The Association for the Advancement of Artificial Intelligence (AAAI) held its 1996 Fall Symposia Series on 9 to 11 November in Cambridge, Massachusetts. This article contains summaries of the seven symposia that were conducted: (1) Configuration; (2) Developing Assistive Technology for People with Disabilities; (3) Embodied Cognition and Action; (4) Flexible Computation: Results, Issues, and Opportunities; (5) Knowledge Representation Systems Based on Natural Language; (6) Learning Complex Behaviors in Adaptive Intelligent Systems; and (7) Plan Execution: Problems and Issues.

The next portion of the workshop was devoted to different approaches to preventing and repairing miscommunication. These sessions represent a progression between different parts of their discourse Research related to achieving from work that clarifies the model or between the discourse robust interaction is an important problem of miscommunication to model and the domain model. Early work concerned work that describes the strategies The last session was the presentation the correction of spelling or grammatical used to repair miscommunication. I of work involving deployed systems errors in a user's utterance so review the most significant issues using speech as a mode of interaction. The approaches were constrained by their have assumed that the system's model differed in two dimensions: First, experimenters impact on overall system performance is always correct.

This article was reprinted with permission from "ACM Computing Surveys: 28(4): December 1996, 653-670. original is available from ACM.

We are developing special-purpose, low-power analog-to-digital converters for speech and music applications, that feature analog circuit models of biological audition to process the audio signal before conversion. This paper describes our most recent converter design, and a working system that uses several copies ofthe chip to compute multiple representations of sound from an analog input. This multi-representation system demonstrates the plausibility of inexpensively implementing an auditory scene analysis approach to sound processing. 1. INTRODUCTION The visual system computes multiple representations of the retinal image, such as motion, orientation, and stereopsis, as an early step in scene analysis. Likewise, the auditory brainstem computes secondary representations of sound, emphasizing properties such as binaural disparity, periodicity, and temporal onsets. Recent research in auditory scene analysis involves using computational models of these auditory brainstem representations in engineering applications. Computation is a major limitation in auditory scene analysis research: the complete auditory processing system described in (Brown and Cooke, 1994) operates at approximately 4000 times real time, running under UNIX on a Sun SPARCstation 1. Standard approaches to hardware acceleration for signal processing algorithms could be used to ease this computational burden in a research environment; a variety of parallel, fixed-point hardware products would work well on these algorithms.

Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feed forward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feedforward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedforward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response.

Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feed forward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feedforward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedforward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response.

Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback withself-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feedforward synapses).A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedforward connectivity.During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response.

We are developing special-purpose, low-power analog-to-digital converters for speech and music applications, that feature analog circuit models of biological audition to process the audio signal before conversion. This paper describes our most recent converter design, and a working system that uses several copies ofthe chip to compute multiple representations of sound from an analog input. This multi-representation system demonstrates the plausibility of inexpensively implementing an auditory scene analysis approach to sound processing. 1. INTRODUCTION The visual system computes multiple representations of the retinal image, such as motion, orientation, and stereopsis, as an early step in scene analysis. Likewise, the auditory brainstem computes secondary representations of sound, emphasizing properties such as binaural disparity, periodicity, and temporal onsets. Recent research in auditory scene analysis involves using computational models of these auditory brainstem representations in engineering applications. Computation is a major limitation in auditory scene analysis research: the complete auditoryprocessing system described in (Brown and Cooke, 1994) operates at approximately 4000 times real time, running under UNIX on a Sun SPARCstation 1. Standard approaches to hardware acceleration for signal processing algorithms could be used to ease this computational burden in a research environment; a variety of parallel, fixed-point hardware products would work well on these algorithms.

As a field, knowledge representation has often been accused of being off in a theoretical no-man's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR-92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93).

As a field, knowledge representation has often been accused of being off in a theoretical no-man's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR-92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93).