Specifying causality in action theories: a default logic approach

Recent research on reasoning about action has shown that the traditional logic form of domain constraints is problematic to represent ramifications of actions that are related to causality of domains. To handle this problem properly, as proposed by some researchers, it is necessary to describe causal relations of domains explicitly in action theories. In this paper, we address this problem from a new point of view. Specifically, unlike other researchers viewing causal relations as some kind of inference rules, we distinguish causal relations between defeasible and non-defeasible cases. It turns out that a causal theory in our formalism can be specified by using Reiter's default logic. Based on this idea, we propose a causality-based minimal change approach for representing effects of actions, and argue that our approach provides more plausible solutions for the ramification and qualification problems compared with other related work. We also describe a logic programming approximation to compute causal theories of actions which provides an implementational basis for our approach.     

Integrating Discrete and Continuous Change in a Logical Framework

The goal of our work is to develop theoretical foundations for the representation of knowledge in domains in which properties may vary continuously. One achievement of our research is that it extends the applicability of current research on theories of action. Furthermore, we are able to apply known approaches to the frame and ramification problems, developed for discretely changing worlds, to domains in which the world changes continuously.
Our approach is based on the discrete situation calculus and on a monotonic solution to the frame problem. In order to address the combined frame and ramification problems, we extend Lin and Reiter's work. We use Pinto and Reiter's extension to the situation calculus to represent occurrences . We extend this work further to allow for reasoning by default. For example, if we know that a ball is falling and we do not have any reason to believe that an action would interfere with the ball's motion, then we assume that the ball will hit the ground. Finally, we extend the language of the situation calculus to allow for properties that change within situations. We also show that our proposed situation calculus inherits the solutions to the frame and ramification problems.     

Mechanical verification of strategies

This paper presents a method of representing planning domains in the Boyer-Moore logic so that we can prove mechanically whether a strategy solves a problem. Current approaches require explicit frame axioms and state constraints to be included as part of a domain specification and use a programming language for expressing strategies. These make it difficult to specify domains and verify plans efficiently. Our method avoids explicit frame axioms, since domains are specified by programming an interpreter for sequences of actions in the Boyer-Moore logic. Strategies are represented as planners, Lisp programs that take an initial state and other arguments as input and return a sequence of actions that, when executed in the given initial state, will bring about a goal state. Mechanical verification of a strategy is accomplished by proving that the corresponding planner solves all instances of the given problem. We illustrate our approach by verifying strategies in some variations of the blocks world.The work described here was supported in part by NSF Grant MIP-9017499.     

A unifying action calculus

McCarthy's Situation Calculus is arguably the oldest special-purpose knowledge representation formalism, designed to axiomatize knowledge of actions and their effects. Four decades of research in this area have led to a variety of alternative formalisms: While some approaches can be considered instances or extensions of the classical Situation Calculus, like Reiter's successor state axioms or the Fluent Calculus, there are also special planning languages like ADL and approaches based on a linear (rather than branching) time structure like the Event Calculus. The co-existence of many different calculi has two main disadvantages: The formal relations among them is a largely open issue, and a lot of today's research concerns the transfer of specific results from one approach to another. In this paper, we present a unifying action calculus, which encompasses (well-defined classes of) all of the aforementioned formalisms. Our calculus not only facilitates comparisons and translations between specific approaches, it also allows to solve interesting problems for various calculi at once. We exemplify this by providing a general, calculus-independent solution to a problem of practical relevance, which is intimately related to McCarthy's quest for elaboration tolerant formalisms: the modularity of domain axiomatizations.     

TEMPORAL REASONING WITH THE MODAL LOGIC Z

This article presents a formal theory of concurrent actions that handles the qualification, ramification, and frame problems. The theory is capable of temporal explanation, i.e., reasoning forward and backward. The approach uses the modal logic Z to extend the work of Lifschitz and Rabinov on miracle-based temporal reasoning. The advantages of miracles for describing unknown actions are augmented with the ability to handle concurrent actions that can provide for the most economical explanation of state changes. For temporal explanation problems restricted to finite domains, it has a worst-case exponential decision procedure. The theory is as general as first-order logic in what it can express as preconditions and consequences of actions.     

The logical control of an elevator

This paper presents a detailed example of the design of a logical feedback controller for finite state machines. In this approach, the control objectives and associated control actions are formulated as a set of axioms each of the form X implies Y, where X asserts that (i) the current state satisfies a set of conditions and (ii) the control action y will steer the current state towards a given target state; Y asserts that the next control input will take the value y. An automatic theorem prover establishes which of the assertions X is true, and then the corresponding control y is applied. The main advantages of this system are its flexibility (changing the control law is accomplished through changing only the axioms) and the fact that, by the design of the system, control actions will provably achieve the control objectives. The illustrative design problem presented in this paper is that of the logical specification and logical feedback control of an elevator     

Estimating the Attraction Domain for the Boost Inverter

This work presents an approach for estimating the domain of attraction for polynomial systems with state and control‐signal constraints, including saturation. In many problems, it is possible to derive global stability properties for such systems, neglecting constraints. Consideration of the constraints usually makes the problem much more complicated. In this paper, the stability analysis performed for the unconstrained case is used for the problem as a whole. For application of the method, there are powerful computational tools that can be employed in cases of polynomial systems. The technique is not only valid for the analysis of equilibrium points, but also for other attractors, such as limit cycles. As examples, the domain of attraction for given control laws is estimated for both a nonlinear DC‐DC boost converter and for a boost inverter.     

基于流演算理论的动态访问控制模型研究

访问控制模型为系统的信息安全提供了一个理论框架,其目的是保护系统资源不被非法用户盗用,防止合法用户对受保护信息进行非法使用。然而,现有的访问控制模型大部分属于静态授权模型,不能方便地描述大规模、异构的分布式网络系统中授权过程的动态变化。为了解决上述不足,在充分研究流演算理论的基础上,提出了一个基于流演算理论的访问控制模型(FCDAC)。FCDAC将动态世界中的所有授权过程都看作是动作的结果,通过动作来实现状态的变化,并且在系统中只需描述动作的前提条件公理和状态更新公理就可容易地实现权限的变化。最后,通过一个教务管理实例验证上述理论,结果表明FCDAC是可行的。     

Least-restrictive move-blocking model predictive control

Move-blocking lowers the computational complexity of model predictive control (MPC) problems by reducing the number of optimization variables. However, this may render states close to constraints infeasible. Thus move-blocking generally results in control laws that are restrictive; the controller domains may be unacceptably and unnecessarily small. Furthermore, different move-blocking strategies may result in controller domains of different sizes, all other factors being equal. In this paper an approach is proposed to design move-blocking MPC control laws that are least-restrictive, i.e. the controller domain is equal to the maximum controlled invariant set. The domains of different move-blocking controllers are then by design equal to each other. This allows comparison of differing move-blocking strategies based on cost performance only, without needing to consider domain size also. Thus this paper is a step towards being able to derive optimal move-blocking MPC control laws.     

基于动态描述逻辑DDL的动作理论

基于一阶谓词逻辑或高阶逻辑的动作理论与采用命题语言的动作理论之间存在一个关于描述和推理能力的鸿沟;作为描述逻辑的动态扩展,动态描述逻辑DDL为基于描述逻辑的动作刻画和推理提供了一种途径.系统地研究了基于DDL的动作表示和推理问题.首先,在应用描述逻辑对静态领域知识进行刻画的基础上,引入带参数的原子动作定义式和带参数的复...     

基于与状态无关的激活集的包含派生谓词的规划问题求解

派生谓词是PDDL2．2语言的新特性之一。在2004年的规划大赛IPC-4上,许多规划系统都无法求解包含派生谓词的两个标准竞赛问题。在经典规划中,派生谓词是指不受领域动作直接影响的谓词,它们在当前状态下的真值是在封闭世界假设中由某些基本谓词通过领域公理推导出来的。本文提出一种新的方法来求解包含派生谓词的规划问题,即用与状态无关的激活集来取代派生谓词用于放宽式规划中。     

Ontologies for agents

An ontology is a computational model of some portion of the world. It is often captured in some form of a semantic network-a graph whose nodes are concepts or individual objects and whose arcs represent relationships or associations among the concepts. This network is augmented by properties and attributes, constraints, functions, and rules that govern the behavior of the concepts. Formally, an ontology is an agreement about a shared conceptualization, which includes frameworks for modeling domain knowledge and agreements about the representation of particular domain theories. Definitions associate the names of entities in a universe of discourse (for example, classes, relations, functions, or other objects) with human readable text describing what the names mean, and formal axioms that constrain the interpretation and well formed use of these names. For information systems, or for the Internet, ontologies can be used to organize keywords and database concepts by capturing the semantic relationships among the keywords or among the tables and fields in a database. The semantic relationships give users an abstract view of an information space for their domain of interest     

Integrated Temporal Reasoning with Periodic Events

In many areas of Computer Science, including planning, workflows, guidelines, and protocol management, one has to deal with abstract plans, procedures, or schedules involving temporal constraints between classes of actions that have to be repeated at periodic times and may be instantiated in different ways for different executions of the plans (procedures, schedules). In this paper we propose an integrated framework to deal with both qualitative temporal constraints on classes of actions and temporal constraints between instances of actions, in which temporal reasoning is used to amalgamate both types of constraints and to check their consistency. In particular, we consider an expressive formalism to deal with temporal constraints between classes of actions (with special attention to periodic actions) which takes into account different components such as frame times, numeric quantification, periods, and qualitative relations. We define the notions of (contextual) concretization of qualitative temporal constraints between classes and use this notion to formally define the consistency of a knowledge base of temporal constraints between classes and a set of temporal constraints on instances, and to define the algorithm for checking such a consistency. An application for scheduling lessons in a school is shown in an example.     

Constraint satisfaction techniques in planning and scheduling

Over the last few years constraint satisfaction, planning, and scheduling have received increased attention, and substantial effort has been invested in exploiting constraint satisfaction techniques when solving real life planning and scheduling problems. Constraint satisfaction is the process of finding a solution to a set of constraints. Planning is the process of finding a sequence of actions that transfer the world from some initial state to a desired state. Scheduling is the problem of assigning a set of tasks to a set of resources subject to a set of constraints. In this paper, we introduce the main definitions and techniques of constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view.     

Task planning under uncertainty using a spreading activationnetwork

As robotics and automation applications extend to the service sector, researchers have to increasingly deal with performing robotic actions in uncertain and unstructured environments. A traditional solution to this problem models uncertainty about the effects of actions by probabilities conditioned on the state of the environment, making it possible to select plans that have the highest probability of success in a given situation. Reactive systems use another approach to handling uncertainty, by employing a set of predefined situation-response rules that make it possible to move toward the goal from any situation, whether expected or unexpected. This paper describes a planner that combines the two approaches. A proactive component generates plans that are biased toward picking the most reliable action in a given situation, and a reactive component can alter the selected actions based on unexpected situations that may arise in uncertain environments. Action selection is driven by a spreading activation mechanism on a probabilistic network that encodes the domain knowledge. A decision-theoretic framework incorporates quantitative goal utilities and action costs into the action selection mechanism. Experiments conducted demonstrate the ability of the planner to plan with hard and soft domain constraints and action costs, modify plans as a reaction to unexpected changes in the environment or goal utilities, and plan in situations with multiple conflicting goals