Fuzzy Set: 1965 … Fuzzy Logic: 1973 … BISC: 1990 … New Direction: 2000 - ….

BISC Phantoms

Born at the beginning of the second half of the last century, decision analysis, or DA for short, was the brainchild of von Neumann, Morgenstern, Wald, and other great intellects of that period. Decision analysis was, and remains, driven by a quest for a theory that is prescriptive, rigorous, quantitative, and precise. The question is: can this aim be achieved? A contention that is advanced in the following is that the aim is unachievable by a prolongation of existing theories. What is needed in decision analysis is a significant shift in direction--a shift from computing with numbers to computing with words and from manipulation of measurements to manipulation of perceptions. Decisions are based on information. More often than not, the decision-relevant information is a mixture of measurements and perceptions. The problem with perceptions is that they are intrinsically imprecise, reflecting the bounded ability of the human mind to resolve detail and store information. More specifically, perceptions are f-granular in the sense that (1) the boundaries of perceived classes are unsharp; and (2) the values of perceived attributes are granular, with a granule being a clump of values drawn together by indistinguishability, similarity, proximity, or functionality. For example, a perception of likelihood may be described as "very unlikely," and a perception of gain as "not very high." Existing decision theories have a fundamental limitation--they lack the capability to operate on perception-based information. To add this capability to an existing theory, T, three stages of generalization are required: (1) f-generalization, which adds to T the capability to operate on fuzzy sets, leading to a generalized theory denoted as T+; (2) f.g-generalization, which leads to a theory denoted as T++, and adds to T+ the capability to compute with f-granular variables, e.g., a random variable that takes the values small, medium, and large with respective probabilities low, high, and low; and (3) nl-generalization, which leads to a theory denoted as Tp, and adds to T++ the capability to operate on perception-based information expressed in a natural language. Perception-based decision analysis represents a significant change in direction in the evolution of decision analysis. As we move farther into the age of machine intelligence and automation of reasoning, the need for a shift from computing with numbers to computing with words, and from manipulation of measurements to manipulation of perceptions, will cease to be a matter of debate. Phantom Agents: The main characteristic of biological systems is their rich, but simple sensory and processing modalities which plays a key role in turning the individual's local behavior into a cohesive global intelligent behavior, capable of accomplishing complex missions. Significant advantages of collective Phantom Agents over the conventional agents include: robustness and system reliability through redundancy, reconfigurability of missions, distributed sensing through mixture of measurement and perception based sensors, and intelligent decision-making where the decision-relevant information is a mixture of measurements and perceptions. Ultimately, we want to design a new generation of collective Phantom Agents that use perception-based computing to carry out tasks requiring judgment, perception and higher level of intelligence declarative evolutionary collaborative learning for survival adaptation coordination of individual skills Based on Computational Theory of Perception Computing with words Reinforcement Learning Fuzzy-DNA Computing Fuzzy-Anticipation Perception-Based Decision Analysis evolution through DNA coding Phantom Decision is the next generation of intelligent decision analysis technology based on Computational Theory of Perceptions. It uses the current BISC technology such as l the BISC Decision Support System and the Perception-Based Decision Analysis (Decision Engine: Resources Allocation and Task Assignment) l the Perception-Based Reinforcement Learning and Artificial DNA Evolutionary Computing: For computing with Words and Perceptions; For Multi-objectives and Multi-Criteria Optimization Purposes and with capability to learn and to be self-aware l the Perception-Based Information Processing and Retrieval (as part of Precisiated Natural Language) to add deductive capabilities to the system l Computing with words: To add the capability to perform Perception-Based computing and Precisiated Natural Language Time Series Analysis  l Fuzzy-Anticipation: Event and task anticipation such as design of System of Flags for Attack and Danger Anticipation and provide the Anticipatory decision  Phantom Decision is used to make decision in a complex, uncertain, and distributed environment where decision is required to be made based on huge number of atomic decisions given huge number of distributed sensors with perception-based, measurement-based, and phantom-based information.