SEKE 2012 Proceedings - Knowledge Systems Institute

i 2 Learning: Perpetual Learning through Bias Shifting
Du Zhang
Department of Computer Science
California State University
Sacramento, CA 95819-6021
zhangd@ecs.csus.edu
Abstract
How to develop an agent system that can engage in
perpetual learning to incrementally improve its problem
solving performance over time is a challenging research
topic. In this paper, we describe a framework called
i 2 Learning for such perpetual learning agents. The
i 2 Learning framework has the following characteristics: (1)
the learning episodes of the agent are triggered by
inconsistencies it encounters during its problem-solving
episodes; (2) the perpetual learning process is embodied in
the continuous knowledge refinement and revision so as to
overcome encountered inconsistencies; (3) each learning
episode results in incremental improvement of the agent’s
performance; and (4) i 2 Learning is an overarching
structure that accommodates the growth and expansion of
various inconsistency-specific learning strategies. Using
mutually exclusive inconsistency as an example, we
demonstrate how i 2 Learning facilitates learning through
bias shifting.
Keywords: inconsistency, i 2 Learning, inductive bias, bias
shifting, perpetual learning agents.
1. Introduction
One of the challenges in developing an agent system
that can engage in perpetual learning to incrementally
improve its problem solving performance over time is
deciding on what will trigger its perpetual learning
episodes. In this paper, we describe a f ramework called
i 2 Learning for such perpetual learning agents. i 2 Learning,
which stands for inconsistency-induced learning, allows for
inconsistencies to be utilized as stimuli to learning episodes.
The proposed framework has the following characteristics:
(1) the learning episodes of th e agent are trigg ered by
inconsistencies it encounters during its problem-solving
episodes; (2) the perpetual learning process is embodied in
the continuous knowledge refinement and revision so as to
overcome encountered inconsistencies; (3) each learning
episode results in incremental improvement of the agent’s
performance; and (4) i 2 Learning is an overarching structure
that accommodates the growth and expansion of various
inconsistency-specific learning strategies.
Inconsistencies are ubiquitous in the real world,
manifesting themselves in a plethora of human behaviors
and the computing systems we build [2,6,16-22].
Inconsistencies are ph enomena reflecting various causes
rooted in data, information, knowledge, meta-knowledge, to
expertise [22]. As such, inconsistencies can be utilized as
important heuristics in an agent’s pursuit of perpetual
learning capability. When encountering an inconsistency or
a conflicting circumstance during its problem solving
episode, an agent recognizes the nature of such
inconsistency, and overcomes the inconsistency through
refining or augmenting its knowledge such that its
performance at tasks is improved. This continuous and
alternating sequence of problem-solving episodes and
i 2 Learning episodes underpins the agent’s incremental
performance improvement process.
In this paper, our focus is on describing how the
i 2 Learning framework facilitates the development of
perpetual learning agents that incrementally improve their
performance over time. Because of its generality and
flexibility, the i 2 Learning framework accommodates
different types of inconsistencies and allows various
inconsistency-specific heuristics to be deployed in the
learning process. Using mutually exclusive inconsistency as
an example, we demonstrate how i 2 Learning facilitates
learning through bias shifting.
The rest of the paper is organized as follows. Section 2
offers a bri ef review on related work. Section 3 des cribes
i 2 Learning, the proposed framework for perpetual learning
agents. In Section 4, we discuss the i 2 Learning for a
particular type of inconsistency, mutually exclusive
inconsistency, and describe how an iterative deepening bias
shifting process can be incorporated into the framework to
accomplish the learning process. Finally, Section 5
concludes the paper with remarks on future work.
2. Related Work
The areas o f related work to the results in this paper
include: lifelong learning agent systems, learning through
overcoming inconsistencies, and inconsistency-induced
learning through bias shifting.
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