SEKE 2012 Proceedings - Knowledge Systems Institute

specific learning method or heuristics to explain, resolve, or
accommodate the conflicting circumstance. Some human
interaction may be required at this stage.
Figure 1. Framework of i 2 Learning for Perpetual Learners.
The outcome of the learning process results in
knowledge refinement or augmentation to KB, mKB, or WM,
or all of them. Afterward, i 2 Learning module notifies CAL of
the learning result, and passes any WM revisions to CAL.
CAL in turn refreshes WM with any changes from the
i 2 Learning module and restarts the problem solving session
that was suspended earlier. This signifies the end of the
current learning burst and the agent is ready to return to the
problem solving episode to pick up where it lef t off. The
agent will continue engaged in problem solving until it
detects the next inconsistent scenario. Each such iteration
results in an incremental performance improvement for the
agent. Figure 2 illustrates the continuous nature of such
perpetual learning agents. The logic of t he i 2 Learning
module is given in Figure 3.
Figure 2. Spiral Model of Perpetual Learning Agents.
The types of learning that help improve the agent’s
performance are e ssentially embodied in the types of
inconsistency handling. There have been various
classifications of inconsistent data, information, knowledge,
and meta-knowledge in different domains [16-22].
1 Analyzing inconsistent scenario from WM;
2 Identifying category c and morphology m for
the inconsistency;
3 Choosing appropriate learning method or
heuristics;
4 Retrieving knowledge and bias;
5 Conducting i 2 Learning(c, m) {
6 case:
7 when (c=c i m=m ij ): i 2 Learning(c i ,m ij );break;
8 when (c=c k m=m kl ): i 2 Learning(c k ,m kl );break;
9 …………
10 else: default handling; break;
11 }
12 if (human interaction is needed) then {
13 Query expert;
14 Receive human response;
15 }
16 if (KB needs to be refined) then {
17 Refine KB;
18 }
19 if (mKB needs to be refined) then {
20 Refine mKB;
21 }
22 if (WM needs to be revised) then {
23 Revise WM and pass WM revisions to CAL;
24 }
25 Notify CAL to restart the current problem
26 solving session
Figure 3. The Learning Module.
The proposed framework is an overarching structure that
accommodates growth and ex pansion in various
inconsistency specific learning. Depending on which types
of inconsistency the learning module can recognize and
what corresponding algorithms or heuristics it comes
equipped with in handling the inconsistency at hand, its
inconsistency-induced learning capacities can change
dynamically. The inconsistent scenarios an agent encounters
at different points in time may be different and the learning
strategies it adopts in the subsequent learning bursts can
vary accordingly. The lines of 511 in Figure 3 can embody
a rich set of inconsistency-specific learning algorithms.
There are two modes of i 2 Learning: problem-solving
mode, and speedup mode, each having different pace or rate
for learning. When inconsistencies are encountered in WM
during problem solving sessions, an agent works under the
problem-solving mode and its pace of learning is driven by
how frequent conflicting decisions or act ions arise during
knowledge application. On the other hand, inconsistencies
can be intentionally injected into WM to induce learning
bursts during the time when an agent is n ot engaged in
problem solving. This latter case can be regarded as
inconsistency-induced speedup learning. The primary
objective of the agents is problem solving, and learning is
just the means for agents to get progressively better at what
they do.
251