Truth Maintenance System Research Articles

Machine Learning Implementation on Medical Domain to Identify Disease Insights

Machine learning has become as a part of our lives and we are living with the technology. We need to understand what is happening with the health of the person to be precise we need to analyze our own health. In this scenario we are implementing machine learning methodology in health care information for the problem statement of personalizing the medical information which is a private information we need to make it safe while using and implementing some sort of algorithms. In this paper we are discussing about understanding the human disease patterns and using which random forest and other machine learning models work and predict the actual procedure a person has to follow to get a good health and avoid the different health loss activities we are doing regularly. In this random forest is the most accurate algorithm worked with this concept and we need to analyze the other reasons for understanding which kind of information is most useful for performing machine learning. Machine learning cannot be implemented for all type of issues in the real time. But we can maintain a better break through of machine learning implementation on medical issues as mentioned in this article. We are performing a better algorithm to understand the human problems related to health care and we are proposing with sample implementations and explanation with relevant results. We tried to implement IT algorithm which gives the trust on the algorithm based on the truth maintenance system.

Local Regulation on Homelessness and Beggars in Yogyakarta: Human Rights and Maqasid of Sharia Perspectives

The existence of vagrants and beggars as a sub-culture of poverty is a separate problem for the government and the people of Yogyakarta in general. This research was conducted to examine and analyze the effectiveness of the Yogyakarta City Government Regional Regulation No. 1 of 2014 concerning the handling of homeless and human rights beggars in the review of maqasid shariah and seeks to find the crucial factors that influence the effectiveness of the DIY city government regulation based on a human rights perspective. humans in the view of maqasid syariah. The facts in the field were collected using qualitative methods with a sociological normative juridical approach and technical analysis using analytical descriptions. The results of this study are the Role and Functions of the Social Service in tackling Homeless and Beggars in the Special Region of Yogyakarta which has been procedurally fulfilled based on the Yogyakarta Regional Regulation No. 1 of 2014, but conceptually has not been fully implemented in the DIY City Regional Regulation No. 1 of 2014. In the perspective of human rights, in terms of quantity, the regional regulation has guaranteed the fulfillment of the rights of the homeless and beggars, the implementation is in accordance with standard operational procedures in the field. According to the Maqasid syariah perspective, the government's program to overcome sprawl has realized the maqasid syariah, namely the maintenance of religion (hifz din), reason (hifz al-aql) maintenance of the soul (hifz al-nafs) maintenance of offspring (hifz al-nasl) maintenance of property ( hifz al-mal)

STRATEGI PENGEMBANGAN MUSEUM SUBAK TABANAN SEBAGAI DAYA TARIK WISATA BUDAYA

Museum Subak Tabanan is one regional museum which is held by Dinas Kebudayaan Kabupaten Tabanan since Tabanan has the widest rice field in Bali that cultivate in traditional irrigation system called ‘Subak’. In this Museum, agricultural devices are kept and displayed, but it’s almost forgotten since the pandemi covid 19 known from number of its visitor become low. So here is needed a study to identify the potency as well as strategy to attract more visitor to Museum Subak Tabanan in order this museum can fulfill its function such as keep the artefacts, provide information and educate young generation. Main data is collected by interviewing the management, while supporting data is taken by observation and documentation study is. Data analized by SWOT analysis to find out the strength, weakness, opportunity and threat which later showed four strategies, named strategy SO, ST, WO, WT. The research found out two conclusions: (a) Museum Subak Tabanan has potency to develop such as collection, miniature, film documenter to display in audio visual room, and (b) strategy to be hold to attact more visitors such as create supporting attraction, build MoU with school or other institution, evaluate maintenance and storing system, promotion, and digitalization if possible. It is believed, with its strength and opportunity, it can invite visitor more. The research found out recommendation for the management to optimalize the museum as the suitable place for conservation and source of information needed by young generation who never see those collection in their life. For that reason, dusting and regular maintenance is necessary as well as supporting facilities, safety, and create more promotion.

Adaptive E-Learning System Using Justification Based Truth Maintenance System

In most E-learning systems, educational activities are presented in a static way without bearing in mind the particulars or student levels and skills. Personalization and adaptation of an E-learning management system are dependent on the flexibility of the system in providing different learning and content models to individual students based on their characteristics. In this paper, we suggest an Adaptive E-learning system which is providing adaptability with support of justification-based truth maintenance system. The system is accomplished of signifying students with suitable knowledge fillings and customized learning paths based on the student’s profile, interests, and previous results.

ANALISIS MAQASHID SYARIAH PADA INDEKS PEMBANGUNAN MANUSIA

This study aims to determine the concept of human development made by UNDP, and its relevance to the condition of the developing country Indonesia at this time , and how the Islamic judge it. In addition , this study also aims to lift Islamic persefektif related human development, as a critique falsifies Human Development Index which is an instrument in determining the countries in the world in the category of developed, developing, undeveloped, or underdeveloped. From the research conducted, it was found that the concept of human development according to the UNDP and maqashid banking is not face to face with each other because both concepts are not mutually contradictory. This can be explained by considering three factors of human development made by UNDP (health, education, income) using Shari'ah maqashid scales consisting of human development in the field of religion, life, intellect, lineage and property. From the results of the comparison can be concluded that part of the maintenance of mental health, education part of the reason maintenance, and maintenance revenue part of the treasure.

Diagnosing a Strong-Fault Model by Conflict and Consistency.

The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods.

Ticker: A system for incremental ASP-based stream reasoning

AbstractIn complex reasoning tasks, as expressible by Answer Set Programming (ASP), problems often permit for multiple solutions. In dynamic environments, where knowledge is continuously changing, the question arises how a given model can be incrementally adjusted relative to new and outdated information. This paper introduces Ticker, a prototypical engine for well-defined logical reasoning over streaming data. Ticker builds on a practical fragment of the recent rule-based language LARS, which extends ASP for streams by providing flexible expiration control and temporal modalities. We discuss Ticker's reasoning strategies: first, the repeated one-shot solving mode calls Clingo on an ASP encoding. We show how this translation can be incrementally updated when new data is streaming in or time passes by. Based on this, we build on Doyle's classic justification-based truth-maintenance system to update models of non-stratified programs. Finally, we empirically compare the obtained evaluation mechanisms.

Reason Maintenance in Product Modelling via Open Source CAD System

The present and future challenges of a new product design, forecasting and risk management launch strategy for a new product modelling decision process. This paper intends to propose and to look towards the development of a low-cost integrated CAD-CAPP-CAD/CAM product modelling system for the design and manufacture of a proposed product. It is a mapping between several design phases like functional design, technical design and physical design. The modelling data generation process begins with the drafting of a product to be maintained using the drafting software package. From the CAD drawing, the data are transferred to be used as the product models and a CAPP software package will then prepare the operational parameters for the manufacturing of the product. These process data are relayed to a CAM software package, which will then generate the automating information-processing functions. The final stage of the function is to support design and manufacturing operations that may have reaped many benefits in terms of its initial equipment and software costs.

Reason Maintenance in Product Modelling via Open Source CAD System

Reason Maintenance in Product Modelling via Open Source CAD System

What we can learn from conflicts in propositional satisfiability

Learning is a general concept, playing an important role in many Artificial intelligence domains. In this paper, we address the learning paradigm used to explain failures or conflicts encountered during search. This explanation, derived by conflict analysis, and generally expressed as a new constraint, is usually used to dynamically avoid future occurrences of similar situations. Before focusing on clause learning in Boolean satisfiability (SAT), we first overview some important works on this powerful reasoning tool in other domains such as constraint satisfaction and truth maintenance systems. Then, we present a comprehensive survey of the most important works having led to what is called today—conflict driven clause learning—which is one of the key components of modern SAT solvers. We also overview some of the original extensions or variants of clauses learning. In theory, current SAT solvers with clause learning are as powerful as general resolution proof systems. In practice, real-world SAT instances with millions of variables and clauses are now in the scope of this solving paradigm.

Engineering Kindness

The author provides first steps toward building a software agent/robot with compassionate intelligence. She approaches this goal with an example software agent, EM-2. She also gives a generalized software requirements guide for anyone wishing to pursue other means of building compassionate intelligence into an AI system. The purpose of EM-2 is not to build an agent with a state of mind that mimics empathy or consciousness, but rather to create practical applications of AI systems with knowledge and reasoning methods that positively take into account the feelings and state of self and others during decision making, action, or problem solving. To program EM-2 the author re-purposes code and architectural ideas from collaborative multi-agent systems and affective common sense reasoning with new concepts and philosophies from the human arts and sciences relating to compassion. EM-2 has predicates and an agent architecture based on a meta-cognition mental process that was used on India's worst prisoners to cultivate compassion for others, Vipassana or mindfulness. She describes and presents code snippets for common sense based affective inference and the I-TMS, an Irrational Truth Maintenance System, that maintains consistency in agent memory as feelings change over time, and provides a machine theoretic description of the consistency issues of combining affect and logic. The author summarizes the growing body of new biological, cognitive and immune discoveries about compassion and the consequences of these discoveries for programmers working with human-level AI and hybrid human-robot systems.

Abduction in work-planning problems in complex objects

Assumption-based truth maintenance systems (ATMSs) and basic concepts of operating these systems are described. An ATM-based abduction algorithm (AAA) is developed. A heuristic method for this algorithm is proposed. The experimental results of power-maintenance scheduling justified the algorithm's efficiency.

Lifecycle scenario design for product end-of-life strategy

This paper proposes a method for supporting the design of product lifecycles. The main approach involves supporting designers in determining a lifecycle strategy by describing lifecycle scenarios at an early stage of lifecycle design. The authors define a representational scheme for the lifecycle scenario and outline a support system based on the idea of the Cognitive Design Process model allowing the designers to examine various possibilities of lifecycle strategy. A number of alternative scenarios are managed by the Truth Maintenance System implemented in this approach. Finally, in order to embody the strategy in the later stages, the system derives requirements for product and process design. This paper outlines the lifecycle scenario of a cellular phone as a case study, which indicates the system's suitability for computer-aided description of scenarios and its facilitation of lifecycle strategy development.

Learning from conflicts in propositional satisfiability

Learning is a general concept, playing an important role in many Artificial intelligence domains. In this paper, we address the learning paradigm used to explain failures or conflicts encountered during search. This explanation, derived by conflict analysis, and generally expressed as a new constraint, is usually used to dynamically avoid future occurrences of similar situations. Before focusing on clause learning in Boolean satisfiability (SAT), we first overview some important works on this powerful reasoning tool in other domains such as constraint satisfaction and truth maintenance systems. Then, we present a comprehensive survey of the most important works having led to what is called today—conflict driven clause learning (CDCL)—which is one of the key components of modern SAT solvers. In theory, current SAT solvers with clause learning are as powerful as general resolution proof systems. In practice, real-world SAT instances with millions of variables and clauses are now in the scope of this solving paradigm.

Adaptive Fuzzy Interpolation

Fuzzy interpolative reasoning strengthens the power of fuzzy inference by the enhancement of the robustness of fuzzy systems and the reduction of the systems’ complexity. However, after a series of interpolations, it is possible that multiple object values for a common variable are inferred, leading to inconsistency in interpolated results. Such inconsistencies may result from defective interpolated rules or incorrect interpolative transformations. This paper presents a novel approach for identification and correction of defective rules in interpolative transformations, thereby removing the inconsistencies. In particular, an assumption-based truth-maintenance system (ATMS) is used to record dependences between interpolations, and the underlying technique that the classical general diagnostic engine (GDE) employs for fault localization is adapted to isolate possible faulty interpolated rules and their associated interpolative transformations. From this, an algorithm is introduced to allow for the modification of the original linear interpolation to become first-order piecewise linear. The approach is applied to a realistic problem, which predicates the diarrheal disease rates in remote villages, to demonstrate the potential of this study.

Large Scale Diagnosis Using Associations between System Outputs and Components

Model-based diagnosis (MBD) uses an abstraction of system to diagnose possible faulty functions of an underlying system. To improve the solution efficiency for multi-fault diagnosis problems, especially for large scale systems, this paper proposes a method to induce reasonable diagnosis solutions, under coarse diagnosis, by using the relationships between system outputs and components. Compared to existing diagnosis methods, the proposed framework only needs to consider associations between outputs and components by using an assumption-based truth maintenance system (ATMS) [de Kleer 1986] to obtain correlation components for every output node. As a result, our method significantly reduces the number of variables required for model diagnosis, which makes it suitable for large scale circuit systems.

Product configuration as decision support: The declarative paradigm in practice

AbstractProduct configuration is a key technology, which enables businesses to deliver and deploy individualized products. In many cases, finding the optimal configuration solution for the user is a creative process that requires them to decide trade-offs between conflicting goals (multicriteria optimization problem). These problems are best supported by an interactive dialog that is managed by a dedicated software program (the configurator) that provides decision support. We illustrate this using a real example (configuration of a business software system). This productively used application makes the user aware of which choices are available in a given situation, provides assistance in resolving inconsistent choices and defaults, and generates explanations if desired. One of the key configurator components used to manage this is a truth maintenance system. We describe how this component is used and two novel extensions to it: methods for declarative handling of defaults (of varying strength) and the declarative handling of incompleteness. Finally, we summarize our experiences made during the implementation of this application and the pros and cons of declarative versus procedural approaches.

AN INTEGRATED FRAMEWORK FOR SUSPECT INVESTIGATION

In a complex crime scene with many possible suspects and conflicting evidence, crime investigation requires scientific and logical steps to narrow down the suspects. Since human investigators have difficulty in fully handling all reasoning in this highly complex hypothesis space, we propose a decision support system to aid the investigation process. The system integrates a rule-based reasoner, a Bayesian network for criminal profiling, and an assumption-based truth maintenance system for evidential reasoning to determine the most plausible suspect. The reasoning process reasons about a profiling classification and an alibi credibility measure. It proved effective in a realistic simulation.

A Credibilistic Approach to Assumption-Based Truth Maintenance

This paper presents an extension of the assumption-based truth maintenance system (ATMS), called ATMS, which has the capability to cope with uncertain justifications and assumptions. Such justifications and assumptions are represented and dealt with in the framework of credibility theory. Important concepts in ATMS such as environments, labels, logical consequences, and consistency are redefined by the use of credibility measure. Based on these concepts, the label-updating procedure of the classical ATMS is extended, allowing effective computation of the membership function of any node within the network and that of its supporting environment. In addition, the contradictory environments can be captured with respect to their inconsistency degrees. This paper is compared to the most relevant existing research (i.e., ATMS using necessity as the truth value and ATMS using possibility as the truth value), demonstrating the significant improvements made. This paper also presents an illustrative application of credibilistic ATMS in supporting automated construction of domain models.

Abductive inference methods in problems of job planning in complex objects

Abductive inference methods in intelligent systems are considered. A description of assumption-based truth maintenance systems and basic concepts for dealing with assumption-based truth maintenance systems are presented. The following abductive inference algorithms are developed: Assumption-based Truth Maintenance System-based Abduction Algorithm and Implicate Abductive Algorithm using of prime implicates. These algorithms are compared. The results of experiments held for an example of designing schedules for energy storage plants have confirmed the efficiency of the Assumption-based Truth Maintenance System-based Abduction Algorithm.