Archive for July, 1997

A New Heuristic Approach for Dual Control

Autonomous agents engaged in a continuous interaction with an incompletely known environment face the problem of dual control (Feldbaum, 1965). Simply stated, actions are necessary not only for studying the environment, but also for making progress on the task. In other words, actions must bear a “dual” character: They must be investigators to some degree, but also directors to some degree. Because the number of variables involved in the solution of the dual control problem increases with the number of decision stages, the exact solution of the dual control problem is computationally intractable except for a few special cases.

This paper provides an overview of dual control theory and proposes a heuristic approach towards obtaining a near-optimal dual control method that can be implemented. The proposed algorithm selects control actions taking into account the information contained in past observations as well as the possible information that future observations may reveal. In short, the algorithm anticipates the fact that future learning is possible and selects the control actions accordingly. The algorithm uses memory-based methods to associate long-term benefit estimates to belief states and actions, and selects the actions to execute next according to such estimates. The algorithm uses the outcome of every experience to progressively refine the long-term benefit estimates so that it can make better, improved decisions as it progresses. The algorithm is tested on a classical simulation problem.

Read the paper:

A New Heuristic Approach for Dual Control

by Juan Carlos Santamaria, Ashwin Ram

AAAI-97 Workshop on On-Line Search, Providence, RI, July 1997

Can Your Architecture Do This? A Proposal for Impasse-Driven Asynchronous Memory Retrieval and Integration

We propose an impasse-driven method for generating memory retrieval requests and integrating their contents dynamically and asynchronously into the current reasoning context of an agent. This method extends our previous theory of agent architecture, called experience-based agency (Ram & Francis 1996), by proposing a general method that can replace and augment task-specific mechanisms for generating memory retrievals and invoking integration mechanisms. As part of an overall agent architecture, this method has promise as a way to introduce in a principled way efficient high-level memory operations into systems based on reactive task-network decomposition.

Read the paper:

Can Your Architecture Do This? A Proposal for Impasse-Driven Asynchronous Memory Retrieval and Integration

by Anthony Francis, Ashwin Ram

AAAI-97 Workshop on Robots, Softbots, Immobots: Theories of Action, Planning and Control, Providence, RI, July 1997

Case-Based Planning to Learn

Learning can be viewed as a problem of planning a series of modifications to memory. We adopt this view of learning and propose the applicability of the case-based planning methodology to the task of planning to learn. We argue that relatively simple, fine-grained primitive inferential operators are needed to support flexible planning. We show that it is possible to obtain the benefits of case-based reasoning within a planning to learn framework.

Read the paper:

Case-Based Planning to Learn

by Bill Murdock, Gordon Shippey, Ashwin Ram

2nd International Conference on Case-Based Reasoning (ICCBR-97), Providence, RI, July 1997

Efficient Feature Selection in Conceptual Clustering

Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are discussed and an implementation of a sequential feature selection algorithm based on an existing conceptual clustering system is described. Additionally, we present a second implementation which employs a technique for improving the efficiency of the search for an optimal description and compare the performance of both algorithms.

Read the paper:

Efficient Feature Selection in Conceptual Clustering

by Mark Devaney, Ashwin Ram

14th  International Conference on Machine Learning (ICML-97), Nashville, TN, July 1997

Situation Development in a Complex Real-World Domain

Applying techniques from Machine Learning to real-world domains and problems often requires considerable processing of the input data, to both remove noise and to augment the amount and type of information present. We describe our work in the task of situation assessment in the domain of US Army training exercises involving hundreds of agents interacting in real-time over the course of several days. In particular, we describe techniques we have developed to process this data and draw general conclusions on the types of information required in order to apply various Machine Learning algorithms and how this information may be extracted in real-world situations where it is not directly represented.

Read the paper:

Situation Development in a Complex Real-World Domain

by Mark Devaney and Ashwin Ram

International Conference on Machine Learning (ICML-97) Workshop on Machine Learning Applications in the Real World, Nashville, TN, July 1997