Machine Intelligence 6. Edited by B. Meltzer and D. Michie. Pp. 525. £10. 1971. (Edinburgh University Press.)

A system that performs automated concept acquisition from examples and has been specially designed to work in noisy environments is presented. The learning methodology is aimed at the target problem of finding discriminant descriptions of a given set of concepts and uses both examples and counterexamples. The learned knowledge is expressed in the form of production rules, organized into separate clusters, linked together in a graph structure. Knowledge extraction is guided by a top-down control strategy, through a process of specialization. The system also utilizes a technique of problem reduction to contain the computational complexity. Several criteria are proposed for evaluating the acquired knowledge. The methodology has been tested on a problem in the field of speech recognition and the experimental results obtained are reported and discussed. >

Automated concept acquisition in noisy environments

We present a classification algorithm for inferring affective states (emotions, mental states, attitudes, and the like) from their nonverbal expressions in speech. It is based on the observations that affective states can occur simultaneously and different sets of vocal features, such as intonation and speech rate, distinguish between nonverbal expressions of different affective states. The input to the inference system was a large set of vocal features and metrics that were extracted from each utterance. The classification algorithm conducted independent pairwise comparisons between nine affective-state groups. The classifier used various subsets of metrics of the vocal features and various classification algorithms for different pairs of affective-state groups. Average classification accuracy of the 36 pairwise machines was 75 percent, using 10-fold cross validation. The comparison results were consolidated into a single ranked list of the nine affective-state groups. This list was the output of the system and represented the inferred combination of co-occurring affective states for the analyzed utterance. The inference accuracy of the combined machine was 83 percent. The system automatically characterized over 500 affective state concepts from the Mind Reading database. The inference of co-occurring affective states was validated by comparing the inferred combinations to the lexical definitions of the labels of the analyzed sentences. The distinguishing capabilities of the system were comparable to human performance.

Classification of Complex Information: Inference of Co-Occurring Affective States from Their Expressions in Speech

This thesis has two main themes, autonomous agents and concepts. An autonomous agent is a system capable of interacting with its environment via its own sensors and effectors in order to accomplish some task. Arguments against both purely reactive and purely deliberative agent architectures are presented in favor for hybrid approaches. A novel hybrid approach based on the concept of anticipatory systems is suggested. The basic idea is to let a meta-level component ``run'' a world model faster than real time to make predictions of future states. These predictions are used to guide the agent's behavior on a high-level, whereas the low-level behavior is controlled by a reactive component. A specialization of this architecture, a linearly quasi-anticipatory agent architecture, which treats all agents in the domain (itself included) as being reactive, has been implemented. According to this approach, the state space is divided into desired and undesired regions. When the meta-level component detects that the simulated reactive system has reached an undesired state, it modifies the actual reactive system in order to avoid reaching this state. Results from both single and multi-agent experiments indicate that the behavior of such agents is superior to that of the corresponding reactive agents. Autonomous agents also provide the framework in which the representation and the acquisition of concepts are studied. However, these topics should not be studied without first answering some more fundamental questions regarding concepts, such as: What does it mean to have a concept? What functions do, or should, concepts serve? What is known about the nature of categories? Thus, by trying to answer these questions, we investigate the very concept of concepts. Although these topics seldom are discussed within Artificial Intelligence, they have received some attention in related fields, e.g., cognitive psychology and philosophy. One of the main goals of this thesis is to pull together different lines of argumentation that have emerged from the cognitive sciences in order to establish a solid foundation for further AI research. Previous approaches to concept representation are evaluated in the light of the answers to the questions above. It is concluded that none of the existing approaches is able to serve all the desired functions and that it is unrealistic to expect that any monolithic representation would be adequate. Based on this insight, a novel composite representation scheme is presented in which each component is motivated by the functions a concept should serve. Regarding the acquisition of concepts, some of the requirements that any autonomous concept learning system must meet are identified and provide the basis for an evaluation of the existing theories. A method for making any learning algorithm satisfy one such requirement, namely that of representing concepts by characteristic descriptions, is presented together with some promising experimental results. In contrast to previous methods for learning characteristic descriptions, it is possible with this method to control the degree of generalization. In addition, a new model for integrating learning by being told, learning from examples and learning by observation is outlined.

Autonomous Agents and the Concept of Concepts

A brief outline of A.I. is provided, noting problems, techniques and objectives which are similar to those found in O.R. It is intended that this will indicate to O.R. workers which A.I. areas might be worth following up as relevant to their own work and which problems in A.I. might be attacked by the O.R. community as part of the development of intelligent support systems.

Artificial Intelligence—An Overview of Similarities with O.R.

This paper presents an overview of a research programme on machine learning which is based on the fundamental process of categorization.

A structure of a computer model designed to achieve categorization is outlined and the knowledge representational forms and developmental learning associated with this approach are discussed.

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R. I. Phelps

Papers

A prototypical approach to machine learning

An automatic system for acquisition of natural concepts