Retrieving useful answers from large knowledge bases given under-specified questions is an important problem in the construction of general intelligent agents. The core of this problem is how to get the information an agent needs when it doesn’t know how to ask the right question and doesn’t have the time to exhaustively search all available information.
Context-sensitive asynchronous memory is a model of memory retrieval that solves this problem. The context-sensitive asynchronous memory approach exploits feedback from the task and environment to guide and constrain memory search by interleaving memory retrieval and problem solving. To achieve this behavior, a context-sensitive asynchronous memory uses an asynchronous retrieval system to manage a context- sensitive search process operating over a content-addressable knowledge base. Solutions based on this approach provide useful answers to vague questions efficiently, based on information naturally available during the performance of a task.
The core claims of this approach are:
• Claim 1: An efficient, domain-independent solution to the problem of retrieving useful answers from large knowledge bases given under-specified queries is to interleave memory retrieval with task performance and use feedback from the task or environment to guide the search of memory.
• Claim 2: Interleaving memory retrieval with and exploiting feedback from task performance can be achieved in a domain-independent way using a context- sensitive, asynchronous memory retrieval process.
• Claim 3: A rich, reified, grounded semantic network representation enables context-sensitive memory retrieval processes to retrieve useful information in a domain-independent way for a wide variety of tasks.
• Claim 4: To effectively use a context-sensitive asynchronous memory to retrieve useful answers, a task must be able to work in parallel with a memory process, communicate with it, provide feedback to it, and must possess integration mechanisms to incorporate asynchronous retrievals provided by the memory.
The context-sensitive asynchronous memory approach is applicable to tasks and domains which exhibit the following criteria: problems are difficult to solve, questions are difficult to formulate, a large knowledge base is available yet contains only a small selection of relevant information, and, most importantly, the environment is regular, in that solutions in the knowledge base occur in patterns and relationships similar to those found in situations in which the solutions are likely to be applicable in the future. This approach is domain independent: it is applicable to a wide variety of tasks and problems from simple search applications to complex cognitive agents.
To exploit context-sensitive asynchronous memory, reasoners need certain properties. Experience-based agency is an agent architecture which provides an outline of how to construct complete intelligent agents which use a context-sensitive asynchronous memory to support a reasoning system performing a real task. The experience-based agent architecture combines a context-sensitive asynchronous memory retrieval process with a global store of experience used by all agent processes, a global working memory to provide a uniform way to collect feedback, and a global task controller which orchestrates reasoning and memory. The experience-based agent architecture also provides principles for constructing integration mechanisms that enable reasoning tasks to work with the context-sensitive asynchronous memory.
Furthermore, to help determine when these approaches should be used, this research also contributes theoretical analyses that predict the classes of tasks and situations in which the context-sensitive asynchronous memory and experience-based agent approaches will provide the greatest benefit.
To evaluate the approach, the experience-based agent architecture has been implemented in the Nicole system. Nicole is a large Common Lisp program providing global long-term and working memory stores represented as a rich, reified, grounded semantic network, a context-sensitive asynchronous memory process based on a novel model of context-directed spreading activation, a control system for orchestrating reasoning and memory, and a task language to implement reasoning tasks. Nicole enables the context-sensitive asynchronous memory approach to be applied to real problems, including information retrieval in Nicole-IRIA, a information management application that uses context to recommend useful information (Francis et al. 2000), planning in Nicole-MPA, a case-based least-commitment planner that adapts multiple plans (Ram & Francis 1995) and language understanding in ISAAC (Moorman 1997), a story understanding system which uses Nicole’s retrieval system as part of its creative understanding process. Nicole and her children thus provide a testbed to evaluate the context-sensitive asynchronous memory approach.
Experiments with Nicole support the claims of the approach. Experiments with Nicole-IRIA demonstrate that a context-sensitive asynchronous memory can use feedback from browsing to improve the quality of memory retrieval, while experiments with Nicole-MPA demonstrate how information derived from reasoning can improve the quantity of retrieval. The use of Nicole’s memory in the ISAAC system demonstrates the generality of the context-sensitive asynchronous memory approach. Other experiments with Nicole-MPA demonstrate the importance of representation as a source of power for context-sensitive asynchronous memory, and further demonstrate that the core features of the experience-based agent architecture are crucial sources of power necessary to enable a reasoning task to work with and exploit a context-sensitive asynchronous memory.
In sum, these evaluations demonstrate that the context-sensitive asynchronous memory approach is a general approach to memory retrieval which can provide concrete benefits to real problems.
Read the thesis:
Context-Sensitive Asynchronous Memory
by Anthony Francis
PhD Thesis, College of Computing, Georgia Institute of Technology, Atlanta, GA, 2000
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-00-01.pdf
This book highlights cutting-edge research relevant to the building of a computational model of reading comprehension, as in the processing and understanding of a natural language text or story. A distinguishing feature of the book is its emphasis on “real” understanding of “real” narrative texts rather than on syntactic parsing of single sentences taken out of context or on limited understanding of small, researcher-constructed stories.
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