mwang@watmath.UUCP (mwang) (07/05/84)
_D_E_P_A_R_T_M_E_N_T _O_F _C_O_M_P_U_T_E_R _S_C_I_E_N_C_E
_U_N_I_V_E_R_S_I_T_Y _O_F _W_A_T_E_R_L_O_O
_S_E_M_I_N_A_R _A_C_T_I_V_I_T_I_E_S
_A_R_T_I_F_I_C_I_A_L _I_N_T_E_L_L_I_G_E_N_C_E _S_E_M_I_N_A_R
- Monday, July 9, 1984.
Dr. John Tsotsos of the University of Toronto will
speak on ``Knowledge Organization: Its Role in
Representation and Decision-Making for Expert Sys-
tems.''
TIME: 3:30 PM
ROOM: MC 5158
ABSTRACT
The so-called ``first generation'' expert systems were
rule-based and offered a successful framework for
building applications systems for certain kinds of
tasks. Spatial, temporal and causal reasoning, the
structuring of knowledge, and the explanation of
knowledge in terms of knowledge abstractions and com-
parisons are among the topics of research for ``second
generation'' expert systems. It is proposed that one
of the keys for such research is knowledge organiza-
tion. Knowledge organization determines control struc-
ture design, explanation and evaluation capabilities
for the resultant knowledge base, and has strong influ-
ence on system performance. We are exploring a frame-
work for expert system design that focuses on knowledge
organization. In particular, the representation facil-
itates and enforces the semantics of the organization
of knowledge classes along the relationships of
generalization/specialization,
decomposition/aggregation, temporal precedence, causal
dependency, instantiation, and expectation-activated
similarity. A hypothesize and test control structure
is driven by the class organizational principles, and
includes several interacting dimensions of search
(data-driven, two forms of hypothesis-driven, temporal,
causal, and failure-driven search). The hypothesis
ranking scheme is based on temporal cooperative compu-
tation with hypothesis ``fields of influence'' being
defined by the hypothesis' organizational relation-
ships. This control structure has proven to be robust
July 5, 1984
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enough to handle a variety of interpretation tasks for
continuous temporal data. In addition, explanation
capabilities are enhanced by the multiple organization
principles of the knowledge classes as well as by the
resultant inherent redundancy. A variety of queries
involving different forms of knowledge abstractions and
comparisons can be handled. This framework has result-
ed in a representational language called PSN (Procedur-
al Semantic Networks), and two medical expert systems,
ALVEN (for the assessment of left ventricular perfor-
mance from X-ray films) and CAA (for the analysis of
arrhythmias).
July 5, 1984