An approach to high-level interaction with autonomous robots by means of schematic maps is outlined. Schematic maps are knowledge representation structures to encode qualitative spatial information about a physical environment. A scenario is presented in which robots rely on highlevel knowledge from perception and instruction to perform navigation tasks in a physical environment. The general problem of formally representing a physical environment for acting in it is discussed. A hybrid approach to knowledge and perception driven navigation is proposed. Different requirements for local and global spatial information are noted. Different types of spatial representations for spatial knowledge are contrasted. The advantages of high-level / low-resolution knowledge are pointed out. Creation and use of schematic maps are discussed. A navigation example is presented.

Abstract—In this paper and attached video, we present a third-generation expert system named Knowledge Amplification by Structured Expert Randomization (KASER) for which a patent has been filed by the U.S. Navy’s SPAWAR Systems Center, San Diego, CA (SSC SD). KASER is a creative expert system. It is capable of deductive, inductive, and mixed derivations. Its qualitative creativity is realized by using a tree-search mechanism. The system achieves creative reasoning by using a declarative representation of knowledge consisting of object trees and inheritance. KASER computes with words and phrases. It possesses a capability for metaphor-based explanations. This capability is useful in explaining its creative suggestions and serves to augment the capabilities provided by the explanation subsystems of conventional expert systems. KASER also exhibits an accelerated capability to learn. However,

Abstract: Existing search engines—with Google at the top—have many remarkable capabilities; but what is not among them is deduction capability—the capability to synthesize an answer to a query from bodies of information which reside in various parts of the knowledge base. In recent years, impressive progress has been made in enhancing performance of search engines through the use of methods based on bivalent logic and bivalent-logic-based probability theory. But can such methods be used to add nontrivial deduction capability to search engines, that is, to upgrade search engines to question-answering systems? A view which is articulated in this note is that the answer is "No. " The problem is rooted in the nature of world knowledge, the kind of knowledge that humans acquire through experience and education. It is widely recognized that world knowledge plays an essential role in assessment of relevance, summarization, search and deduction. But a basic issue which is not addressed is that much of world knowledge is perception-based, e.g., "it is hard to find parking in Paris, " "most professors are not rich, " and "it is unlikely to rain in midsummer in San Francisco. " The problem is that (a) perception-based information is intrinsically fuzzy; and (b) bivalent logic is intrinsically unsuited to deal with fuzziness and partial truth. To come to grips with the fuzziness of world knowledge, new tools are needed. The principal new tool—a tool which is briefly described in their note—is Precisiated Natural Language (PNL). PNL is based on fuzzy logic and has the capability to deal with partiality of certainty, partiality of possibility and partiality of truth. These are the capabilities that are needed to be able to draw on world knowledge for assessment of relevance, and for summarization, search and deduction. 1.

We propose to extend the ontology of logical AI to include approximate objects, approximate predicates and approximate theories. Besides the ontology we treat the relations among different approximate theories of the same phenomena. Approximate predicates can’t have complete if-and-only-if definitions and usually don’t even have definite extensions. Some approximate concepts can be refined by learning more and some by defining more and some by both, but it isn’t possible in general to make them well-defined. Approximate concepts are essential for representing common sense knowledge and doing common sense reasoning. Assertions involving approximate concepts can be represented in mathematical logic. A sentence involving an approximate concept may have a definite truth value even if the concept is ill-defined. It is definite that Mount Everest was climbed in 1953 even though exactly what rock and ice is included in that mountain is ill-defined. Likewise, it harms a mosquito to be swatted, although we haven’t a sharp notion of what it means to harm a mosquito. Whatif(x,p), which denotes what x would be like if p were true, is an important kind of approximate object. The article treats successively approximate objects, approximate theories, and formalisms for describing how one object or theory approximates another.

Abstract: The paper introduces novel residuum-based reasoning systems in a pseudo— analysis based uninorm environment. Based on the definitions and theorems for lattice ordered monoids and left continuous uninorms and t-norms, certain distance-based operators are focused on, with the help of which the uninorm-residuum based approximate reasoning system becomes possible in Fuzzy Logic Control (FLC) systems, but as it will be shown, this type of the reasoning partially satisfies the conditions for approximate reasoning and inference mechanism for FLC systems. Keywords:FLC, approximate reasoning, uninorms 1

As most data models, "Computing with words" uses a mix of methods to achieve its aims, including several measurement indices. In this paper we discuss some proposals for such indices in the context of rough set analysis and present some new ones.

Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation.

Data mining offers a valuable tool for clinical decision support in diagnosis, treatment planning, and assessment. Due to several problems, the clinical acceptance of data-based designed systems is limited. To improve acceptance, the presented approach includes relevance information from the rule generation process of fuzzy systems into natural language text for result explanation.

The paper discusses the principles and the algorithm of granular analysis of data in a specific context of urban traffic monitoring and control (EIS). The proposed granular information processing enables extraction of information on the pattern of journeys from the detailed traffic counts. This facilitates progression from the local optimisation of traffic on individual crossroads to the more holistic optimisation of traffic in a road network. The proposed EIS makes use of readily available stop-line queue data, which is used for adaptive tuning of traffic signals, and adds a data processing layer referred to as granular analysis. It is argued that granular analysis is preferred to statistical data processing since it does not require any assumptions about statistical characterisation of traffic. The granulation algorithm has two distinctive features: (i) the information granules are formed by means of hierarchical optimisation of information density and (ii) the granules are created as hyperboxes thus being readily interpretable in the pattern space. The granular estimates of turning movements are calibrated using HUTSIM microsimulator. Key Words: Knowledge and information management, Simulation, Transportation. 1.

Mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. That is why mobile robotics problems are complex with many unanswered questions. To reach a high degree of autonomous operation, a new level of learning is required. On the one hand, promising learning theories such as the adaptive critic and creative control have been proposed, while on other hand the human brain’s processing ability has amazed and inspired researchers in the area of Unmanned Ground Vehicles but has been difficult to emulate in practice. A new direction in the fuzzy theory tries to develop a theory to deal with the perceptions conveyed by the natural language. This paper tries to combine these two fields and present a framework for autonomous robot navigation. The proposed creative controller like the adaptive critic controller has information stored in a dynamic database (DB), plus a dynamic task control center (TCC) that functions as a command center to decompose tasks into sub-tasks with different dynamic models and multi-criteria functions. The TCC module utilizes computational theory of perceptions to deal with the high levels of task planning. The authors are currently trying to implement the model on a real mobile robot and the preliminary results have been described in this paper.