PDDL: A language with a purpose?

In order to make planning technology more accessible and usable the planning community may have to adopt standard notations for embodying symbolic models of planning domains. In this paper it is argued that before we design such languages for planning we must be able to evaluate their quality. In other words, we must clear for what purpose the languages are to be used, and by what criteria the languages’ effectiveness are to be judged. Here some criteria are set down for languages used for theoretical and practical purposes respectively. PDDL is evaluated with respect to them, with differing results depending on whether PDDL’s purpose is to be a theoretical or practical language. From the results of these evaluations some conclusions are drawn for the development of standard languages for AI planning

On the Online Generation of Effective Macro-operators

Macro-operator (“macro”, for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training

Predicting Phishing Websites using Neural Network trained with Back-Propagation

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates

Issues in Planning Domain Model Engineering

The paper raises some issues relating to the engineering of domain models for automated planning. It studies the idea of a domain model as a formal specification of a domain, and considers properties of that specification. It proposes some definitions, which the planning and, more generally, the artificial intelligence community needs to consider, in order to properly deal with engineering issues in domain model creation

On the Effective Configuration of Planning Domain Models

The development of domain-independent planners within the AI Planning community is leading to “off the shelf” technology that can be used in a wide range of applications. Moreover, it allows a modular approach – in which planners and domain knowledge are modules of larger software applications – that facilitates substitutions or improvements of individual modules without changing the rest of the system. This approach also supports the use of reformulation and configuration techniques, which transform how a model is represented in order to improve the efficiency of plan generation. In this paper, we investigate how the performance of planners is affected by domain model configuration. We introduce a fully automated method for this configuration task, and show in an extensive experimental analysis with six planners and seven domains that this process (which can, in principle, be combined with other forms of reformulation and configuration) can have a remarkable impact on performance across planners. Furthermore, studying the obtained domain model configurations can provide useful information to effectively engineer planning domain models

Self-Management in Urban Traffic Control – an Automated Planning Perspective

Advanced urban traffic control systems are often based on feed-back algorithms. They use road traffic data which has been gathered from a couple of minutes to several years. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we argue that systems are needed that can sense, interpret and deliberate with their actions and goals to be achieved, taking into consideration continuous changes in state, required service level and environmental constraints. The requirement of such systems is that they can plan and act effectively after such deliberation, so that behaviourally they appear self-aware. This chapter focuses on the design of a generic architecture for auto- nomic urban traffic control, to enable the network to manage itself both in normal operation and in unexpected scenarios. The reasoning and self- management aspects are implemented using automated planning techniques inspired by both the symbolic artificial intelligence and traditional control engineering.Preliminary test results of the plan generation phase of the architecture are considered and evaluated

Towards The Integration of Model Predictive Control into an AI Planning Framework

This paper describes a framework for a hybrid algorithm that combines both AI Planning and Model Predictive Control approaches to reason with processes and events within a domain. This effectively utilises the strengths of search-based and model-simulation-based methods. We explore this control approach and show how it can be embedded into existing, modern AI Planning technology. This preserves the many advantages of the AI Planning approach, to do with domain independence through declarative modelling, and explicit reasoning, while leveraging the capability of MPC to deal with continuous processes computation within such domains. The developed technique is tested on an urban traffic control application and the results demonstrate the potential in utilising MPC as a heuristic to guide planning search

A General Framework of Generating Estimation Functions for Computing the Mutual Information of Terms

Computing statistical dependence of terms in textual documents is a widely studied subject and a core problem in many areas of science. This study focuses on such a problem and explores the techniques of estimation using the expected mutual information measure. A general framework is established for tackling a variety of estimations: (i) general forms of estimation functions are introduced; (ii) a set of constraints for the estimation functions is discussed; (iii) general forms of probability distributions are defined; (iv) general forms of the measures for calculating mutual information of terms (MIT) are formalised; (v) properties of the MIT measures are studied and, (vi) relations between the MIT measures are revealed. Four estimation methods, as examples, are proposed and mathematical meanings of the individual methods are respectively interpreted. The methods may be directly applied to practical problems for computing dependence values of individual term pairs. Due to its generality, our method is applicable to various areas, involving statistical semantic analysis of textual dat

Exploring the Synergy between two Modular Learning Techniques for Automated Planning

In the last decade the emphasis on improving the operational performance of domain independent automated planners has been in developing complex techniques which merge a range of different strategies. This quest for operational advantage, driven by the regular international planning competitions, has not made it easy to study, understand and predict what combinations of techniques will have what effect on a planner’s behaviour in a particular application domain. In this paper, we consider two machine learning techniques for planner performance improvement, and exploit a modular approach to their combination in order to facilitate the analysis of the impact of each individual component. We believe this can contribute to the development of more transparent planning engines, which are designed using modular, interchangeable, and well-founded components. Specifically, we combined two previously unrelated learning techniques, entanglements and relational decision trees, to guide a “vanilla” search algorithm. We report on a large experimental analysis which demonstrates the effectiveness of the approach in terms of performance improvements, resulting in a very competitive planning configuration despite the use of a more modular and transparent architecture. This gives insights on the strengths and weaknesses of the considered approaches, that will help their future exploitation