The technical challenges here lie in computing appropriate summaries, synthesizing output in a given modality, and determining which modality (or combination) is appropriate for communicating a particular message or response. Current technology can generate grammatical text from certain knowledge-representation formats and can synthesize acceptable speech from unrestricted text (Subsection 3.8). Pioneering work in the automatic design of effective graphics holds much promise, but the field is still in its infancy (Subsection 3.9). Other important questions--how to select and combine appropriate interface modalities automatically, and how to model and exploit discourse structure in multimodal interfaces--have scarcely been considered. Research in several areas is needed to enable agents to tailor explanations to individual users (e.g. Subsections 3.2, 3.3, 3.5, 3.6).
2.1.2 Goal Orientation and Cooperation
Effective use of today’s technology requires memorizing the peculiarities of many resources, databases, and network services. For example, the Internet already supports simple video on demand, but relatively few users know how to use it. The problem of comprehensible access will only worsen with the widespread deployment of digital libraries and commercial transactions; it could become an insurmountable barrier for new users. The problem can be addressed in part with the application of interface conventions and standards, but the fundamental difficulty is that interaction with most applications is far too particular and detailed. What is needed is a technology that will raise the level and quality of discourse between people and machines.
Supporting a truly high level of discourse involves several challenges. Users should be able to phrase requests in terms of what they want accomplished and leave the problem of determining how to achieve that goal to the interface. For this to be possible, agents must be able to understand a wide range of goals, access thousands of NII databases and utilities, negotiate for desired resources owned by different entities with different pricing structures, and combine results obtained from diverse sources. AI planning techniques (Subsection 3.3) provide a solid basis for meeting this challenge; they enable a system to use a logical encoding of a user’s goal and a library of action schemata that describe available information sources, databases, utilities, protocols, and software commands to build, interpret, and execute a plan that will accomplish the desired objective. Unlike standard programs and scripts that are committed to a rigid control flow determined a priori by a programmer, a planner automatically and dynamically synthesizes and executes plans to accomplish a user’s goals. Recent work in collaborative planning even addresses the problem of how a user and computer might collaborate to formulate a shared plan when neither human nor computer alone knows how to achieve a desired goal (Subsection 3.6). In addition to raising the level of discourse, the planning approach avoids the problematic task of writing programs that anticipate all possible changes in system environment, network status, and error conditions.
2.1.3 Customization and Adaptivity
Many users like to customize the look and feel of their computer interface. Witness the proliferation of screen-saver applications, window backgrounds, and custom keyboards and mice. Why stop there? Users should be able to specify preferences about all aspects of system behavior, leaving it to the personal assistant agent to handle conflicts (for example, the conflict between a stated desire to use inexpensive services and an urgent demand). In addition to providing enhanced customization abilities, an intelligent agent-oriented interface must be adaptive. It must adjust automatically to the needs and idiosyncrasies of individual users, and it must change as the user’s experiences or requirements change.
Чтобы распечатать файл, скачайте его (в формате Word).
Ссылка на скачивание - внизу страницы.