ASCoL: Automated Acquisition of Domain Specific Static Constraints from Plan Traces

Domain-independent planning systems require that domain constraints and invariants are specified as part of the input domain model. In AI Planning, the generated plan is correct provided the constraints of the world in which the agent is operating are satisfied. Specifying operator descriptions by hand for planning domain models that also require domain specific constraints is time consuming, error prone and still a challenge for the AI planning community. The LOCM (Cresswell, McCluskey, and West 2013) system carries out automated generation of the dynamic aspects of a planning domain model from a set of example training plans. We enhance the output domain model of the LOCM system to capture static domain constraints from the same set of input training plans as used by LOCM to learn dynamic aspects of the world. In this paper we propose a new framework ASCoL (Automated Static Constraint Learner), to make constraint acquisition more efficient, by observing a set of training plan traces. Most systems that learn constraints automatically do so by analysing the operators of the planning world. Out proposed system will discover static constraints by analysing plan traces for correlations in the data. To do this an algorithm is in the process of development for graph discovery from the collection of ground action instances used in the input plan traces. The proposed algorithm will analyse the complete set of plan traces, based on a predefined set of constraints, and deduces facts from it. We then augment components of the LOCM generated domain with enriched constraints

Learning Static Knowledge for AI Planning Domain Models via Plan Traces

Learning is fundamental to autonomous behaviour and from the point of view of Machine Learning, it is the ability of computers to learn without being programmed explicitly. Attaining such capability for learning domain models for Automated Planning (AP) engines is what triggered research into developing automated domain-learning systems. These systems can learn from training data. Until recent research it was believed that working in dynamically changing and unpredictable environments, it was not possible to construct action models a priori. After the research in the last decade, many systems have proved effective in engineering domain models by learning from plan traces. However, these systems require additional planner oriented information such as a partial domain model, initial, goal and/or intermediate states. Hence, a question arises - whether or not we can learn a dynamic domain model, which covers all domain behaviours from real-time action sequence traces only. The research in this thesis extends an area of the most promising line of work that is connected to work presented in an REF Journal paper. This research aims to enhance the LOCM system and to extend the method of Learning Domain Models for AI Planning Engines via Plan Traces. This method was first published in ICAPS 2009 by Cresswell, McCluskey, and West (Cresswell, 2009). LOCM is unique in that it requires no prior knowledge of the target domain; however, it can produce a dynamic part of a domain model from training. Its main drawback is that it does not produce static knowledge of the domain, and its model lacks certain expressive features. A key aspect of research presented in this thesis is to enhance the technique with the capacity to generate static knowledge. A test and focus for this PhD is to make LOCM able to learn static relationships in a fully automatic way in addition to the dynamic relationships, which LOCM can already learn, using plan traces as input. We present a novel system - The ASCoL (Automatic Static Constraints Learner) which provides a graphical interface for visual representation and exploits directed graph discovery and analysis technique. It has been designed to discover domain-specific static relations/constraints automatically in order to enhance planning domain models. The ASCoL method has wider applications. Combined with LOCM, ASCoL can be a useful tool to produce benchmark domains for automated planning engines. It is also useful as a debugging tool for improving existing domain models. We have evaluated ASCoL on fifteen different IPC domains and on different types of goal-oriented and random-walk plans as input training data and it has been shown to be effective

Cryptic footprints of rare earth elements on natural resources and living organisms

Background: Rare earth elements (REEs) are gaining attention due to rapid rise of modern industries and technological developments in their usage and residual fingerprinting. Cryptic entry of REEs in the natural resources and environment is significant; therefore, life on earth is prone to their nasty effects. Scientific sectors have expressed concerns over the entry of REEs into food chains, which ultimately influences their intake and metabolism in the living organisms. Objectives: Extensive scientific collections and intensive look in to the latest explorations agglomerated in this document aim to depict the distribution of REEs in soil, sediments, surface waters and groundwater possibly around the globe. Furthermore, it draws attention towards potential risks of intensive industrialization and modern agriculture to the exposure of REEs, and their effects on living organisms. It also draws links of REEs usage and their footprints in natural resources with the major food chains involving plants, animals and humans. Methods: Scientific literature preferably spanning over the last five years was obtained online from the MEDLINE and other sources publishing the latest studies on REEs distribution, properties, usage, cycling and intrusion in the environment and food-chains. Distribution of REEs in agricultural soils, sediments, surface and ground water was drawn on the global map, together with transport pathways of REEs and their cycling in the natural resources. Results: Fourteen REEs (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Th and Yb) were plighted in this study. Wide range of their concentrations has been detected in agricultural soils (\u3c 15.9–249.1 μg g−1) and in groundwater (\u3c 3.1–146.2 μg L−1) at various sites worldwide. They have strong tendency to accumulate in the human body, and thus associated with kidney stones. The REEs could also perturb the animal physiology, especially affecting the reproductive development in both terrestrial and aquatic animals. In plants, REEs might affect the germination, root and shoot development and flowering at concentration ranging from 0.4 to 150 mg kg−1. Conclusions: This review article precisely narrates the current status, sources, and potential effects of REEs on plants, animals, humans health. There are also a few examples where REEs have been used to benefit human health. However, still there is scarce information about threshold levels of REEs in the soil, aquatic, and terrestrial resources as well as living entities. Therefore, an aggressive effort is required for global action to generate more data on REEs. This implies we prescribe an urgent need for inter-disciplinary studies about REEs in order to identify their toxic effects on both ecosystems and organisms

Automated Knowledge Engineering Tools in Planning: State-of-the-art and Future Challenges

Intelligent agents must have a model of the dynamics of the domain in which they act. Models can be encoded by human experts or, as required by autonomous systems, automatically acquired from observation. At the state of the art, there existseveral systems for automated acquisition of planning domain models. In this paper we present a brief overview of the automated tools that can be exploited to induce planning domain models. While reviewing the literature on the existing tools for Knowledge Engineering (KE), we do a comparative analysis of them. The analysis is based on a set of criteria. The aim of the analysis is to give insights into the strengths and weaknesses of the considered systems, and to provide input for new, forthcoming research on KE tools in order to address future challenges in the automated KE area

A Brief Review of Automated Knowledge Engineering Tools for Artificial Intelligence Planning & Scheduling

It needs much effort to encode a planning domain model for complex applications. It includes getting the domain knowledge, creating and validating the correctness of knowledge and then maintaining it. The major product is the domain model which consists of a set of operators [1] (also called action schema) written in certain modelling languages. In this paper we briefly review a variety of automated knowledge engineering tools that automatically produce domain models, and discuss the motivation behind the development of these tools with three major concerns that differentiate these tools from one another. The results of the review give insights into the strengths and weaknesses of the considered systems, and point to needs in future work to enhance the capabilities and overcome their short comings

ASCoL: a Tool for Improving Automatic Planning Domain Model Acquisition

Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world. AI Planning requires domain models. Synthesising operator descriptions and domain specific constraints by hand for AI planning domain models is time intense, error-prone and challenging. To alleviate this, utomatic domain model acquisition techniques have been introduced. Amongst others, the LOCM and LOCM2 systems require as input some plan traces only, and are effectively able to automatically encode a large part of the domain knowledge. In particular, LOCM effectively determines the dynamic part of the domain model. On the other hand, the static part of the domain – i.e., the underlying structure of the domain that can not be dynamically changed, but that affects the way in which actions can be performed – is usually missed, since it can hardly be derived by observing transitions only. In this paper we introduce ASCoL, a tool that exploits graph analysis for automatically identifying static relations, in order to enhance planning domain models. ASCoL has been evaluated on domain models generated by LOCM for international planning competition domains, and has been shown to be effective