Sharif Digital Repository

Traditional AI planners often suffer from their poor efficiency. There are many choice points in the planning process, but lack of information precludes proper decision. In this paper, we introduce a new method for adding automatic learning capability to a forward planning system. Our idea is based on a dynamic voting algorithm to choose the best action to proceed to the next state. In every planning cycle, applicable actions (i.e. those actions whose preconditions are satisfied in the current world state) vote to, and compete with each other. As a result of this voting, gradually more useful actions are chosen. This idea has been applied to the blocks world domain, and the preliminary... 

In recent years, a number of new heuristic search methods have been developed in the field of automated planning. Enforced hill climbing (EHC) is one such method which has been frequently used in a number of AI planning systems. Despite certain weaknesses, such as getting trapped in dead-ends in some domains, this method is more competitive than several other methods in many planning domains. In order to enhance the efficiency of ordinary enforced hill climbing, a new form of enforced hill climbing, called guided enforced hill climbing, is introduced in this paper. An adaptive branch ordering function is the main feature that guided enforced hill climbing has added to EHC. Guided enforced... 

One of challenging problems in the domain of web services is the web service composition (i.e the task of composing web services to create new services, capable of fulfilling a need that no single service can satisfy). In real application, there will be a lot of relevant web services for a simple goal, so the search space for such a problem is huge. In this research statement, we show how by extracting some prior knowledge and using simple heuristics, search space and search time can be reduced significantly  

In this article, the relation between meteorological parameters and dust activities in western Iran has been studied. Satellite-based data achieved from TOMS are used to investigate the dust activities within a time period of 30 years. In the first part of this study, we examine the statistical trend of Aerosol Index (AI) and local meteorological parameters in 15 different stations. The same patterns of AI variations in all stations indicate that this region has always been subjected to dust storms which originate from similar sources in the neighboring countries that could be known as a sole dust transfer system. In the second part, we investigate the spatial correlation between the... 

Enforced hill climbing (EHC), a heuristicaa search method, has been frequently used in a number of AI planning systems. This paper presents a new form of EHC, guided enforced hill climbing (GEHC), to enhance EHC efficiency. Main feature in GEHC is an adaptive ordering function. GEHC has shown a significant improvement in EHC efficiency, especially when applied to larger problems  

In this paper, we describe LHTNDT, an algorithm that learns the preconditions of HTN methods by examining plan traces produced by another planner. LHTNDT extracts conditions for applying methods by using decision tree based algorithm. It considers the state of relevant domain objects in both current and goal states. Redundant training samples are removed using graph isomorphism. Our experiments, LHTNDT converged. It can learn most of preconditions correctly and quickly. 80% of our test problems were solved by preconditions extracted by 3/4 of plan traces needed for full convergence  

In many applications, the requirements of static data and large computation times are still major concerns. This paper presents a practical equivalent model of a small area in a large scale system which needs less statistical data of system equipments with acceptable level of accuracy. The proposed technique is designated as the Branch-Cutting Method in which tie lines between the area of interest (AI) and the other parts of the network are cut and the network behavior is modeled with some load and generating units in marginal load points. The load at marginal load points is represented as a multi state load model with the associated state probability. Load uncertainty is modeled using the... 

AI planning is one of the research fields that has benefited from employing satisfiability checking methods. These methods have been proved to be very effective in finding optimal plans for both classical and temporal planning. It is also known that by using planning-based heuristic information in solving SAT formulae, one can develop SAT-based planners that are competitive with state-of-the-art non-optimal planners in classical planning domains. However, using satisfiability for non-optimal temporal planning has not been investigated so far. The main difficulty in using satisfiability in temporal planning is the representation of time, which is a continuous concept. Previously introduced... 

Since that heuristic state space planners have been very successful in classical planning, this approach is currently the most popular strategy in dealing with temporal planning, too. However, all current state-space temporal planners use a search method known as decision epoch planning, which is not complete for problems with required concurrency. In theory, this flaw can be overcome by employing another search method, called temporally lifted progression planning. In this paper, we show that there are two major problems which, if not tackled properly, can cause the latter method to be very inefficient in practice. The first problem is dealing with the remarkably large state space of... 

The trend of gold price in the market is the most important consideration for the investors of the gold, and serves as the basis of gaining profit, so there are scholars who try to forecast the gold price. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best AI techniques for optimization and parameter estimation. In this study a PSO-based time series model for the gold price... 

Stress is a body's natural way of responding to any kind of demand or challenge that everyone experiences from time to time. Although short-Term stress typically does not impose a health burden, exposure to prolonged stress can lead to significant adverse physiological and behavioral changes. Coping with the impact of stress is a challenging task and in this context, stress assessment is essential in preventing detrimental long-Term effects. The public embracement of connected wearable Internet of Things (IoT) devices, as well as the proliferation of Artificial Intelligence (AI) and Machine Learning (ML) technologies, have generated new opportunities for personalized stress tracking and... 

In this paper, we introduce DiFF, a novel extension of the Fast Forward (FF) planning system. FF is a domain independent planner that employs a forward heuristic search. Its search strategy is an enforced form of hill climbing. In order to move to a more promising state, FF evaluates successor states without any particular order. In this paper, we introduce a new form of the enforced hill climbing, which we call directed enforced hill climbing, to enhance the efficiency of Fast Forward planning. This strategy evaluates successor states in the order recommended by an adaptive heuristic function. Our experimental results in several planning domains show a significant improvement in the... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects... 

Macroscopic and microscopic characteristics of spray of a Modified Bio-diesel Fuel (MBF), applying direct injection system have been explored and compared with those of conventional diesel fuel. MBF is a new combination of bio-diesel, molasses bio-ethanol, and water, which has been kept as a single-phase bio-fuel, employing an emulsifier. Lower emissions and production costs, higher oxygen content and cetane number are the key advantages of the MBF to be replaced by conventional fossil fuels in internal combustion engines. Applying atomization model, the spray atomization properties such as Ohnesorge number and Sauter Mean Diameter (SMD) have been investigated. Air entrainment analysis has... 

Macroscopic and microscopic characteristics of spray of a Modified Bio-diesel Fuel (MBF), applying direct injection system have been explored and compared with those of conventional diesel fuel. MBF is a new combination of bio-diesel, molasses bio-ethanol, and water, which has been kept as a single-phase bio-fuel, employing an emulsifier. Lower emissions and production costs, higher oxygen content and cetane number are the key advantages of the MBF to be replaced by conventional fossil fuels in internal combustion engines. Applying atomization model, the spray atomization properties such as Ohnesorge number and Sauter Mean Diameter (SMD) have been investigated. Air entrainment analysis has... 

This paper studies the challenge of accurate FFT computation. A generic and error-free encoding is proposed based on the algebraic integers (AIs). A wise AI-based encoding may greatly decrease the error due to the non-trivial twiddle factors in the FFT computation. Further, a new method for predicting the well-pruned architecture is presented which helps designing an optimized and low-cost architecture when using the AI-based encoding. In order to examine the proposed AI-based FFT computation and also the procedure of designing an optimized architecture, a custom AI-based 16-point radix-2 2 FFT architecture has been designed and implemented using 180-nm CMOS technology. Experimental results... 

This paper introduces the concept of mapping of Artificially Intelligent (AI) computational systems. The concept of homunculus from human neurophysiology is extended to AI systems. It is assumed that an AI system behaves similarly to a mini-column or ganglion in the natural animal brain that comprises a layer of afferent (input) neurons, a number of interconnecting processing cells, and a layer of efferent (output) neurons or organs. The objective of the present study was to identify the correlation between the stimulus to each afferent neuron and the corresponding response from each efferent organ when the intelligent system is subjected to certain stimuli. To clarify the general concept, a...