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1
Content available remote Intelligent Execution Monitoring in Dynamic Environments
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We present a robot control system for known structured environments that integrates robust reactive control with reasoning-based execution monitoring. It provides a robot with a powerful method for dealing with situations that were caused by the interaction with humans or that are due to unexpected changes in the operating environment. On the reactive level, the robot is controlled using a hierarchy of low-level behaviours. On the high level, a logical representation of the world enables the robot to plan action sequences and to reason about the state of the world. If the execution of an action does not have the expected effect, high-level reasoning allows the robot to infer possible explanations and, if necessary, to recover from the failure situation. For the robot to act optimally, the discrepancies between the internal world model and the real world have to be detected and corrected. The proposed system obtains new information about the world by executing sensing actions (active perception) and by sensory interpretation during the robot's operation. It also takes into account temporal information about changes in the environment. All updates of the world model are performed in a way that the changes are consistent with an underlying action theory. Having implemented the proposed system on a common mobile robot platform, we demonstrate the value of intelligent execution monitoring by means of two realistic office delivery scenarios.
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Content available Ontology-driven diagnostic modeling
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The paper presents a concept of diagnostic network models. This class of models consists of statement networks in which nodes represent statements concerning an object. The advantages of such models were enumerated and, in particular, their usefulness in collaborative research on knowledge acquisition was emphasized. Furthermore, not only the significance of dictionaries of statements contents, but also the purposefulness of supporting the development process of the models in question through definition of ontology referring to the studied objects was discussed.
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Content available remote Rough relation properties
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Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. This paper rewrites some properties of rough relations found in the literature, proving their validity.
EN
Set of Experience Knowledge Structure (SOEKS) is a structure able to collect and manage explicit knowledge of formal decision events on different forms. It was built as part of a platform for transforming information into knowledge named Knowledge Supply Chain System (KSCS). In brief, the KSCS takes information from different technologies that make formal decision events, integrates them and transforms them into knowledge represented by Sets of Experience. SOEKS is a structure that can be source and target of multiple technologies. Moreover, it comprises variables, functions, constraints and rules associated in a DNA shape allowing the construction of Decisional DNA. However, when having various dissimilar Sets of Experience as output of the same formal decision event, a renegotiation and unification of the decision has to be performed. The purpose of this paper is to show the process of renegotiating various dissimilar Sets of Experience collected from the same formal decision event.
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Content available remote Analysis of Approximate Petri Nets by Means of Occurrence Graphs
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Approximate Petri nets (AP-nets) can be used for the knowledge representation and approximate reasoning. The AP-net model is defined on the basis of the rough set approach, fuzzy Petri nets and coloured Petri nets. One of the main advantages of AP-net model is a possibility to present the reachability set of a given AP-net by means of an occurrence graph. Such graphs can serve, among others, for analyzing and evaluating an approximate reasoning realized by using AP-net model. The main contribution of the paper is to present the algorithms for construction and analysis of occurrence graphs for the AP-nets, especially in the context of searching for the best decision and finding the shortest distance in order to compute such decision. This approach can be applied to the design and analysis of the formal models for expert systems, control systems, communication systems, etc.
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Content available remote Conflicts in Legal Knowledge Base
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The simulation of inference processes performed by lawyers can be seen as one way to create advisory legal system. In order to simulate such a process as accurately as possible, it is indispensable to make a clear-cut distinction between the provision itself, and its interpretation and inference mechanisms. This distinction would allow for preserving both the universal character of the provision and its applicability to various legal problems. The author’s main objective was to model a selected legal act, together with the inference rules applied, and to represent them in an advisory system, focusing on the most accurate representation of both the content and inference rules. Given that the laws which stand in contradiction prove to be the major challenge, they will constitute the primary focus of this study.
EN
The article discusses the problem of knowledge representation language selection for domain ontologies. In the article the use of ontology as a tool of knowledge representation was presented and the analysis of logical formalisms such as frames, logic programs, description logic, first-order logic and common logic was carried out. Then a number of classic and markup based knowledge representation languages were analysed: Ontolingua, LOOM, OCML, FLogic, SHOE, RDF(S), OWL, OWL2. Based on the analysis of literature relationships and dependencies between versions and profiles of the OWL language were systematised. The article ends with the conclusion, according to which OWL 2 DL language is the most expressive language of retaining decidability, and therefore it is characterized by the highest applicability in the construction of domain ontologies allowing inference.
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Content available remote Meta-queries on deductive databases
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We introduce the notion of a meta-query on relational databases and a technique which can be used to represent and solve a number of interesting problems from the area of knowledge representation using logic. The technique is based on the use of quantifier elimination and may also be used to query relational databases using a declarative query language called SHQL (Semi-Horn Query Language), introduced in [6]. SHQL is a fragment of classical first-order predicate logic and allows us to define a query without supplying its explicit definition. All SHQL queries to the database can be processed in polynomial time (both on the size of the input query and the size of the database). We demonstrate the use of the technique in problem solving by structuring logical puzzles from the Knights and Knaves domain as SHQL meta-queries on relational databases. We also provide additional examples demonstrating the flexibility of the technique. We conclude with a description of a newly developed software tool, The Logic Engineer, which aids in the description of algorithms using transformation and reduction techniques such as those applied in the meta-querying approach
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The article presents problems of the transformation of knowledge codified in the COLREGs into a form which permits its use in navigational information systems. The need to develop a knowledge base in this field and implement it in an expert system supporting navigational decisions onboard ships is rational. It is a well-known fact that navigational information systems greatly support navigation and increase its safety. Examples of regulations (COLREGs rule 13 – overtaking) are interpreted, then presented as decision tables in order to check whether decision rule induction is possible from such data. Selected algorithms of rule induction are briefly described and then tested. The experiments are compared and conclusions are drawn.
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Content available remote A New Class of Fuzzy Petri Nets for Knowledge Representation and Reasoning
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This paper presents a new class of Petri nets called generalised fuzzy Petri nets. The new class extends the existing fuzzy Petri nets by introducing three operators in the form of triangular norms, which are supposed to function as substitute for the min, max and * (algebraic product) operators. To demonstrate the power and the usefulness of this model, an application of the generalised fuzzy Petri nets in the domain of train traffic control is provided. The new model is more flexible than the classical one as in the former class the user has the chance to define the input/output operators. The proposed approach can be used for knowledge representation and reasoning in decision support systems.
11
Content available remote Nested Weight Constraints in ASP
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Weight constraints are a powerful programming construct that has proved very useful within the Answer Set Programming paradigm. In this paper, we argue that practical Answer Set Programming might take profit from introducing some forms of nested weight constraints. We define such empowered constraints (that we call 'Nested Weight Constraints') and discuss their semantics and their complexity.
Studia Humana
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2015
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tom 4
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nr 4
3-12
EN
The concept a finite multi-carrier algebraic system (FMAS) as well as a language for handling systems such as YAFOLL (Yet Another First Order Logic Language) are introduced. The applicability of such systems to building a mathematical model of a part of reality, i.e. a mathematical structure that can be asked questions about the properties of subject domain objects and processes, is demonstrated.
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A Context Search algorithm used for lexical knowledge acquisition is presented. Knowledge representation based on psycholinguistic theories of cognitive processes allows for implementation of a computational model of semantic memory in the form of semantic network. Knowledge acquisition using supervised dialog templates have been performed in a word game designed to guess the concept a human user is thinking about. The game that has been implemented on a web server, demonstrates elementary linguistic competencies based on lexical knowledge stored in semantic memory, enabling at the same time acquisition and validation of knowledge. Possible applications of the algorithm in domains of medical diagnosis and information retrieval are sketched.
EN
Spirtes, Glymour and Scheines [19] formulated a Conjecture that a direct dependence test and a head-to-head meeting test would suffice to construe directed acyclic graph decompositions of a joint probability distribution (Bayesian network) for which Pearl's d-separation [2] applies. This Conjecture was later shown to be a direct consequence of a result of Pearl and Verma [21] , cited as Theorem 1 in [13] , see also Theorem 3.4 in [20]). This paper is intended to prove this Conjecture in a new way, by introducing the concept of p-d-separation (partial dependency separation) . While Pearl's d-separation works with Bayesian networks, p-d-separation is intended to apply to causal networks: that is partially oriented networks in which orientations are given to only to those edges, that express statistically confirmed causal influence, whereas undirected edges express existence of direct influence without possibility of determination of direction of causation. As a consequence of the particular way of proving the validity of this Conjecture, an algorithm for construction of all the directed acyclic graphs (dags) carrying the available independence information is also presented. The notion of a partially oriented graphs (pog) is introduced and within this graph the notion of p-d-separation is defined. It is demon strated that the p-d-separation within the pog is equivalent to d-separation in all dags.
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In this paper, acquisition of knowledge from user's programs written in Pascal to the knowledge base of the computer is introduced. It is shown that system with such a kind of knowledge can be used as the automatic programming system. First, representation of knowledge acquired by the computer from different programs is characterized. Then searching for pieces of knowledge in the knowledge base needed for synthesis of a program specified in user requirement is described. Finally, the construction of a new program from the found pieces of knowledge is shown.
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Content available remote On Fuzzy Reasoning Using Matrix Representation of Extended Fuzzy Petri Nets
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In 1990 Shyi-Ming Chen et al. presented a new approach to knowledge representation using fuzzy Petri nets (FPN). A fuzzy Petri net model allows a structural representation of knowledge and has a systematic procedure for supporting fuzzy reasoning. In this paper we propose an algebraic (matrix) representation of FPNs. We use this representation in a fuzzy reasoning algorithm which is simple to implement in modern programming languages such as C++, C# or Java. Furthermore, there exists MATLAB - a computer system which makes it possible to solve many computing problems, especially those with matrix and vector formulations. We present also an approach enabling us to carry out a fuzzy reasoning process using the MATLAB environment.
17
Content available remote Monitoring Agents using Declarative Planning
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In this paper we consider the following problem: Given a particular description of a multi-agent system (MAS), is it implemented properly? We assume that we are given (possibly incomplete) information about the system and aim at refuting its proper implementation. In our approach, agent collaboration is described as an action theory. Action sequences reaching the collaboration goal are computed by a planner, whose compliance with the actual MASbehaviour allows to detect possible collaboration failures. The approach can be fruitfully applied to aid in offline testing of a MASimplementation, as well as in online monitoring.
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Content available remote Probabilistic Complex Actions in GOLOG
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Uncertainty seems to be inherent in most robotic applications. This is because a robot's sensors and actuators are in general imprecise and prone to error. The logic-based action language GOLOG was introduced for the purpose of high-level robot control, but its usefulness was limited because it did not address uncertainty. bIn this paper we show how this deficiency can be overcome.
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Cognition is meant as the process of acquiring knowledge from the world. This process is supposed to happen within agents, which build such knowledge with the purpose to use it to determine their actions on the world. Following Peircean ideas, we postulate that such knowledge is encoded by means of signs. According to Peirce, signs are anything that can be used to represent anything else. Also, for Peirce, to represent means to be able to generate another sign, called the interpretant of the original sign, which still holds the same power of interpretability, I.e, its power to be transformed into a new sign, holding this same power. This happens through a processcalled semiosis, the process by which a sign is transformed into an interpretant. This whole process is performed with the aim of subsidizing the agent in deciding its behavior. So, even though the semiosis process has the power to continue infinitely, it usually stops whenever the generated interpretant brings enough information in order for the agent to effectively act in the world. We take signals to be the substract of signs. Signals are any physical property, which can be measured and captured by the agent, by means of its sensors. This includes any kind of internal memory the agent is able to have access, in order to operate. In this sense, signs can be both in the world (if these signals come from sensors) and within the own agent’s mind (if signals come from an internal memory). We understandan agent’s mind as the agents’ control system. In either case, signals can be abstracted as numbers. Not simply numbers, but numbers coming from specific sensors or specific memories. Using ideas from Peircean philosophy, in this work we postulate a pathway, in which signals, collected by either sensors or memory, can be organized in such a way that they can be effectively used as knowledge, in order for an agent to be able to decide its actions on the world, on the pursuit of its internal motivations. We postulate that agents identify and create a model of the world based on possibilities, existents, and laws, and based on this model, they are able to decide an action that maximizes the chance for the world to gain a shape, which the agents intend for it to be. This theory is postulated particularly for the case of artificial autonomous agents, meant to be constructed by engineering artifacts.
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We learn through experience. Our brain stores knowledge in terms of keeping our own experience from past situations as well as adding knowledge by learning from experiences of others. All these experiences, over generations, are stored in individual's DNA that carries this information into the future. Our idea is to develop an artificial system, an architecture that would support discovering, adding, storing, improving, and sharing knowledge through experience, in a way similar in some very general sense to what happens in nature. We propose a novel approach in which knowledge is represented by Set of Experience Knowledge Structure (SOEKS), and is carried into the future by Decisional DNA. This paper reports on our efforts at the stage of developing and example of SOEKS.
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