constraint of isomorphism encourages mappings that maximize the consistency of relational corresondences between the elements of the two analogs. The constraint of semantic similarity supports mapping hypotheses to the degree that mapped predicates have similar meanings. The constraint of prog-mafic central/! / favors mappings involving elements the analogist believes to be Important in order to achieve the purpose for which the analogy Is being used. The theory is implemented in a computer program called ACME (Analogical Constraint Mapping Engine), which represents constraints by means of a network of supporting and competing hypotheses regarding what elements to map. A coop-erative algorithm for parallel constraint satisfaction identifies mapping hypotheses that collectively represent the overall mapping that best fits the interacting constraints. ACME has been applied to a wide range of examples that include problem analogies, analogical arguments, explanatory analogies, story analogies, formal analogies, and metaphors. ACME is sensitive to semantic and pragmatic Information if it Is available,.and yet able to compute mappings between formally Isomorphic analogs without any similar or identical elements. The theory Is able to account for empirical findings regarding the impact of consistency and similarity on human processing of analogies.

Adaptive Execution in Complex Dynamic Worlds Robert James Firby Yale University 1989 A robot acting in the real world must use flexible plans because actions will sometimes fail to produce desired effects, and unexpected events will sometimes demand the robot shift its attention. A plan is usually construed as a list of primitive robot actions to be executed one after another but in a complex domain, a plan must be structured to cope effectively with the myriad unpredictable details it will encounter during execution. However, adding structure to a plan involves more than augmenting the primitive plan representation; it requires a complete model of interaction with the world called situation-driven execution. Situation-driven execution assumes that a plan consists of tasks with three major components: a satisfaction test, a window of activity, and a set of execution methods that are appropriate in different circumstances. Execution of such a plan proceeds by selecting an unsatisfied t...

In this paper we address the problem of automatically generating job workflows for the Grid. These workflows describe the execution of a complex application built from individual application components. In our work we have developed two workflow generators: the first (the Concrete Workflow Generator CWG) maps an abstract workflow defined in terms of application-level components to the set of available Grid resources. The second generator (Abstract and Concrete Workflow Generator, ACWG) takes a wider perspective and not only performs the abstract to concrete mapping but also enables the construction of the abstract workflow based on the available components. This system operates in the application domain and chooses application components based on the application metadata attributes. We describe our current ACWG based on AI planning technologies and outline how these technologies can play a crucial role in developing complex application workflows in Grid environments. Although our work is preliminary, CWG has already been used to map high energy physics applications onto the Grid. In one particular experiment, a set of production runs lasted 7 days and resulted in the generation of 167,500 events by 678 jobs. Additionally, ACWG was used to map gravitational physics workflows, with hundreds of nodes onto the available resources, resulting in 975 tasks, 1365 data transfers and 975 output files produced.

Abstract. Expertise consists of rapid selection and application of compiled experience. Robust reasoning, however, requires adaptation to new contingencies and intelligent modification of past experience. And novel or creative reasoning, by its real nature, necessitates general problem-solving abilities unconstrained by past behavior. This article presents a comprehensive computational model of analogical (case-based) reasoning that transitions smoothly between case replay, case adaptation, and general problem solving, exploiting and modifying past experience when available and resorting to general problem-solving methods when required. Learning occurs by accumulation of new cases, especially in situations that required extensive problem solving, and by tuning the indexing structure of the memory model to retrieve progressively more appropriate cases. The derivational replay mechanism is discussed in some detail, and extensive results of the first full implementation are presented. These results show up to a large performance improvement in a simple transportation domain for structurally similar problems, and smaller improvements when less strict similarity metrics are used for problems that share partial structure in a process-job planning domain and in an extended version of the STRIPS robot domain.

In the last few years we have witnessed a surge of business-to-consumer and business-to-business commerce operated on the Internet. However, most current electronic commerce systems are little more than electronic catalogues that allow a user to purchase a product under predetermined and inflexible terms and conditions. We believe that in the next few years we will see a new generation of electronic commerce systems emerge, based on automated negotiation. In this paper, we identify the main parameters on which any automated negotiation depends. To show the applicability of our classification framework, we use it to categorise a representative sample of some of the most prominent negotiation models that exist in the literature.

A persistent problem in machine planning is that of repairing plans that fail. Two solutions have been suggested to deal with this problem: planning critics and met a-planning techniques. Unfortunately, both of these suggestions suffer from lack of flexibility due to an extremely restricted view of how to describe planning failures. This paper presents an alternative approach in which plan failures are described in terms of causal explanations of why they occurred. These explanations are used to access different abstract replanning strategies, which are then turned into specific changes to the faulty plans. The approach is demonstrated using examples from CHEF, a case-based planner that creates and debugs plans in the domain of Szee hwan cooking. I. THE PROBLEM OF PLAN FAILURE.

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Problem solvers often have multiple operators available to them but must select just one to apply. We present three experiments that demonstrate that solvers use at least two sources of information to make operator selections in the building sticks task (BST): information from their past history of using the operators and information from the current context of the problem. Specifically, problem solvers are more likely to use an operator the more successful it has been in the past and the closer it takes the current state to the goal state. These two effects, respectively, represent the learning and performance processes that influence solvers ’ operator selections. A computational model of BST problem solving, developed within the ACT-R theory (Anderson, 1993), provides the unifying framework in which both types of processes can be integrated to predict solvers ’ selection tendencies. � 1996 Academic Press, Inc. Most problems can be approached in multiple ways but solved by only a few. Problem solving can be viewed, then, as finding one of the few paths that leads from a problem’s initial state to its goal state through some space of possible intermediate states (Newell & Simon, 1972). In this framework,