Linear planning logic: An efficient language and theorem prover for robotic task planning

In this paper, we introduce a novel logic language and theorem prover for robotic task planning. Our language, which we call Linear Planning Logic (LPL), is a fragment of linear logic whose resource-conscious semantics are well suited for reasoning with dynamic state, while its structure admits efficient theorem provers for automatic plan construction. LPL can be considered as an extension of Linear Hereditary Harrop Formulas (LHHF), whose careful design allows the minimization of nondeterminism in proof search, providing a sufficient basis for the design of linear logic programming languages such as Lolli. Our new language extends on the expressivity of LHHF, while keeping the resulting nondeterminism in proof search to a minimum for efficiency. This paper introduces the LPL language, presents the main ideas behind our theorem prover on a smaller fragment of this language and finally provides an experimental illustration of its operation on the problem of task planning for the hexapod robot RHex. © 2014 IEEE

Symbolic Planning with Axioms

Axioms are an extension for classical planning models that allow for modeling complex preconditions and goals exponentially more compactly. Although axioms were introduced in planning more than a decade ago, modern planning techniques rarely support axioms, especially in cost-optimal planning. Symbolic search is a popular and competitive optimal planning technique based on the manipulation of sets of states. In this work, we extend symbolic search algorithms to support axioms natively. We analyze different ways of encoding derived variables and axiom rules to evaluate them in a symbolic representation. We prove that all encodings are sound and complete, and empirically show that the presented approach outperforms the previous state of the art in costoptimal classical planning with axioms.This work was supported by the German National Science Foundation (DFG) as part of the project EPSDAC (MA 7790/1-1) and the Research Unit FOR 1513 (HYBRIS). The FAI group of Saarland University has received support by DFG grant 389792660 as part of TRR 248 (see https://perspicuous-computing.science)

Marvin: A Heuristic Search Planner with Online Macro-Action Learning

This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate macro-actions, which are then used during search for a solution plan. We provide an overview of its architecture and search behaviour, detailing the algorithms used. We also empirically demonstrate the effectiveness of its features in various planning domains; in particular, the effects on performance due to the use of macro-actions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates

PDDL2.1: An extension of PDDL for expressing temporal planning domains

In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community

PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

In recent years research in the planning community has moved increasingly toward s application of planners to realistic problems involving both time and many typ es of resources. For example, interest in planning demonstrated by the space res earch community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating fo rce behind the progress that has been made in planning since 1998. The third com petition (held in 2002) set the planning community the challenge of handling tim e and numeric resources. This necessitated the development of a modelling langua ge capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community

PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

In recent years research in the planning community has moved increasingly toward s application of planners to realistic problems involving both time and many typ es of resources. For example, interest in planning demonstrated by the space res earch community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating fo rce behind the progress that has been made in planning since 1998. The third com petition (held in 2002) set the planning community the challenge of handling tim e and numeric resources. This necessitated the development of a modelling langua ge capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community

Object-orientated planning domain engineering

The development of domain independent planners focuses on the creation of generic problem solvers. These solvers are designed to solve problems that are declaratively described to them. In order to solve arbitrary problems, the planner must possess efficient and effective algorithms; however, an often overlooked requirement is the need for a complete and correct description of the problem domain. Currently, the most common domain description language is a prepositional logic, state-based language called STRIPS. This thesis develops a new object-orientated domain description language that addresses some of the common errors made in writing STRIPS domains. This new language also features powerful semantics that are shown to gready ease the description of certain domain features. A common criticism of domain independent planning is that the requirement of being domain independent necessarily precludes the exploitation of domain specific knowledge that would increase efficiency. One technique used to address this is to recognise patterns of behaviour in domains and abstract them out into a higher-level representations that are exploitable. These higher-level representations are called generic types. This thesis investigates the ways in which generic types can be used to assist the domain engineering process. A language is developed for describing the behavioural patterns of generic types and the ways in which they can be exploited. This opens a domain independent channel for domain specific knowledge to pass from the domain engineer to the planner

Cross organisational compatible workflows generation and execution

With the development of internet and electronics, the demand for electronic and online commerce has increased. This has, in turn, increased the demand for business process automation. Workflow has established itself as the technology used for business process automation. Since business organisations have to work in coordination with many other business organisations in order to succeed in business, the workflows of business organisations are expected to collaborate with those of other business organisations. Collaborating organisations can only proceed in business if they have compatible workflows. Therefore, there is a need for cross organisational workflow collaboration. The dynamism and complexity of online and electronic business and high demand from the market leave the workflows prone to frequent changes. If a workflow changes, it has to be re-engineered as well as reconciled with the workflows of the collaborating organisations. To avoid the continuous re-engineering and reconciliation of workflows, and to reuse the existing units of work done, the focus has recently shifted from modeling workflows to automatic workflow generation. Workflows must proceed to runtime execution, otherwise, the effort invested in the build time workflow modeling is wasted. Therefore, workflow management and collaboration systems must support workflow enactment and runtime workflow collaboration. Although substantial research has been done in build-time workflow collaboration, automatic workflow generation, workflow enactment and runtime workflow collaboration, the integration of these highly inter-dependent aspects of workflow has not been considered in the literature. The research work presented in this thesis investigates the integration of these different aspects. The main focus of the research presented in this thesis is the creation of a framework that is able to generate multiple sets of compatible workflows for multiple collaborating organisations, from their OWLS process definitions and high level goals. The proposed framework also supports runtime enactment and runtime collaboration of the generated workflows

Optimal Planning with State Constraints

In the classical planning model, state variables are assigned values in the initial state and remain unchanged unless explicitly affected by action effects. However, some properties of states are more naturally modelled not as direct effects of actions but instead as derived, in each state, from the primary variables via a set of rules. We refer to those rules as state constraints. The two types of state constraints that will be discussed here are numeric state constraints and logical rules that we will refer to as axioms. When using state constraints we make a distinction between primary variables, whose values are directly affected by action effects, and secondary variables, whose values are determined by state constraints. While primary variables have finite and discrete domains, as in classical planning, there is no such requirement for secondary variables. For example, using numeric state constraints allows us to have secondary variables whose values are real numbers. We show that state constraints are a construct that lets us combine classical planning methods with specialised solvers developed for other types of problems. For example, introducing numeric state constraints enables us to apply planning techniques in domains involving interconnected physical systems, such as power networks. To solve these types of problems optimally, we adapt commonly used methods from optimal classical planning, namely state-space search guided by admissible heuristics. In heuristics based on monotonic relaxation, the idea is that in a relaxed state each variable assumes a set of values instead of just a single value. With state constraints, the challenge becomes to evaluate the conditions, such as goals and action preconditions, that involve secondary variables. We employ consistency checking tools to evaluate whether these conditions are satisfied in the relaxed state. In our work with numerical constraints we use linear programming, while with axioms we use answer set programming and three value semantics. This allows us to build a relaxed planning graph and compute constraint-aware version of heuristics based on monotonic relaxation. We also adapt pattern database heuristics. We notice that an abstract state can be thought of as a state in the monotonic relaxation in which the variables in the pattern hold only one value, while the variables not in the pattern simultaneously hold all the values in their domains. This means that we can apply the same technique for evaluating conditions on secondary variables as we did for the monotonic relaxation and build pattern databases similarly as it is done in classical planning. To make better use of our heuristics, we modify the A* algorithm by combining two techniques that were previously used independently – partial expansion and preferred operators. Our modified algorithm, which we call PrefPEA, is most beneficial in cases where heuristic is expensive to compute, but accurate, and states have many successors