This paper examines the issues that arise in the control of blackboard systems for applications with large and complicated search spaces by analyzing the evolution of blackboard control architectures. We feel that the issues addressed here apply more generally to AI application domains involving complex multi-dimensional search, in which control knowledge is as important to successful problem solving as domain knowledge. Evolution is viewed largely from the context of the Hearsay-II (HSII) speech understanding system. The appeal of the blackboard model is that it provides great flexibility in structuring problem solving. On the other hand, many of the features that are responsible for this flexibility make effective control difficult because they complicate the process of estimating the expected value of potential actions. Among the key themes in the evolution of blackboard control is the development of mechanisms that support more sophisticated goal-directed reasoning. In the basic co...

This paper presents a computational model of real-time task-oriented dialogue skills. The architecture, termed Ymir, bridges between multimodal perception and multimodal action and supports the creation of autonomous computer characters that afford full-duplex, real-time face-to-face interaction with a human. Ymir has been prototyped in software, and a humanoid created, called Gandalf, capable of fluid multimodal dialogue. Ymir demonstrates several new ideas in the creation of communicative computer agents, including perceptual integration of multimodal events, distributed planning and decision making, an explicit handling of real-time, and layered input analysis and motor control with human characteristics. This paper describes the architecture and explains its main elements. Examples ofimplementation and performance are given, and the architectures limitations and possibilities are discussed.

chematically in Figure 1. It comprises a set of data structures (labeled boxes) in a global memory and a three-step execution cycle: (a) an executor executes the next action, making changes to memory and producing associated events; (b) an agenda manager triggers known actions whose conditions are satisfied by those events and puts contextspecific instances on an agenda of possible actions; and (c) a scheduler rates possible actions against the current control plan and chooses the one with the highest rating as the next action to be executed. Control plans play a central role in the architecture. As shown in Figure 1, the control plan is a data structure containing any number of component plans, each with its own hierarchical and temporal organization. Typical control plans are abstract and do not specify sequences of particular actions. Instead, each "step" in a plan specifies: (a) a class of intended actions in terms o

for Correspondence An agent should adopt different control modes in different situations. Depending on the predictability of its environment and the constraint imposed by its goals, the agent should modulate its sensitivity to run-time events and its commitment to specific actions. We propose an opportunistic control model that supports this flexibility. 3 Abstract Given its multiple goals, limited resources, and dynamic environment, an intelligent agent must decide which of many possible actions to execute at each point in time. Planning and reactive models embody two different modes of control. By contrast, we characterize a two-dimensional space of control modes, each of which maximizes the quality of run-time behavior in the corresponding region of a two-dimensional space of control situations. The situation space is defined by dimensions representing the predictability of the agent's task environment and the constraint imposed by its goals. The space of control modes is defined ...

Abstract- Scheduling is the process of devising or designing a procedure for a particular objective, specifying the sequence or time for each item in the procedure. Qpical scheduling problems are railway time-tabling, project scheduling, production scheduling, and scheduling computer systems as in flexible manufacturing systems and multiprocessor scheduling. Further, there are a number of related problems belonging to the larger class of planning problems, such as the early stage of project management and resource allocation in a job shop. Scheduling is a rich area demanding the application of efficient methods to tackle the combinatorial explosion that results in real world applications. Recent developments in artificial intelligence (AI) have led to the use of knowledge-based techniques for solving scheduling problems. In this paper, we survey several existing intelligent planning and scheduling systems with the aim of providing a guide to the main AI techniques used. In view of the prevailing difference in usage of the terms planning and scheduling between AI and OR, we present a taxonomy of planning and scheduling problems. We illustrate the modeling of real world problems b m closed deterministic worlds to complex real worlds with the project scheduling example. We survey some of the more successful planning and scheduling systems, and highlight their features. Finally, we consolidate the AI approaches to knowledge representation and problem solving in the project management context. Index Tern- Intelligent systems, knowledge-based systems, planning, project management, scheduling.

As companies attempt to increase customization levels in their product offerings, move towards smaller lot production, and experiment with more flexible customer/supplier arrangements such as those made possible by electronic data interchange (EDI), they increasingly require the ability to (1) respond quickly, accurately, and competitively to customer requests for bids on new or modified products and (2) efficiently work out supplier/subcontractor arrangements for these products. This in turn requires the ability to (1) rapidly convert standard-based product specifications into process plans and (2) quickly integrate process plans for new orders into the existing production schedule to best accommodate the current state of the manufacturing enterprise.

We are developing a generic control architecture suitable for use as a single intelligent agent or as multiple cooperating agents. The generic architecture combines a task-oriented domain controller with a meta-controller that schedules activities within the domain controller. The domain controller provides functions for model-based situation assessment and planning, and inter-controller communication. Typically, these functions are performed by modules taken from a repository of reusable software. In tasks that are simple, deterministic or time-stressed, the modules may be compiled into or replaced by conventional control algorithms. In complex, distributed, cooperative, non-deterministic or unstressed situations, these modules will usually exploit knowledge-based reasoning and deliberative control. To improve the controller development process, we are combining many of the best ideas from software engineering and knowledge engineering in a software environment. This environment incl...

Multimodal interaction between people is generally effortless and effective. We exchange glances, take turns speaking and make facial expressions to achieve the goals of the dialogue. Bringing such an interaction style to computers would constitute a relatively new kind of interface that relies on social convention and psychosocial dialogue skills. The proposed work deals with supporting such "full-duplex" relationship between human and machine. I propose a computational architecture where psychosocial competence is modeled as a closely integrated system of layered functional analysis and reactive-reflective behaviors. Multimodal output is generated in two phases, an issuance phase and an execution phase. This architecture will be used in a prototype interface, based on the metaphor of face-to-face communication, to control a graphical agent capable of speech and gesture. The agent's role will be to support and sustain dialogue with the user. Results of this work are hoped to shed ligh...

In this paper, we discuss the need for a hybrid reasoning approach to handing Semantic Web data and explain why we believe that the Blackboard Architecture is particularly suitable. We describe how we have utilised it for combining ontological inference, rules and constraint based reasoning within a Semantic Web context. After describing the metaphor on which the Blackboard Architecture is based we introduce the key components of the architecture: the blackboard Panels containing the solution space facts and problem related goals and sub-goals; the differing behaviours of the associated Knowledge Sources and how they interact with the blackboard; and, finally, the Controller and how it manages and focuses the problem solving effort. To help clarify, we use our test-bed system, the AKTive Workgroup Builder and Blackboard (AWB+B) to explain some of the issues and problems encountered when implementing a Semantic Web Blackboard System in Java, using Jena. We also discuss our reasons why we elected to use the Jena toolkit and explain its usage within several of the key components of our system. 1

This text adopts a cognitive viewpoint on design, focusing on individually conducted activities actually implemented in professional, industrial design projects. It presents elements for a cognitive descriptive model of design that, on the one hand, furthers our understanding of design, and on the other hand, offers elements to people who wish to use such knowledge in order to advance education and practice of professional designers. The text is especially concerned with dynamic aspects of design —that is, it focuses on the activity implemented by designers, especially the cognitive processes and/or strategies they use — rather than with static aspects. Section 1 presents the classical cognitive viewpoint on design, that is, the symbolic information-processing (SIP) approach, represented by Herbert A. Simon. Section 2 focuses on the main alternative to the SIP approach for design, i.e. the "situativity " (SIT) approach, mainly represented by Donald Schön. Section 3 is the main division of this text. It presents nuances and critiques with respect to both SIP and SIT approaches, and completes and integrates these two approaches into our own cognitively oriented dynamic approach to design.