Demonic memory is a form of reconstructive memory for process histories. As a process executes, its states are regularly checkpointed, generating a history of the process at low time resolution. Following the initial ...
ISBN:
(纸本)9780897913065
Demonic memory is a form of reconstructive memory for process histories. As a process executes, its states are regularly checkpointed, generating a history of the process at low time resolution. Following the initial generation, any prior state of the process can be reconstructed by starting from a checkpointed state and re-executing the process up through the desired state, thereby exploiting the redundancy between the states of a process and the description of that process (i.e., a computer program).The reconstruction of states is automatic and transparent. The history of a process may be examined as though it were a large two-dimensional array, or address space-time, with a normal address space as one axis and steps of process time as the other. An attempt to examine a state that is not physically stored triggers a “demon” which reconstructs that memory state before access is *** requires an exact description of the original execution of the process. If the original process execution depends on non-deterministic events (e.g., user input), these events are recorded in an exception list, and are replayed at the proper points during *** more efficient than explicitly storing all state changes, such a checkpointing system is still prohibitively expensive for many applications; each copy (or snapshot) of the system's state may be very large, and many snapshots may be required. Demonic memory saves both space and time by using a virtual copy mechanism. (Virtual copies share unchanging data with the objects that they are copies of, only storing differences from a prototype or original [MiBK86].) In demonic memory, the snapshot at each checkpoint is a virtual copy of the preceding checkpoint's snapshot. Hence it is called a virtual snapshot. In order to make the virtual snapshot mechanism efficient, state information is initially saved in relatively large units of space and time, on the order of pages and seconds, with single-word/single-s
Contrast-Detail (C/D) analysis allows the quantitative determination of an imaging system's ability to display a range of varying-size targets as a function of contrast. Using this technique, a contrast-detail plo...
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Welcome to the proceedings of the 9th International Conference on Intelligent Virtual Agents, held September 14–16, 2009 in Amsterdam, The Netherlands. Intelligent virtual agents (IVAs) are interactive characters tha...
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ISBN:
(数字)9783642043802
ISBN:
(纸本)9783642043796
Welcome to the proceedings of the 9th International Conference on Intelligent Virtual Agents, held September 14–16, 2009 in Amsterdam, The Netherlands. Intelligent virtual agents (IVAs) are interactive characters that exhibit hum- like qualities and communicate with humans or with each other using natural human modalities such as speech and gesture. They are capable of real-time perception, cognition and action, allowing them to participate in a dynamic physical and social environment. IVA is an interdisciplinary annual conference and the main forum for p- senting research on modeling, developing and evaluating IVAs with a focus on communicative abilities and social behavior. The development of IVAs requires expertise in multimodal interaction and several AI ?elds such as cognitive m- eling, planning, vision and natural language processing. Computational models are typically based on experimental studies and theories of human–human and human–robot interaction; conversely, IVA technology may provide interesting lessons for these ?elds. The realization of engaging IVAs is a challenging task, so reusable modules and tools are of great value. The ?elds of application range from robot assistants, social simulation and tutoring to games and artistic - ploration.
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralque...
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ISBN:
(数字)9783540476986
ISBN:
(纸本)9783540476979
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data (“Web mining”). Thisis due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.
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