Constraints in Non-Boolean Contexts

In high-level constraint modelling languages, constraints can occur in non-Boolean contexts: implicitly, in the form of partial functions, or more explicitly, in the form of constraints on local variables in non-Boolean expressions. Specifications using these facilities are often more succinct. However, these specifications are typically executed on solvers that only support questions of the form of existentially quantified conjunctions of constraints. We show how we can translate expressions with constraints appearing in non-Boolean contexts into conjunctions of ordinary constraints. The translation is clearly structured into constrained type elimination, local variable lifting and partial function elimination. We explain our approach in the context of the modelling language Zinc. An implementation of it is an integral part of our Zinc compiler

XML Representation of Constraint Networks: Format XCSP 2.1

We propose a new extended format to represent constraint networks using XML. This format allows us to represent constraints defined either in extension or in intension. It also allows us to reference global constraints. Any instance of the problems CSP (Constraint Satisfaction Problem), QCSP (Quantified CSP) and WCSP (Weighted CSP) can be represented using this format

Doménově specifické jazyky ve funkcionálním programování

In Artificial Intelligence, especially in area of constraint programming, it's popular to design various modeling languages which allow solving problems on domain level and by using domain specific abstractions. Techniques known from research on Domain-Specific Languages are often useful in this effort. Functional programming languages offer new tools for designing such languages, particularly Domain-Specific Embedded Languages. This work investigates the advantages and disadvantages of using functional programming for designing and implementing a Domain-Specific Embedded Language for state space search problems.V umělé inteligenci, obzvláště v programování s omezujícími podmínkami, je populární navrhovat rozličné modelovací jazyky, které umožňují řešit problémy na úrovni domény a prostřednictvím doménových abstrakcí. Při tom je často užitečné používat techniky známé z oblasti doménově specifických jazyků. Funkcionální programovací jazyky poskytují nové prostředky pro návrh těchto jazyků, obzvláště v případě vnořených doménově specifických jazyků. Tato práce zkoumá výhody a nevýhody využití technik funkcionálního programování při návrhu a implementaci vnořeného doménově specifického jazyka pro problémy prohledávání stavových prostorů.Katedra teoretické informatiky a matematické logikyDepartment of Theoretical Computer Science and Mathematical LogicFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Integrating Planning and Scheduling : A Constraint-based Approach

Automated decision making is one of the important problems of Artificial Intelligence (AI). Planning and scheduling are two sub-fields of AI that research automated decision making. The main focus of planning is on general representations of actions, causal reasoning among actions and domain-independent solving strategies. Scheduling generally optimizes problems with complex temporal and resource constraints that have simpler causal relations between actions. However, there are problems that have both planning characteristics (causal constraints) and scheduling characteristics (temporal and resource constraints), and have strong interactions between these constraints. An integrated approach is needed to solve this class of problems efficiently. The main contribution of this thesis is an integrated constraint-based planning and scheduling approach that can model and solve problems that have both planning and scheduling characteristics. In our representation problems are described using a multi-valued state variable planning language with explicit representation of different types of resources, and a new action model where each action is represented by a set of transitions. This action-transition model makes the representation of actions with delayed effects, effects with different durations, and the representation of complex temporal and resource constraints like time-windows, deadline goals, sequence-dependent setup times, etc simpler. Constraint-based techniques have been successfully applied to solve scheduling problems. Therefore, to solve a combined planning/scheduling problem we compile it into a CSP. This compilation is bounded by the number of action occurrences. The constraint model is based on the notion of “support” for each type of transition. The constraint model can be viewed as a system of CSPs, one for each state variable and resource, that are synchronized by a simple temporal network for action start times. Central to our constraint model is the explicit representation and maintenance of the precedence constraints between transitions on the same state variable or resource. We propose a branching scheme for solving the CSP based on establishing supports for transitions, which imply precedence constraints. Furthermore, we propose new propagation and inference techniques that infer precedence relations from temporal and mutex constraints, and infer tighter temporal bounds from the precedence constraints. The distinguishing feature of these inference and propagation techniques is that they not only consider the transitions and actions that are included in the plan but can also consider actions and transitions that are not yet included in or excluded from the plan. We conclude the thesis with a modeling case study of a complex satellite problem domain to demonstrate the effectiveness of our representation. This problem domain has action choices that are tightly coupled with temporal and resource constraints. We show that most of the complexities of this problem can be expressed in our representation in a simple and intuitive way

The modelling language zinc

We describe the Zinc modelling language. Zinc provides set constraints, user defined types, constrained types, and polymorphic predicates and functions. The last allows Zinc to be readily extended to different application domains by user-defined libraries. Zinc is designed to support a modelling methodology in which the same conceptual model can be automatically mapped into different design models, thus allowing modellers to easily “plug and play” with different solving techniques and so choose the most appropriate for that problem