Using temporal logics to express search control knowledge for planning

Bacchus, Fahiem; Kabanza, Froduald

doi:10.1016/s0004-3702(99)00071-5

Cited by 359 publications

(310 citation statements)

References 17 publications

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“…Comparing Pdk with other planners that allow for the employment of heuristic knowledge, such as TLPLAN [1], involves, beyond efficiency, also expressiveness considerations. Although TLPLAN is faster than Pdk, the specification of heuristic knowledge is often quite long and cumbersome.…”

Section: Resultsmentioning

confidence: 99%

“…Artificial Intelligence planning is concerned with the automatic synthesis of sequences of actions that, when executed, lead from a given initial state to a state satisfying some logics have been used in different perspectives in this context, either in the deductive view [10,12], or by model checking [5], or as a language to add control knowledge to a specialised planner [1,6]. The system presented in this work is a planner fully based on Linear Time Logic (LTL), planning problem into LTL in such a way that planning is reduced to model search.…”

Section: Introductionmentioning

confidence: 99%

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Pdk: The System and Its Language

Mayer

Limongelli

Orlandini

et al. 2005

Lecture Notes in Computer Science

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This paper presents the planning system Pdk (Planning with Domain Knowledge), based on the translation of planning problems into Linear Time Logic theories, in such a way that finding solution plans is reduced to model search. The model search mechanism is based on temporal tableaux. The planning language accepted by the system allows one to specify extra problem dependent information, that can be of help both in reducing the search space and finding plans of better quality.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pdk: The System and Its Language

Mayer

Limongelli

Orlandini

et al. 2005

Lecture Notes in Computer Science

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show abstract

“…[DvLF93]) or planning (e.g. [BK00]). However, they are probably most well known for their role in automatic model checking approaches that allow verifying whether a system model, specified in some formal graphical or textual modeling language, conforms to a temporal logics formula.…”

Section: Temporal Logics and Model Checkingmentioning

confidence: 99%

Simulation and Statistical Model Checking of Logic-Based Multi-Agent System Models

Kroiß

2014

Advances in Intelligent Systems and Computing

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This thesis presents SALMA (Simulation and Analysis of Logic-Based MultiAgent Models), a new approach for simulation and statistical model checking of multi-agent system models.Statistical model checking is a relatively new branch of model-based approximative verification methods that help to overcome the well-known scalability problems of exact model checking. In contrast to existing solutions, SALMA specifies the mechanisms of the simulated system by means of logical axioms based upon the well-established situation calculus. Leveraging the resulting first-order logic structure of the system model, the simulation is coupled with a statistical model-checker that uses a first-order variant of time-bounded linear temporal logic (LTL) for describing properties. This is combined with a procedural and process-based language for describing agent behavior. Together, these parts create a very expressive framework for modeling and verification that allows direct fine-grained reasoning about the agents' interaction with each other and with their (physical) environment.SALMA extends the classical situation calculus and linear temporal logic (LTL) with means to address the specific requirements of multi-agent simulation models. In particular, cyber-physical domains are considered where the agents interact with their physical environment. Among other things, the thesis describes a generic situation calculus axiomatization that encompasses sensing and information transfer in multi agent systems, for instance sensor measurements or inter-agent messages. The proposed model explicitly accounts for real-time constraints and stochastic effects that are inevitable in cyber-physical systems.In order to make SALMA's statistical model checking facilities usable also for more complex problems, a mechanism for the efficient on-the-fly evaluation of first-order LTL properties was developed. In particular, the presented algorithm uses an interval-based representation of the formula evaluation state together with several other optimization techniques to avoid unnecessary computation.Altogether, the goal of this thesis was to create an approach for simulation and statistical model checking of multi-agent systems that builds upon well-proven logical and statistical foundations, but at the same time takes a pragmatic software engineering perspective that considers factors like usability, scalability, and extensibility. In fact, experience gained during several small to mid-sized experiments that are presented in this thesis suggest that the SALMA approach seems to be able to live up to these expectations.

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“…TALplanner (inspired by TLplan, Bacchus & Kabanza, 2000) allows the user to specify a set of first-order TAL formulas that must be entailed by the final plan. This serves two separate purposes.…”

Section: Using Domain-dependent Control Rulesmentioning

confidence: 99%

“…However, it is also possible to specify constraints related to traditional measures of plan quality, such as constraints that forbid certain "stupid" actions from taking place, as in the Blockhead blocks world planner by Kibler and Morris (1981) or TLplan by Bacchus and Kabanza (2000), which initially inspired the development of TALplanner. For example, in a logistics domain one may specify a temporally extended goal stating that once a package is at its destination, it is never picked up again, and a goal stating that trucks driving between two locations always use the shortest path.…”

Section: Introductionmentioning

confidence: 99%

TALplanner in IPC-2002: Extensions and Control Rules

Kvarnström¹,

Magnusson²

2003

jair

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TALplanner is a forward-chaining planner that relies on domain knowledge in the shape of temporal logic formulas in order to prune irrelevant parts of the search space. TALplanner recently participated in the third International Planning Competition, which had a clear emphasis on increasing the complexity of the problem domains being used as benchmark tests and the expressivity required to represent these domains in a planning system. Like many other planners, TALplanner had support for some but not all aspects of this increase in expressivity, and a number of changes to the planner were required. After a short introduction to TALplanner, this article describes some of the changes that were made before and during the competition. We also describe the process of introducing suitable domain knowledge for several of the competition domains.

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Using temporal logics to express search control knowledge for planning

Cited by 359 publications

References 17 publications

Pdk: The System and Its Language

Pdk: The System and Its Language

Simulation and Statistical Model Checking of Logic-Based Multi-Agent System Models

TALplanner in IPC-2002: Extensions and Control Rules

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