Exploiting Block Deordering for Improving Planners Efficiency

Capturing and exploiting structural knowledge of planning problems has shown to be a successful strategy for making the planning process more ef- ficient. Plans can be decomposed into its constituent coherent subplans, called blocks, that encapsulate some effects and preconditions, reducing interference and thus allowing more deordering of plans. According to the nature of blocks, they can be straightforwardly transformed into useful macro-operators (shortly, “macros”). Macros are well known and widely studied kind of structural knowledge because they can be easily encoded in the domain model and thus exploited by standard planning engines. In this paper, we introduce a method, called BLOMA, that learns domain-specific macros from plans, decomposed into “macro-blocks” which are extensions of blocks, utilising structural knowledge they capture. In contrast to existing macro learning techniques, macro-blocks are often able to capture high-level activities that form a basis for useful longer macros (i.e. those consisting of more original operators). Our method is evaluated by using the IPC benchmarks with state-of-the-art planning engines, and shows considerable improvement in many cases

Using Plan Decomposition for Continuing Plan Optimisation and Macro Generation

This thesis addresses three problems in the field of classical AI planning: decomposing a plan into meaningful subplans, continuing plan quality optimisation, and macro generation for efficient planning. The importance and difficulty of each of these problems is outlined below. (1) Decomposing a plan into meaningful subplans can facilitate a number of postplan generation tasks, including plan quality optimisation and macro generation – the two key concerns of this thesis. However, conventional plan decomposition techniques are often unable to decompose plans because they consider dependencies among steps, rather than subplans. (2) Finding high quality plans for large planning problems is hard. Planners that guarantee optimal, or bounded suboptimal, plan quality often cannot solve them In one experiment with the Genome Edit Distance domain optimal planners solved only 11.5% of problems. Anytime planners promise a way to successively produce better plans over time. However, current anytime planners tend to reach a limit where they stop finding any further improvement, and the plans produced are still very far from the best possible. In the same experiment, the LAMA anytime planner solved all problems but found plans whose average quality is 1.57 times worse than the best known. (3) Finding solutions quickly or even finding any solution for large problems within some resource constraint is also difficult. The best-performing planner in the 2014 international planning competition still failed to solve 29.3% of problems. Re-engineering a domain model by capturing and exploiting structural knowledge in the form of macros has been found very useful in speeding up planners. However, existing planner independent macro generation techniques often fail to capture some promising macro candidates because the constituent actions are not found in sequence in the totally ordered training plans. This thesis contributes to plan decomposition by developing a new plan deordering technique, named block deordering, that allows two subplans to be unordered even when their constituent steps cannot. Based on the block-deordered plan, this thesis further contributes to plan optimisation and macro generation, and their implementations in two systems, named BDPO2 and BloMa. Key to BDPO2 is a decomposition into subproblems of improving parts of the current best plan, rather than the plan as a whole. BDPO2 can be seen as an application of the large neighbourhood search strategy to planning. We use several windowing strategies to extract subplans from the block deordering of the current plan, and on-line learning for applying the most promising subplanners to the most promising subplans. We demonstrate empirically that even starting with the best plans found by other means, BDPO2 is still able to continue improving plan quality, and often produces better plans than other anytime planners when all are given enough runtime. BloMa uses an automatic planner independent technique to extract and filter “self-containe” subplans as macros from the block deordered training plans. These macros represent important longer activities useful to improve planners coverage and efficiency compared to the traditional macro generation approaches

Environmental Changes in the Hindu Raj Mountains, Pakistan

Global Environmental Change among the world’s mountains has become a field of interest for researchers and this issue has been widely studied in many parts of the world. This exploratory research aims to study the changes that have occurred and are still occurring in the Hindu Raj Mountains of northern Pakistan, which is an unexplored region with a wide potential for research. To study the changes in various aspects of physical and social setup, five villages/sub-valleys were selected at varying altitudes above mean sea level. Changes in the bio-physical environment were explored using remote sensing technology. It was found that drastic changes have taken place and are still going on in the natural environment as well as the socio-economic setup of the study area since 1970. The population of the study area has increased by manifold resulting in changes in the household and family structure. Moreover, the land use land cover of the study area has changed considerably. Forest cover has decreased drastically with an increase in both the built up and barren land areas

Epileptic seizure detection from electroencephalogram (EEG) signals using linear graph convolutional network and DenseNet based hybrid framework

A clinical condition known as epilepsy occurs when the brain's regular electrical activity is disturbed, resulting in a rapid, aberrant, and excessive discharge of brain neurons. The electroencephalogram (EEG) signal is the measurement of electrical activity received from the nerve cells of the cerebral cortex to make precise diagnoses of disorders, which is made crucial attention for treating epilepsy patients in recent years. The concentration on grid-like data has been a significant drawback of existing deep learning-based automatic epileptic seizure detection algorithms from raw EEG signals; nevertheless, physiological recordings frequently have irregular and unordered structures, making it challenging to think of them as a matrix. In order to take advantage of the implicit information that exists in seizure detection, graph neural networks have received a lot of attention. These networks feature interacting nodes connected by edges whose weights can be either dictated by temporal correlations or anatomical junctions. To address this limitation, a novel hybrid framework is proposed for epileptic seizure detection by using linear graph convolution neural network (LGCN) and DenseNet. When compared to previous deep learning networks, DenseNet achieves the model's higher computational accuracy and memory efficiency by reducing the vanishing gradient problem and enhancing feature propagation in each of its layers. The Stockwell transform (S-transform) is used to preprocess from the raw EEG signal and then group the resulting matrix into time-frequency blocks as inputs for the LGCN to use for feature selection and after the Densenet uses for classification. The proposed hybrid framework outperforms the state-of-the-art in seizure detection tasks, achieving 98% accuracy and 98.60% specificity in extensive experiments on the publicly available CHB-MIT EEG dataset

Plan quality optimisation via block decomposition

AI planners have to compromise between the speed of the planning process and the quality of the generated plan. Anytime planners try to balance these objectives by finding plans of better quality over time, but current anytime planners often do not make

Block-structured plan deordering

Partially ordered plans have several useful properties, such as exhibiting the structure of the plan more clearly which facilitates post-plan generation tasks like scheduling the plan, explaining it to a user, or breaking it into subplans for distribute

Continuing Plan Quality Optimisation

Finding high quality plans for large planning problems is hard. Although some current anytime planners are often able to improve plans quickly, they tend to reach a limit at which the plans produced are still very far from the best possible, but these planners fail to find any further improvement, even when given several hours of runtime. We present an approach to continuing plan quality optimisation at larger time scales, and its implementation in a system called BDPO2. Key to this approach is a decomposition into subproblems of improving parts of the current best plan. The decomposition is based on block deordering, a form of plan deordering which identifies hierarchical plan structure. BDPO2 can be seen as an application of the large neighbourhood search (LNS) local search strategy to planning, where the neighbourhood of a plan is defined by replacing one or more subplans with improved subplans. On-line learning is also used to adapt the strategy for selecting subplans and subplanners over the course of plan optimisation. Even starting from the best plans found by other means, BDPO2 is able to continue improving plan quality, often producing better plans than other anytime planners when all are given enough runtime. The best results, however, are achieved by a combination of different techniques working together