Linear logic as a tool for planning under temporal uncertainty

The typical AI problem is that of making a plan of the actions to be performed by a controller so that it could get into a set of final situations, if it started with a certain initial situation.The plans, and related winning strategies, happen to be finite in the case of a finite number of states and a finite number of instant actions.The situation becomes much more complex when we deal with planning under temporal uncertainty caused by actions with delayed effects.Here we introduce a tree-based formalism to express plans, or winning strategies, in finite state systems in which actions may have quantitatively delayed effects. Since the delays are non-deterministic and continuous, we need an infinite branching to display all possible delays. Nevertheless, under reasonable assumptions, we show that infinite winning strategies which may arise in this context can be captured by finite plans.The above planning problem is specified in logical terms within a Horn fragment of affine logic. Among other things, the advantage of linear logic approach is that we can easily capture ‘preemptive/anticipative’ plans (in which a new action β may be taken at some moment within the running time of an action α being carried out, in order to be prepared before completion of action α).In this paper we propose a comprehensive and adequate logical model of strong planning under temporal uncertainty which addresses infinity concerns. In particular, we establish a direct correspondence between linear logic proofs and plans, or winning strategies, for the actions with quantitative delayed effects.     

Vectorial languages and linear temporal logic

Determining for a given deterministic complete automaton the sequence of visited states while reading a given word is the core of important problems with automata-based solutions, such as approximate string matching. The main difficulty is to do this computation efficiently. Considering words as vectors and working on them using vectorial operations allows to solve the problem faster than using local operations.

In this paper, we show first that the set of vectorial operations needed by an algorithm representing a given automaton depends on the language accepted by the automaton. We give precise characterizations for star-free, solvable and regular languages using vectorial algorithms. We also study classes of languages associated with restricted sets of vectorial operations and relate them with languages defined by fragments of linear temporal logic.

Finally, we consider the converse problem of constructing an automaton from a given vectorial algorithm. As a byproduct, we show that the satisfiability problem for some extensions of LTL characterizing solvable and regular languages is PSPACE-complete.     

A framework for multi-robot motion planning from temporal logic specifications

We propose a framework for the coordination of a network of robots with respect to formal requirement specifications expressed in temporal logics.A regular tessellation is used to partition the space of interest into a union of disjoint regular and equal cells with finite facets,and each cell can only be occupied by a robot or an obstacle.Each robot is assumed to be equipped with a finite collection of continuous-time nonlinear closed-loop dynamics to be operated in.The robot is then modeled as a hybrid automaton for capturing the finitely many modes of operation for either staying within the current cell or reaching an adjacent cell through the corresponding facet.By taking the motion capabilities into account,a bisimilar discrete abstraction of the hybrid automaton can be constructed.Having the two systems bisimilar,all properties that are expressible in temporal logics such as Linear-time Temporal Logic,Computation Tree Logic,and μ -calculus can be preserved.Motion planning can then be performed at a discrete level by considering the parallel composition of discrete abstractions of the robots with a requirement specification given in a suitable temporal logic.The bisimilarity ensures that the discrete planning solutions are executable by the robots.For demonstration purpose,a finite automaton is used as the abstraction and the requirement specification is expressed in Computation Tree Logic.The model checker Cadence SMV is used to generate coordinated verified motion planning solutions.Two autonomous aerial robots are used to demonstrate how the proposed framework may be applied to solve coordinated motion planning problems.     

Moments and points in an interval-based temporal logic

This paper develops and explores a first-order theory of time that appears useful as an underlying framework for a wide range of practical applications in artificial intelligence (AI). In particular, it presents a concise, formal axiomatization of "interval-based" time as described by Allen and then explores the relationship between interval-based and point-based temporal theories in detail. This analysis should be useful to both theoretical and practical work in AI that involves the representation of time, since it shows what distinctions are actually substantive and what arise merely from formalisms being notational variants of one another.     

Constructive linear-time temporal logic: Proof systems and Kripke semantics

In this paper we study a version of constructive linear-time temporal logic (LTL) with the “next” temporal operator. The logic is originally due to Davies, who has shown that the proof system of the logic corresponds to a type system for binding-time analysis via the Curry-Howard isomorphism. However, he did not investigate the logic itself in detail; he has proved only that the logic augmented with negation and classical reasoning is equivalent to (the “next” fragment of) the standard formulation of classical linear-time temporal logic. We give natural deduction, sequent calculus and Hilbert-style proof systems for constructive LTL with conjunction, disjunction and falsehood, and show that the sequent calculus enjoys cut elimination. Moreover, we also consider Kripke semantics and prove soundness and completeness. One distinguishing feature of this logic is that distributivity of the “next” operator over disjunction “?(A∨B)⊃?A∨?B” is rejected in view of a type-theoretic interpretation.     

A decidable first-order logic for knowledge representation

Decidable first-order logics with reasonable model-theoretic semantics have several benefits for knowledge representation. These logics have the expressive power of standard first order logic along with an inference algorithm that will always terminate, both important considerations for knowledge representation. Knowledge representation systems that include a faithful implementation of one of these logics can also use its model-theoretic semantics to provide meanings for the data they store. One such logic, a variant of a simple type of first-order relevance logic, is developed and its properties described. This logic, although extremely weak, does capture a non-trivial and well-motivated set of inferences that can be entrusted to a knowledge representation system.This is a revised and much extended version of a paper of the same name that appears in the Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, California, 1985.     

TALplanner: A temporal logic based forward chaining planner

We present TALplanner, a forward-chaining planner based on the use of domain-dependent search control knowledge represented as formulas in the Temporal Action Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning about action and change in incompletely specified dynamic environments. TAL is used as the formal semantic basis for TALplanner, where a TAL goal narrative with control formulas is input to TALplanner which then generates a TAL narrative that entails the goal and control formulas. The sequential version of TALplanner is presented. The expressivity of plan operators is then extended to deal with an interesting class of resource types. An algorithm for generating concurrent plans, where operators have varying durations and internal state, is also presented. All versions of TALplanner have been implemented. The potential of these techniques is demonstrated by applying TALplanner to a number of standard planning benchmarks in the literature. This revised version was published online in June 2006 with corrections to the Cover Date.     

Tableau-based automata construction for dynamic linear time temporal logic*

We present a tableau-based algorithm for obtaining a Büchi automaton from a formula in Dynamic Linear Time Temporal Logic (DLTL), a logic which extends LTL by indexing the until operator with regular programs. The construction of the states of the automaton is similar to the standard construction for LTL, but a different technique must be used to verify the fulfillment of until formulas. The resulting automaton is a Büchi automaton rather than a generalized one. The construction can be done on-the-fly, while checking for the emptiness of the automaton. We also extend the construction to the Product Version of DLTL.*This research has been partially supported by the project MIUR PRIN 2005 ‘Specification and verification of agent interaction protocols’.     

Adding a temporal dimension to a logic system

We introduce a methodology whereby an arbitrary logic system L can be enriched with temporal features to create a new system T(L). The new system is constructed by combining L with a pure propositional temporal logic T (such as linear temporal logic with Since and Until) in a special way. We refer to this method as adding a temporal dimension to L or just temporalising L. We show that the logic system T(L) preserves several properties of the original temporal logic like soundness, completeness, decidability, conservativeness and separation over linear flows of time. We then focus on the temporalisation of first-order logic, and a comparison is make with other first-order approaches to the handling of time.     

Post-design analysis for building and refining AI planning systems

The growth of industrial applications of artificial intelligence has raised the need for design tools to aid in the conception and implementation of such complex systems. The design of automated planning systems faces several engineering challenges including the proper modeling of the domain knowledge: the creation of a model that represents the problem to be solved, the world that surrounds the system, and the ways the system can interact with and change the world in order to solve the problem. Knowledge modeling in AI planning is a hard task that involves acquiring the system requirements and making design decisions that can determine the behavior and performance of the resulting system. In this paper we investigate how knowledge acquired during a post-design phase of modeling can be used to improve the prospective model. A post-design framework is introduced which combines a knowledge engineering tool and a virtual prototyping environment for the analysis and simulation of plans. This framework demonstrates that post-design analysis supports the discovery of missing requirements and can guide the model refinement cycle. We present three case studies using benchmark domains and eight state-of-the-art planners. Our results demonstrate that significant improvements in plan quality and an increase in planning speed of up to three orders of magnitude can be achieved through a careful post-design process. We argue that such a process is critical for the deployment of AI planning technology in real-world engineering applications.     

Completeness of a combination of neighbourhood logic and temporal logic

This paper presents a completeness result, with respect to a possible world semantics, for a combination of a first-order temporal logic and neighbourhood logic. This logic was considered by Qiu and Zhou (1998, Proceedings of the PROCOMET 98, pp 444–461) to define semantics of a real-time OCCAM-like programming language.Received June 1999Accepted in revised form September 2003 by M. R. Hansen and C. B. Jones     

基于粗糙描述逻辑的不精确时态知识表示研究

在粗糙描述逻辑基础上扩充不精确时态关系,以满足不精确时态知识表示与推理的需要。首先给出了粗糙集及粗糙描述逻辑的相关概念;接着通过定义粗糙时态描述逻辑不精确时态关系,扩展了粗糙描述逻辑中具体域,并给出了可靠性和完备性证明;最后通过实际例子说明粗糙时态描述逻辑的知识表示和应用,结果表明扩展后的粗糙时态描述逻辑可以实现不精确时态知识的表示与推理。     

Expressiveness of temporal query languages: on the modelling of intervals, interval relationships and states

Storing and retrieving time-related information are important, or even critical, tasks on many areas of computer science (CS) and in particular for artificial intelligence (AI). The expressive power of temporal databases/query languages has been studied from different perspectives, but the kind of temporal information they are able to store and retrieve is not always conveniently addressed. Here we assess a number of temporal query languages with respect to the modelling of time intervals, interval relationships and states, which can be thought of as the building blocks to represent and reason about a large and important class of historic information. To survey the facilities and issues which are particular to certain temporal query languages not only gives an idea about how useful they can be in particular contexts, but also gives an interesting insight in how these issues are, in many cases, ultimately inherent to the database paradigm. While in the area of AI declarative languages are usually the preferred choice, other areas of CS heavily rely on the extended relational paradigm. This paper, then, will be concerned with the representation of historic information in two well known temporal query languages: Templog in the context of temporal deductive databases, and TSQL2 in the context of temporal relational databases. We hope the results highlighted here will increase cross-fertilisation between different communities. This article can be related to recent publications drawing the attention towards the different approaches followed by the Databases and AI communities when using time-related concepts.     

SAIRVO: a planning system implementing the ACTEM concept

The Laval University Design and Use of Scenes (LUDUS) project is aimed at developing a scene management system. To model a scene we propose to describe actors as autonomous systems which interact together within a temporal framework. Each actor is characterized by its autonomy in making decisions according to the situations it faces. In our approach, the actor's autonomy stems from the possibility of modelling decision and planning knowledge in appropriate knowledge bases. To specify an actor, the concept of the ACTEM (ACTing systEM) is introduced. The SAIRVO system implements the ACTEM model, and combines various characteristics of both expert systems and planners, as well as an interaction mode between ACTEMS in the form of message exchanges. A brief outline of research works related to planning systems is presented, the knowledge structures implemented in SAIRVO are described, and the general architecture of the system is given.     

Robustness of temporal logic specifications for continuous-time signals

In this paper, we consider the robust interpretation of Metric Temporal Logic (MTL) formulas over signals that take values in metric spaces. For such signals, which are generated by systems whose states are equipped with non-trivial metrics, for example continuous or hybrid, robustness is not only natural, but also a critical measure of system performance. Thus, we propose multi-valued semantics for MTL formulas, which capture not only the usual Boolean satisfiability of the formula, but also topological information regarding the distance, ε$\epsilon$, from unsatisfiability. We prove that any other signal that remains ε$\epsilon$-close to the initial one also satisfies the same MTL specification under the usual Boolean semantics. Finally, our framework is applied to the problem of testing formulas of two fragments of MTL, namely Metric Interval Temporal Logic (MITL) and closed Metric Temporal Logic (clMTL), over continuous-time signals using only discrete-time analysis. The motivating idea behind our approach is that if the continuous-time signal fulfills certain conditions and the discrete-time signal robustly satisfies the temporal logic specification, then the corresponding continuous-time signal should also satisfy the same temporal logic specification.     

The saturated tableaux for linear miniscoped horn-like temporal logic

A new type of tableau system is proposed for the complete class (called miniscoped Horn-like) of first-order linear temporal logic. The described system instead of induction-like postulates contains some nonlogical axioms indicating the saturation of a derivation process in this system.     

On the complexity of extension checking in default logic

The complexity of deciding equivalence of a formula to an extension of a default theory is investigated for the Reiter, justified, constrained, and rational semantics.