Constanin Aliferis's ISP Comps Reading List
Reading list for comprehensive examination of C.F.Aliferis.
(Final version, November '94)
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Papers emphasizing applied and theoretical temporal reasoning
(1) Kahn MG " Modelling time in medical decision-support programs " Med
Dec Making
1991;11:249-264
(2) Allen JG "Towards a general theory of actions and time" Artificial
Intelligence
1984;23:123-154.
(3) Allen JF et al. "A common sense theory of time" IJCAI proceedings
1985:528-531.
(4) Ladkin P "Primitives and units for time specification" AAAI proc.
1986, 354-359.
(5) Chun HW "A representation for temporal sequence and duration in
massively parallel
networks" AAAI proc. 1986, 372-376.
(6) Portinale L "Modelling uncertain temporal evolutions in model-based
diagnosis"
Uncertainty in AI proc. 1992, 244-251.
(7) Dagum P et al. "Dynamic network models for forecasting" Uncertainty in
AI proc.
1992, 41-48.
(8) Kjaerulff U "A computational scheme for reasoning in dynamic
probabilistic networks"
Uncertainty in AI proc. 1992, 121-129.
(9) Andreassen S "Planning of therapy and tests in causal probabilistic
networks" Artificial
intelligence in medicine 4 ( ) 227-241.
(10) Sucar LE et al. "Expressing relational and temporal knowledge in
visual probabilistic
networks" Uncertainty in AI proc. 1992,303-309.
(11) Polaschek JX et al. "Using belief networks to interpret qualitative
data in the ICU"
Respiratory care 38 ( ):60-71, 1993.
(12) Berzuini C et al. "Bayesian networks for patient monitoring"
Artificial intelligence in
medicine 4 ( ) 243-260.
(13) Andreassen et al. "A model-based approach to insulin adjustment" Proc
AIME-91
Conf. 1991 239-248.
(14) Kanazawa K "A logic and time nets for probabilistic inference" AAAI
proc. 1991,
360-365.
(15) Dean T et al. "Probabilistic temporal reasoning" AAAI proc. 1988, 524-528.
(16) Kohane I. "Temporal reasoning in medical expert systems" MEDINFO
proc. 1986,
170-174.
(17) Provan G. "Tradeoffs in constructing and evaluating temporal
influence diagrams"
Uncertainty in AI proc. 1993, 40-47.
(18) Davis E "Representations of commonsense knowledge" Morgan Kaufman 1990.
(chapter 5 on temporal reasoning)
(19) Bacchus F et al "A non-reified temporal logic" Artificial
Intelligence 52 ( ) 87-108.
(20) Galton A "A critical examination of Allen's theory of action and
time" Artificial
Intelligence 42 ( ) 159-188.
(21) Shoham Y et al "Problems in formal temporal reasoning" 36 ( ) 49-61.
(22) Shoham Y "Temporal logics in AI: Semantical and ontological
considerations "
Artificial Intelligence 33 ( ) 89-104.
(23) Shoham Y et al "Temporal reasoning in artificial intelligence" in:
exploring artificial
intelligence, 419-437.
(24) Berzuini C et al "Temporal reasoning with probabilities" Uncertainty
in AI proc.
1989,14-21.
(25) B Blumenfeld "A connectionism approach to the recognition of trends
in time
ordered medical parameters", 1989 SCAMC 288-294
(26) T Leong "Dynamic decision modelling in medicine" 1993 SCAMC 478-484
(27) I Kohane et al "Hypothesis-driven data abstraction with trend
templates" 1993
SCAMC 444-448
(28) Thomason RH. Symbolic Logic: An Introduction. London: Collier-Macmillan,
1970. [NOTE: this reference is background knowledge for logic-based
temporal
reasoning work]
(29) Ramoni M et al "Forecasting Glucose concentration in diabetic
patients using
ignorant belief networks" AAAI spring symposium, March 1994
(30) Shahar Y et al. "Temporal -abstraction mechanisms in management of
clinical
protocols" Report KSL-91-19
(31) Dagum P et al. "Foundations of time in Bayesian Belief Networks"
(32) Goldszmidt M et al. "Action networks: a framework for reasoning about
actions and
change under uncertainty" Rockwell International Science center,
Technical memo
#126
* (33) Chatfield C "The analysis of time series" Chapman and Hall New York
1989
(chptrs 1-5, 9-11).
* (33) Afifi A, and Clark V "Computer-aided multivariate analysis" Van
Nostrand
Reinhold, New York 1990 (Chpt 13 Survival analysis)
Uncertain reasoning
(34) Bachus F. "Using first-order probability logic for the construction
of bayesian
networks" Uncertainty in AI proc. 1993, 219-226.
(35) Drudzel M. et al. "Causality in Bayesian belief networks" Uncertainty
in AI proc.
1993, 3-11.
(36) Cooper GF "A method for using belief networks as influence diagrams"
---55-63.
(37) J.Pearl: Probabilistic reasoning in intelligent systems, Morgan-
Kaufmann 1988.
(38) Cooper G "The computational complexity of probabilistic inference
using belief
networks" Artificial Intelligence 42:393-405.
(39) Henrion M "An introduction to algorithms for inference in belief
networks" in
Uncertainty in AI 5, 129-138, Amsterdam:North Holland.
(40) Winston W "Operations research" PWS-Kent, Boston (Chpts 19,20,21 on Markov
chains, deterministic, and probabilistic programming)
(41) Cormen TH, Leiserson CE, Rivest RL. Introduction to algorithms. Mc
Graw-Hill,
1992. (chapter on complexity theory)
(42) Neapolitan RE "Probabilistic reasoning in expert systems" (chpts 5,6,7).
(43) Krippendorf K "Information theory: Structural models for qualitative
data" Sage
university press, 1986
(44) Hovorka et a; "Causal probabilistic network modelling-An illustration
of its role
in the management of chronic diseases" IBM systems journal 1992;31:635-48
(45) Druzdzel M "Probabilistic reasoning in Decision Support Systems" (chpt 8,
explanation) PhD thesis 1993.
(46) Geiger et al. "Advances in probabilistic Reasoning" 7th UAI
proceedings, 118-126
(47) Egar JW et al. "Automated modelling of medical decisions" report KSL-93-30
(48) Dagum P et al "A Bayesian analysis of simulation algorithms for
inference in belief
networks" Networks 23 ( ) 499-516
(49) Dagum P et al "Approximating probabilisitic inference in Bayesian
belief networks
is NP-hard" Artificial Intelligence 60 ( ) 141-153
* (50) Madigan D et al "Strategies for graphical model selection"------
(51) Middleton et al. "A tutorial introduction to stochastic simulation
inference" AI in
Medicine 5 (1993) 315-340.
(52) Howard R et al . "Influence diagrams" in Readings on the principles
and applications
of decision analysis" vol II p721-762. Strategic Decisions Group,
Menlo Park CA.
* (53) Freund J "Mathematical statistics" (chpts 2-6,8 : background on
probability theory,
and sampling distributions, chpts 9,10,12 : background on decision
theory, estimation
and hypothesis testing)
* (54) Agresti A "Categorical data analysis" (chpts 1-5 : background on
uni- and
multivariate analysis)
* (55) Lee P "Bayesian statistics" (chpts 1-4 & 7-8: background on
bayesian estimation
and hypothesis testing)
* (56) Mooney C, Duval R "Bootsrapping", Sage University Press, 1990
(57) Dean T "Decision-Theoretic Planning and Markov Decision Processes"
(paper under
publication)
* (58) Howson C, Urbach P "Scientific reasoning" Open Court 1993
Machine learning
(59) Spirtes et al. "Causality, prediction and search" Springer Verlang 1992.
(60) Lam W et al "Using causal information and local measures to learn
bayesian
networks" Uncertainty in AI proc. 1993, 243-250.
(61) Singh M et al "An algorithm for the construction of bayesian network
structures from
data" Uncertainty in AI proc. 1993, 259-265.
(62) Suzuki J "A construction of bayesian networks from databases based on
an MDL
principle" Uncertainty in AI proc. 1993, 266-273.
(63) Neal RM "Connectionist learning of belief networks" Artificial
Intelligence 56 ( ) 71-
113.
(64) Cooper GF "A method for learning BN from databases that contain
hidden variables"
to appear in Journal of Intelligent Information Systems.
(65) Pearl J et al. "A statistical semantics for causation" Artificial
Intelligence frontiers in
Statistics: AI and statistics III, Hand DJ ed., Chapman and Hall 1993.
(66) Fung RM et al. "Constructor: a system for the induction of
probabilistic models"
AAAI proc.1990, 762-769.
(67) Cooper GF et al. "A bayesian method for the induction of
probabilistic networks from
data" Machine learning 9, 309-347 ( ).
(68) Blum R. "Discovery and representation of causal relationships from a
large time-
oriented clinical database: the Rx project" Comp. Biomed. Res.-----
(69) Rumelhart D et al. "Parallel distributed processing , volume 1 :
foundations" MIT
press 1986. [Chpts 1,7,8]
(70) McLelland J et al. "Parallel distributed processing, volume 2 :
psychological and
biological models" MIT press 1986. [Chpts 14,17,20,21,24,26]
(71) Hertz J et al. "Introduction to the theory of neural computation"
Addison-Wesley
1991.(chpts 5,6,7)
(72) Geiger et al. "Learning Gaussian Networks" Tech report MSR-TR-94-10
(73) Heckerman et al. "Learning Bayesian Networks: The combination of
knowledge and
statistical data"
(74) E. Herskovits "Computer-Based Probabilistic-Network Construction"
Ph.D. thesis,
Stanford, 1991.
(75) Mitchell T "Essentials of machine learning" (chpts 1-3 & 8 :
overview, tree induction, concept learning and genetic algorithms) (to be
published)
(76) "Artificial Intelligence" M.Ginsberg (chapter 15 on machine learning)
(77) "Computer systems that learn" S.Weiss and C.Kulikowski.
Morgan-Kauffman, 1991.
[NOTE: statistical approaches for model learning from data are referenced
in the two
previous sections, and are indicated with a "*"]