Planning Description Language Research Articles

Semantic Context-Aware Service Composition for Building Automation System

Service-oriented architecture (SOA) is realized by independent, standardized, and self-describing units known as services. This architecture has been widely used and verified for automatic, dynamic, and self-configuring distributed systems such as in building automation. This paper presents a building automation system adopting SOA paradigm with devices implemented by device profile for web service (DPWS) in which context information is collected, processed, and sent to a composition engine to coordinate appropriate devices/services based on the context, composition plan, and predefined policy rules. A six-phased composition process is proposed to carry out the task. In addition, two other components are designed to support the composition process: building ontology as a schema for representing semantic data and composition plan description language to describe context-based composite services in form of composition plans. A prototype consisting of a DPWSim simulator and SamBAS is developed to illustrate and test the proposed idea. Comparison analysis and experimental results imply the feasibility and scalability of the system.

Learning action models from plan examples using weighted MAX-SAT

AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as input. However, building action models from scratch is a difficult and time-consuming task, even for experts. In this paper, we develop an algorithm called ARMS (action-relation modelling system) for automatically discovering action models from a set of successful observed plans. Unlike the previous work in action-model learning, we do not assume complete knowledge of states in the middle of observed plans. In fact, our approach works when no or partial intermediate states are given. These example plans are obtained by an observation agent who does not know the logical encoding of the actions and the full state information between the actions. In a real world application, the cost is prohibitively high in labelling the training examples by manually annotating every state in a plan example from snapshots of an environment. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted MAX-SAT) problem and solves it using a MAX-SAT solver. We lay the theoretical foundations of the learning problem and evaluate the effectiveness of ARMS empirically.

Agent Model for Dynamically Changing Plan in π-Calculus

The authors propose a new model for the agent system and a new language. First, they develop a new model to use a new language developed for describing agent plans based on 7r-calculus, called PDL (Plan Description Language). The plans described in PDL can be changed dynamically while it is executing, because 7r-calculus provides dynamically changing structures. By using this property, agents can, in executing their plans, change those plans to adapt to the environment around them. This property is very important to agents, and is called reflection. Second, the authors implement a system. In order to do so, they propose a primitive language, called PiL (Pi-calculus Language). PiL is used on a computer more easily than the mathematical notations of 7r-calculus. Finally, they show, by using this system, that their proposition is very useful in a dynamically changing environment.

APPLYING π-CALCULUS TO DYNAMICALLY-CHANGING PLAN FOR AGENT MODEL

In this paper, we apply π-calculus1,2 to the planning for agents. First, we have proposed a new language for describing agent plans based on π-calculus, called PDL (Plan Description Language), and the model to execute them. The plans described in PDL can be changed dynamically while they are executing, because π-calculus provides dynamically-changing structures. By using this property, agents can change their plans to adapt to the environment around them by themselves in executing their plans. This property called reflection is very important to agents. We state the properties as theorems and prove them. Second, we have implemented an interpreter for PDL. In order to implement the system, we propose a primitive language, called PiL (Pi-calculus Language). PiL can be used on computers more easily than using the mathematical notations of π-calculus. We have shown that this system executes programs correctly and a plan written in PDL can be executed on the system. Finally, we have proved that the PDL is useful in the dynamically-changing environment by experiments.

Program analysis by levels of description

A plan/goal analysis of computer programs is developed and partially tested. This approach combines the ideas of plan analysis and top-down design and provides a principled description of a computer program at increasing levels of specificity. Three types of description were used in a simple debugging task: plan, goal and complete (plan + goal). Subjects were given a buggy program and a description of that program and the time taken to correct the bug was recorded. Contrary to predictions, the plan description was the most useful program documentation for both novice and advanced programmers. It is theorized that while high-level descriptions allow a program's goals to be understood, plans are precisely the information required to debug the implementation of these goals. This plan approach integrates the use of PDL as both a program and a plan description language.