Advances in Information and Intelligent Systems
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Contents:
Although the state-of-the art offers a substantial body of methods, representations, and algorithms, the full realization of this promise requires a concerted attack on several fundamental scientific problems. This report recommends several basic research initiatives in AI, each of which has high potential for large payback to the NII endeavor. Speech and image processing will contribute to improved user interfaces and enable automatic classification of multimedia content. Knowledge representation structures, plausible reasoning algorithms, and large-scale ontologies will enable NII systems to reason about user objectives and abilities and infer the databases and services of most interest.
Machine-learning and planning methods will provide the basis for systems that relieve the user from the need to memorize details of database protocols or personally track changes to network services; they can also be used to construct systems that automatically adapt to human preferences. Research on software agent architecture will enable more sophisticated interfaces, software development aids, and simulation systems. Development of computational models of collaboration will enable multiple software agents to coordinate and thus furnish enhanced network services; they can also provide the basis for building human-computer interface systems that collaborate with people in using NII resources to solve problems and perform tasks.
The workshop included a presentation by NSF of IITA program goals and a brief discussion of a report aimed at identifying important AI research thrusts that could support the development of twenty-first century computing systems. This report records the results of these discussions. The time from workshop presentation to written report was long, arduous, and fraught with debate and difficult decisions. We thank the editors for their efforts in producing this report. Special thanks to Dan Weld for his dedication and perseverance; his skill in unifying the varied contributions was critical to this report.
IEEE (SMC) INISTA 2018
The National Information Infrastructure NII will have a profound effect on the education, lifestyle, and well-being of Americans from every corner of society. The infrastructure will transport critical information and software to every home, open educational and training opportunities to remote communities, and accelerate commerce by reducing the time to develop new products and increasing the efficiency of markets.
Because electronic delivery is orders of magnitude faster than traditional transport, the NII will create new markets in information services and will spur development of strategic applications in areas such as health care, environmental monitoring, and advanced manufacturing. The NII is expected to grow to include a million networked information repositories that support fast access to medical images, interactive product simulations, digital libraries, and multimedia educational materials.
Current trends in semiconductor density, processor speed, and network bandwidth suggest that the infrastructure will be thousands of times larger than existing systems such as the Internet; the array of services supported by the NII will be unimaginably vast.
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But, who will be able to use the NII and take advantage of the opportunities it offers? Most people have no formal training in computers.
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They have little interest in the computer itself; rather, they want to find something or someone or accomplish some task. No matter how fast the computers of the future become, the NII will not achieve its full potential unless the infrastructure is flexible and easy to use. Instead of being a source for data, the NII should be a source for services and solutions. To prevent critical limitations in the NII, we must understand how people reason about the world and how they interact with each other; and we must engineer our machines to do the same.
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Artificial intelligence AI uses the theoretical and experimental tools of computer science to study the phenomena of intelligent behavior and to construct intelligent systems. The field is diverse and multifaceted--it addresses one of the most profound scientific problems, and also develops practical technology.
AI research has also produced an extensive body of principles, representations, algorithms, and spin-off technologies. Successful applications range from the DART system, which was used in deployment planning for Desert Shield, to broadly adopted symbolic math packages, such as Mathematicareg. In the next subsection, we provide an overview of the technical challenges confronting the NII. Then, we outline specific research areas with potentially large payback.
Sections 2 and 3 elaborate each of these points and link the challenges to the research areas.
Numerous obstacles block the development of both the NII and National Challenge applications in education, health care, advanced manufacturing, and electronic commerce. We discuss these challenges briefly here; in Section 2 we elaborate and explain the potential role of intelligent software systems in meeting these challenges. Current computer systems are complex and difficult to use even for experts.
The NII will be orders of magnitude more complex than the Internet, and could easily become a labyrinth of databases and services. For the NII to be accessible to all citizens, dramatic improvements must be made in the design of user interfaces This point is elaborated in Subsection 2.
An intelligent interface to NII resources could help people find and do what they want, when they want, in a manner that is natural to them, and without their having to know or specify irrelevant details of NII structure.
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A natural metaphor for such an interface is a software agent, an intelligent agent i. Users will want to communicate with their agents in familiar and flexible ways--by speaking English, drawing diagrams, or providing concrete examples. Agents should be goal oriented, allowing users to state what they want accomplished, then automatically determining how and when to achieve the goal. These agents should understand an expressive range of commands so that users can form questions or requests without having to learn--or be limited by--an artificial query language.
They should be cooperative, collaborating with the user to refine incorrect or incomplete requests. Furthermore, personal software agents should have the ability to be customized, automatically adapting to different users by following direct requests from users and learning from experience with them. Because the majority of transactions will be interactions between two autonomous programs, an NII equivalent of maps and signposts must be designed to provide guidance to software agents as well as people.
Three factors conspire to confound the task of developing flexible NII support services: scale, scope, and heterogeneity. The scope too will be vast: the stored information will range across all subjects. In addition, data will be represented in an incredible variety of forms, including various human languages, digital and video images, audio, geometric computer-aided design CAD models, mathematical equations, and database relations. A foundational suite of high-level information infrastructure services Subsection 2.
We envision at least three types of infrastructure services that could provide critical support for common problems: data and knowledge management services, integration and translation services, and knowledge discovery services. Data and knowledge management services address two common NII needs: 1 finding information that is relevant to your task or goals; 2 finding the right audience for a piece of information you have produced. These services allow information consumers to quickly locate useful facts and software resources in a huge morass of heterogeneous, distributed data.
Data that are similar in content can vary greatly in form and in the operations that can be performed on them.
Integration and translation services might convert information from one format to another subject to semantic constraints. For example, a financial translation service would not just perform the unit conversion from Japanese yen into U. Instead, knowledge discovery services could track the creation of new databases and updates to existing repositories. These services could cross-index related topics to discover new correlations and produce summaries. Current technology provides less support than the development of the NII and ambitious National Challenge applications require.
Many software development problems could be ameliorated by devising a set of powerful tools and environments Subsection 2.
We envision at least three distinct kinds of tools to which AI techniques could contribute: 1 rapid prototyping systems that combine services for specifying and refining designs with modular libraries of previously developed software and world knowledge; 2 intelligent project management aids that include software to promote collaboration and distributed decision making as well as next-generation project management software capable of checking resource utilization and assisting group leaders in replanning when unexpected conditions occur; 3 distributed simulation and synthetic environments to be used by applications for education, training, and computational prototyping of products.
A substantial body of AI research has addressed both the underlying nature of intelligence and the development of engineering algorithms necessary to reproduce rudimentary machine intelligence. This research has placed the field of AI in position to make enormous contributions to NII interfaces, flexible infrastructure, and development tools as well as to National Challenge applications. However, a concerted attack on several fundamental scientific problems is required to fully realize this promise. Here, we briefly present several key subareas within AI that we believe to be especially relevant to the development of a flexible and adaptive NII; in Section 3 we describe the state of the art of each of these AI subfields and suggest promising directions for research.
Research in knowledge representation Subsection 3. Knowledge representation is important to the NII because almost every intelligent computational activity depends on it to some degree. Knowledge representation systems offer the benefits of object-oriented databases and the structuring capabilities of hypertext-based libraries; they also provide increased expressiveness and more powerful algorithms for information retrieval and update.
Machine learning methods Subsection 3. These methods can be used to construct interface systems that adapt to the needs of individual users, programs that discover important regularities in the content of distributed databases, and systems that automatically acquire models of the capability of new network services. The field of planning Subsection 3. By reasoning about formal models of the capabilities and content of network services and databases, AI planning systems can focus information-gathering activities in profitable directions.
July , Development of such a flexible interface paradigm raises several challenges in the areas of machine perception and automatic explanation. The fields of speech and language processing Section 3. As a simple example, a monolithic map will not suffice for NII navigation, because users will want customized directions that are sensitive to factors such as individual objectives and local network congestion. Please accommodate for your bank fees in the transferred amount, so that the received amount at the CoMeSySo account is the exact conference fees, not less the bank fees usually called as OUR.
Because planning systems take a declarative goal specification as input, they can also help raise the level of user interfaces, allowing users to specify what they want done, then computing actions needed to achieve the goal and determining when these actions should be executed. Work in plausible reasoning Subsection 3. Algorithms have been developed to support diagnostic reasoning, causal inference, and evaluation of the tradeoffs between plan cost and goal satisfaction.
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