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Inded: a distributed knowledge-based learning system - intelligent systems, ieee [see also ieee expert]

INDED: A Distributed
Knowledge-Based
Learning System
Jennifer Seitzer, James P. Buckley, and Yi Pan, University of Dayton
SOMETIMES DEFINED AS THENON-
THE AUTHORS' INDED SYSTEM PERFORMS RULE DISCOVERY
trivial process of identifying valid, novel,potentially useful, and understandable patterns USING THE TECHNIQUES OF INDUCTIVE LOGIC PROGRAMMING,
in data, knowledge discovery in databases AND ACCUMULATES AND HANDLES KNOWLEDGE USING
offers powerful solutions but requires sizablequantities of time and space.1 Data mining, part A DEDUCTIVE NONMONOTONIC REASONING ENGINE.
of the knowledge-discovery process, attempts TO SAVE TIME AND SPACE, THE AUTHORS RUN INDED
to reveal patterns within a database to exploitimplicit information that was previously un- IN PARALLEL ON A BEOWULF CLUSTER.
known.2 An IF-THEN rule (IF antecedentTHEN consequent), where the antecedent andconsequent are logical conjunctions of predi-cates (first-order logic) or propositions (propo- lacious and lead to nonsensical discovered background knowledge, INDED houses a sitional logic), often denotes such discovered rules. Some data, however, exhibits enough deduction engine that uses deductive logic patterns.3 Graphs and hypergraphs are also mutual exclusivity to render it partitionable programming to compute the current state used extensively as knowledge-representation among processors. This examination of par- (current set of true facts) as new rules and constructs because of their ability to depict titionability of data has been the underlying facts are procured.
causal chains or networks of implications by driving force of this work. A great deal of interconnecting the consequent of one rule to work has been done in parallelizing unguided Inductive logic programming. ILP em-
the antecedent of another.
discovery of association rules.5,6 The novel bodies a new research area in artificial In our system INDED (induction-deduc- aspects of our work include the paralleliza- intelligence that attempts to attain some tion, pronounced "indeed"), using the lan- tion of both a nonmonotonic reasoning sys- machine-learning goals while using logic- guage of logic programming, we use a tem and an inductive logic programming programming techniques, language, and hypergraph to represent the knowledge base learner. In this article, we describe the methodologies. Others have applied ILP to from which rules are mined. Because the schemes we have explored and are exploring data mining, knowledge acquisition, and sci- hypergraph gets inordinately large in IN- in this pursuit. We also present our data-par- entific discovery.7 An ILP system aims to DED's serial version,4 we have devised a titioning algorithms that we based on data output a rule that covers (entails) an entire parallel implementation that creates smaller set of positive observations, or examples, and excludes or does not cover a set of neg- In this article, we investigate the integrity ative examples.8 To construct this rule, ILP and meaning of decomposing data so that INDED's serial implementation
uses a set of known facts and rules (knowl- many processors can attempt to learn the edge) called domain or background knowl- same global pattern simultaneously (although Our knowledge-discovery system INDED edge. In essence, ILP tries to synthesize a locally, each discovered pattern is usually uses inductive logic programming7 as its dis- logic program, or at least part of a logic pro- unique). Many data decompositions are fal- covery technique. To maintain a database of gram, using examples, background knowl- 1094-7167/00/$10.00 2000 IEEE IEEE INTELLIGENT SYSTEMS
father(george, catherine). married(X,Y) ← married(Y,X). mother(catherine, mary). ancestor(X,X) ←. married(catherine, henry). ancestor(X,Z) ← ancestor(X,Y), mother(Y,Z).
father(henry, mary). ancestor(X,Z) ← ancestor(X,Y), father(Y,Z).
mother(elizabeth, henry). relative(Y,Z) ← ancestor(X,Y), ancestor(X,Z).
married(mary, bob).
relative(X,Y) ← relative(Y,X). inlaw(X,Z) ← relative(X,Y), married(Y,Z), relative(X,Z). Figure 1. An extensional database used for the family inlaw(X,Y) ← inlaw(Y,X).
tree example.
Figure 2. A family tree intensional database.
edge, and an entailment relation. The fol-lowing definitions come from Inductive father(george,catherine) ←. Logic Programming by Nada Lavrac and mother(catherine,mary) ←. married(catherine,henry) ←. father(henry,mary) ←. Definition 2.3 (coverage). Given back- mother(elizabeth,henry) ←. ground knowledge B, hypothesis H, and married(mary,bob) ←. example set E, hypothesis H covers married(catherine,henry) ← married(henry,catherine). example e ∈ E with respect to B if B  married(bob,mary) ← married(mary,bob). ancestor(elizabeth,mary) ← ancestor(elizabeth,george), Definition 2.4 (complete). A hypothesisH mother(george,mary). is complete with respect to back- ancestor(elizabeth,mary) ← ancestor(elizabeth,henry), ground B and examples E if all positive father(henry,mary). examples are covered—that is, if for all ancestor(catherine,henry) ← ancestor(catherine,george), e ∈ E+, B  H = e.
mother(george,henry). Definition 2.5 (consistent). A hypothesisH relative(george,mary) ← ancestor(henry,george), is consistent with respect to back- ancestor(henry,mary). ground B and examples E if no negative inlaw(bob,elizabeth) ← relative(bob,george), examples are covered—that is, if for all e ∈ E–, B  H e.
Definition 2.6 (formal problem state- ment). Let E be a set of training examplesconsisting of true E+ and false E– ground Figure 3. A family tree ground instantiation section.
facts of an unknown (target) predicate T.
Let L be a description language specify-ing syntactic restrictions on the definitionof predicate T. Let B be background tions of these semantics, for this article, we Framework of operation. The input files
knowledge defining predicates qi that can intuitively accept stable and well- to INDED that initialize the system are an may be used in the definition of T and founded models as those sets of facts gener- extensional database and an intensional that provide additional information about ated by transitively applying modus ponens database. The EDB comprises initial ground the arguments of the examples of predi- to rules.) This deduction engine is, essen- facts (facts with no variables, only con- cate T. The ILP problem is to produce a tially, a justification truth maintenance sys- stants). For example, the EDB in Figure 1 definition H for T, expressed in L, such tem that accommodates nonmonotonic comes from the universal family tree do- that H is complete and consistent with updates in the forms of positive or negative main.12 The family tree is a canonical appli- respect to the examples E and back- cation of logic programming and exempli- ground knowledge B. The induction engine, using the current fies the ability to represent relations with a state that the deduction engine creates as the Serial architecture. INDED comprises two
background knowledge base, along with pos- The IDB consists of universally quantified main computation engines. The deduction itive examples E+ and negative examples E–, rules; we assume the syntactic constraint that engine is a bottom-up reasoning system that induces rules that we can then use to augment each IDB contains no constants, only vari- computes the current state by generating a the deductive engine's hypergraph. INDED ables. Figure 2 displays one possible IDB stable model, if one exists, of the current uses a standard top-down hypothesis con- used in the family tree domain. ground instantiation represented internally struction algorithm (learning algorithm).7 Together, the EDB and IDB form the inter- as a hypergraph, and by generating the well- Two user-input values that indicate suffi- nal ground instantiation represented inter- founded model,9 if no stable model exists.10 ciency—and necessity—stopping criteria nally as the deduction engine hypergraph.
(Although we have cited the formal defini- dictate termination. Because this process enumerates all possible heir(mary,henry). heir(mary,george). mother(catherine,mary). heir(mary,catherine). heir(catherine,george). father(henry,mary). married(mary,bob). Figure 5. Positive example set to learn heir(X,Y).
married(mary,bob). married(bob,mary). heir(bob, george). heir(bob, catherine). relative(george,mary). heir(catherine, george). inlaw(bob,elizabeth). heir(george, catherine). ancestor(mary, george). heir(bob,mary). mother(george, bob). heir(george,mary). relative(mary, bob). inlaw(mary, bob). heir(mary,bob). inlaw(catherine, mary).
heir(catherine,henry). Figure 4. Part of the family tree domain background knowledge.
Figure 6. A negative example set to learn heir(X,Y).
combinations of instantiations of constants attempt to learn a rule that answers the to variables, the ground instantiation grows question, "What is an heir?" Each fact of heir(X,Y) ← mother(Y,X). exponentially. Figure 3 shows a small part of E+ represents a pair of constants that relate heir(X,Y) ← ancestor(Y,X).
the ground instantiation used in the family to one another through the heir(X,Y) rela- tree example.
tion and can be read "X is the heir of Y." Figure 7. Learned rules defining heir(X,Y).
The ground rules do not necessarily make Figure 5 displays a positive example set semantic sense. Currently, the instantiation exemplifying the notion.
process is an exhaustive, mechanical proce- Additionally, the induction engine also dure that assigns all possible combinations uses a negative example set E–. This set of that do not depict the relationship of of all constants to the variables. Part of our negated ground facts also pertain to the tar- heir(X,Y). Figure 6 shows the negative current research involves devising ways of get predicate. These facts represent pairs examples employed for our family tree limiting the instantiation process so as to pro- that do not exhibit the relationship ex- duce a smaller hypergraph.13 pressed by the target predicate. In our exam- Ultimately, from the three input files, E+, The inference engine (also called deduc- ple, these facts indicate pairs of constants E–, and B, the induction engine produces a tion engine) operates on the ground instan-tiation to deduce the current state, or equiv-alently, the domain background knowledge.
The current state is syntactically similar to Positive examples the EDB; it is a collection of facts. Here, however, the engine includes both positiveand negative facts. Again, because of the huge size, Figure 4 shows a small part of the deduced domain background knowledge.
With the generated domain background Negative examples knowledge base, we can induce new knowl- edge in the form of implications. In IN- DED, the background knowledge serves asinput to the induction engine for this pur-pose. The induction engine also uses a pos- Background knowledge itive example set E+. This is a set of ground facts that pertain to the target predicate (thepredicate that is the consequent—equiva-lently, the head—of the implication beinglearned). In the family tree example, we Figure 8. Inded's induction and deduction engines.
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set of clauses that define the target predi-cate. That is, for each clause (equivalently, Input: Target example sets E = E+ ≈ E–, domain knowledge B,
rules or implications), the target predicate serves as the head, and the chosen predi- Output: Intensional rule(s) of learned hypothesis H
cates from the background knowledge B BEGIN ALGORITHM 2.7 form the rule antecedent (body). Figure 7 while ( Number Pos examples covered / E+ ) ≤
displays the induced clauses that define Make new intensional rule: In every iteration, as control shifts from –head is the target predicate found in E = E+ ≈ E– the deduction to the induction engine, –make body as follows: INDED learns exactly one target predicate while (Number Neg examples covered/ E– ) ≥
and produces one set of clauses, in the form of implications, which define the target - Add literal to the body by choosing predicate. The following framework gen- the highest ranked unchosen predicate eralizes the above integration of a bottom- symbol in the background knowledge base up (forward reasoning) nonmonotonic - Choose the variables of this literal deductive system with a top-down, ILP learning system. The actions reflect IN- Append the new intensional rule to the rule set H to be returned DED's iterative, synergistic behavior. The following lists the steps of one iteration (see END ALGORITHM 2.7 • Compute the ground instantiation of Figure 9. Algorithm 2.7. We have implemented this generic top-down hypothesis algorithm in Inded. The algorithm logic program P called PG, using the forms the underpinnings of other top-down ILP systems such as FOIL and Golem.7 EDB and IDB as inputs. Store PG in a • Compute the current state B of the knowledge base using well-founded INDED's induction engine returns the set of one atom is not assigned true or false), we models, stable models, or both of PG.
intensional rules in the order of generation.
factor out the total part (the hypergraph part • Induce hypothesis H, using E+ , E–, and For example, the first generated intensional housing atoms that were assigned) and B (as the domain background knowledge rule is the first in the returned set. assign truth values to the remaining sub- base) as inputs to the induction engine.
Algorithm 2.7, a standard hypothesis graph by finding the first truth assignment • Augment the IDB with newly learned construction algorithm (learning algo- that is a stable model, if such an assignment intensional rules.
rithm) used in INDED, (see Figure 9) uses exists. The resultant set of true facts along and combines the answers to the five ques- with the negations of the false facts form Induction engine. Any ILP system's ulti-
tions. A generic top-down ILP hypothesis the background knowledge base, which mate goal is to generate a set of intensional construction algorithm typically uses two INDED uses to indicate the current state in rules. Creating this set of rules requires nested programming loops. The outer (cov- the deduction engine, or which INDED can answers to five questions. Here, each answer ering) loop attempts to cover all positive use to induce more knowledge in the induc- manifests as an algorithm in INDED's induc- examples, while the inner loop (special- tion engine.
ization) attempts to exclude all negativeexamples.
How many distinct rules must be cre- ated for the same target predicate? The deductive engine. INDED's deduc-
Apply Covering Algo to E+. tive-reasoning system represents the Memory limitations greatly limit the prob- How do we choose the target predicate ground instantiation P of the combined
lems that the serial version can address. By variables? Apply Reflexive Nam- EDB and IDB as a hypergraph PG. INDED
parallelizing INDED we aim to represents each atom a ∈ P as a vertex with
For any given rule, how many literals a set of incoming hyperedges, where each output faster learned rules and obtain must we include in the rule body? Apply hyperedge corresponds to one rule body of higher-quality rules than does serial Covering Algo to E–.
which a is the head. Also associated with For any given rule, which literal do we each vertex is a set of outgoing hyperedge increase the problem space by decreas- choose? Apply Predicate Ranking parts, each corresponding to one (positive ing the size of the internal deduction or negative) appearance of a in the body of For any chosen literal, how do we some rule r ∈ P. To compute the current
choose the constituent variables? Apply state, INDED first acquires the well- In our pursuit to parallelize INDED, we Chosen Pred Variable Naming founded model using the Bilattice algo- are investigating many schemes. We have rithm.14 If the model is not total (at least designed each scheme for implementation on a Beowulf cluster—a collection of personal Our strategy lets the induction engine all specified target predicates.
computers coordinated to form a supercom- initially discover a target predicate from Extending this implementation, we acquire puter.15 Physically, a local area network in- positive and negative examples and an ini- a pipelined system where the deduction terconnects the computers. The collective tial background knowledge base. Mean- engine computes state Si+1 while the induc- execution of a set of protocols forming a while, the deduction engine computes the tion engine uses Si to induce new rules (where portable parallel-programming package such current state using the initial input files.
i is the current iteration number).
as MPI (Message Passing Interface) or PVM This current state is sent to the induction (Parallel Virtual Machine) handles software engine as its background knowledge base Data-parallel decomposition with data
in the subsequent iteration. INDED then partitioning. In this method, each worker
We are using the following paralleliza- feeds the learned predicate from the induc- MPI node runs INDED when invoked by the tion schemes, each of which addresses one tion engine from one iteration into the master MPI node.16 Each worker executes by of our previously stated parallelization deductive engine for use during the next running a partial background knowledge iteration in its computation of the current base which, as in the serial version, its deduc- state. The induction engine then uses the tion engine spawns. Worker nodes create the large-grained-control parallel decompo- current state as the background knowledge partial background knowledge bases in one sition, where one cluster node runs the for the induction engine during the subse- of two ways.
induction engine while another node runs In the first method, each worker receives the deduction engine; the full serial IDB and a partial EDB. Using large-grained-control parallel decompo- a partial EDB creates a significantly sition, where we establish a pipeline of smaller (and different) hypergraph on each processors, each operating on a different THREE TYPES OF LOCALITY
Beowulf worker node. In some cases, we current state as created in previous (or have found the accuracy of rules obtained OF REFERENCE CAN
subsequent) pipelined iterations; collectively by the processors housing the data-parallel decomposition, where each COEXIST IN A KNOWLEDGE
smaller hypergraphs to equal those ob- node runs the same program with smaller tained in the original serial system. More- BASE SYSTEM: SPATIAL,
input files (hence smaller internal hyper- over, by using this parallel version, we can grapple with problems involving larger TEMPORAL, AND FUNCTIONAL.
a speculative parallel approach, where knowledge bases than those workable on each node attempts to learn the same rule the serial system. This decomposition leads using a different predicate-ranking algo- to a faster execution owing to the signifi- rithm in the induction engine.
cantly smaller internal hypergraph being quent iteration.
built. The challenge is to determine the best Naive and pipelined decomposition. Be-
During iteration i, the induction engine way to decompose the serial EDB into cause the induction and deduction engine generally computes new intensional rules for smaller ones so that the obtained rules were depend on each other as shown in Figure 8, the deduction engine to use in its computa- as accurate as those learned by the serial parallelizing the two engines is difficult. In tion of the current state in iteration i + 1.
this decomposition, we create a very coarse- Simultaneously, during iteration i, the deduc- The second method consists of creating grained system in which two nodes share tion engine computes a new current state for a partial background knowledge base by the execution. One node houses the deduc- the induction engine to use as its background directly partitioning the generated back- tion engine; the other houses the induction knowledge in iteration i + 1. This process ground knowledge from the deduction en- repeats until the deduction engine discovers gine. Because the induction engine orga- Table 1. Temporal and functional locality in data.
ancestor(mary,mary). ancestor(george,mary). Figure 10. Background knowledge partitioning for worker nodes.
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nizes the background knowledge by pre- ular brand of toothpaste on Monday, we receive a full copy of the serial IDB. The ser- dicate expression, and no precedences are will see numerous sales for this brand on ial EDB—the initial set of facts, therefore— posed between the predicate expressions, is decomposed and partitioned among the partitioning the background knowledge is In functional locality of reference, we nodes. The algorithm in Figure 11 trans- quite easy. Each node receives small back- appeal to a semantic relationship between forms a large serial EDB into p smaller ground knowledge bases solely comprising entities that can reside physically removed EDBs to be placed on p Beowulf nodes. It atoms related to one (or a small number of) from one another and those that the data- systematically creates sets based on con- predicates. For example, Figure 10 shows base can represent in different sections.
stants appearing in the positive example set one such part from partitioning our back- Items exhibiting functional locality of ref- E+. Some facts from the serial EDB could ground knowledge base for our family tree erence, however, have a strong semantic tie appear in more than one processor.
that affects data transactions relating to Load balancing is another interesting them. We can exploit all three of these Global hypergraph using speculative
challenge that we have undertaken in this localities in distributed knowledge mining parallelism. In this parallelization, each
decomposition. To keep execution time and help justify the schemes adopted in our Beowulf node searches the space of all pos- roughly equal on all worker nodes, we are implementations. sible rules independently and differently.
attempting to keep data files roughly equal All machines have the same input files.
in size. Because generally this is an NP- Therefore, each worker is discovering from complete problem similar to bin-packing, the same background knowledge base.
we employ an approximation algorithm Every rule discovered by INDED is con- where a binary heap organizes each predi- structed by systematically appending cho- cate grouping.
sen predicate expressions to an originally TO RETAIN ALL GLOBAL
empty rule body. Employing various algo- Data partitioning and data locality. In our rithms, each of which designates a different pursuit of partitioning the EDB, we found search strategy, ranks the predicate expres- that data transactions frequently exhibited a THE PREDICATES IN THE
sions. INDED chooses the highest-ranked form of locality of reference and based our expressions to constitute the rule body CURRENT STATE, ALL
partitioning schemes on this observation.
under construction. In this parallelization of Cache systems ardently exploit locality of BEOWULF NODES RECEIVE
INDED, each Beowulf node employs a dif- reference, where the general area of mem- ferent ranking procedure and hence can con- A FULL COPY OF THE SERIAL
ory referenced by sequential instructions struct very different rules. The processes tends to be repeatedly accessed. Because execute concurrently, although asynchro- locality of reference in the context of knowl- nously, depending on the processors' avail- edge discovery also exists, we attempt to exploit it to increase the efficiency of rule We have implemented two methods for mining. According to a precept of knowl- Example data set exhibiting locality. We handling the rules each worker generates.
edge discovery, data in a knowledge base now show an example data set that encom- In the first, as soon as a process converges system are nonrandom and tend to cluster passes the notions of temporal and functional (finds a valid set of rules), it broadcasts a somewhat predictably. This tendency mim- locality of reference. The data pertains to a message to announce the procedure's end.
ics locality of reference. Three types of supermarket enterprise and maintains infor- When other processes receive the message, locality of reference can coexist in a knowl- mation about various items sold in a sequen- they terminate. So, the processor that fin- edge base system: spatial, temporal, and tial, date-ordered manner. Each data set seg- ishes first procures the learned rule. The ment represents a small snapshot of the data other method compares each worker's rules.
In spatial locality of reference, certain data for a particular time period.
Different processes can generate different items appear together physically in a data- In Table 1, the data is from a store that rules owing to the use of different ranking base. For example, a supermarket is divided had a coupon for milk during the indicated algorithms. the master node then automati- into several departments, each maintaining a time period, forming a temporal locality cally verifies each rule by testing the cov- section in the database. Clearly, the database among many of the purchased items. In par- erage of a separate set of E+ and E–. Each groups information about the sale of chil- ticular, 30% of all items purchased were rule is assigned a verification ratio that con- dren's toys in one section and stores the sale milk, and 75% of all transactions contained veys the accuracy of the learned rule on information about different vegetables in a a milk purchase. We also demonstrated these new (verification) examples. Syntac- different section. To exploit this locality, we functional locality. We see here that when tically, the verification examples are iden- can mine different rules on different sections milk is on sale, customers tend to also pur- tical to the examples used to learn the rule.
of data independently and simultaneously chase cereal. In the data set, we see that However, we reserve the verification exam- using concurrency.
50% of the purchases are either milk or ples for the verification process rather than In temporal locality of reference, the data use them for the learning process. Imple- items that have been used in the recent past Partitioning algorithm. To retain all menting both methods has let us accelerate tend to appear in the near future. For exam- global dependencies among the predicates the mining process as well as achieve better ple, if a supermarket has a sale on a partic- in the current state, all Beowulf nodes and richer solutions.
number of nodes increased. Input: Number of processors p in Beowulf
The problem domain with which we are Serial extensional database (EDB) experimenting relates to the diagnosis of dia- Positive example set E+ betes. The accuracy rules discovered by the Negative example set E– cluster has varied. The rule serial INDED Output: p individual worker node EDBs
BEGIN ALGORITHM 3.1 For each example e ∈ E+  E– Do For each constant c ∈ e Do We attribute the variance of rule accuracy create an initially empty set Sc of facts by the clusters to our partitioning algorithm.
Create one (initially empty) set Snone for facts that have We anticipate extensive refinement of this no constants in any example e ∈ E+ E– algorithm as we continue this work.
For each fact f ∈ EDB Do The speculative parallel implementa- For each constant c′ ∈ f Do tions, produced a dramatic speedup when Sc′ = Sc′  f all ranking algorithms are tested for each If no set exists for c then execution. For this implementation, we Snone = Snone  f have implemented an automatic rule veri- Distribute the contents of Snone among all constant sets fier running on each worker. This verifier Determine load balance by summing all set cardinalities numerically quantifies the accuracy of dis- to reflect total parallel EDB entries K covered rules and, therefore, enables the Define min_local_load = [K/p] master to use a quantitative method for rule Distribute all sets Sci where 1 ≤ i ≤ m, (m num constants in EDB) evenly among the processors so that each processor has an EDB ofroughly equal cardinality such that each node has an EDB ofcardinality ≥ min_local_load as defined above.
END ALGORITHM 3.1 Figure 11. Algorithm 3.1 (EDB partitioning algorithm). This algorithm is O(n), where n is the number of facts in theextensional database.
WE LOOK FORWARD TO EXTEN-
sive experimentation with different parti-tioning algorithms of the EDB as well as with the background knowledge in the data-par-allel parallelization scheme. Particularly, we intend to refine the partitioning algorithm toconsider well-connected components formedby constant appearances in chains of rule antecedents and consequents. We anticipatea better decomposition of the EDB using well-connected components of a represent-ing constant graph. Additionally, we intend tocontinue experimentation with the specula- tive-parallelization scheme, and are enhanc- ing and devising new predicate-ranking algo- Number of processors rithms used by the induction engine. Twoalgorithms of particular interest use set the- Figure 12. Performance of rule mining on a cluster.
oretic operations for ranking predicates.
Additionally, we have found that one of Current status and results
system. The naive decomposition reduced the most interesting problems of parallel rule execution time by 50%. The data-parallel discovery is effective data partitioning, and We have implemented the four paral- implementations also experienced greatly we have presented a data-partitioning algo- lelization schemes for INDED, a large reduced execution time. Figure 12 illus- rithm suited for parallelizing ILP discovery knowledge-based learning and reasoning trates the consistent reduction of time as the systems. We have also experimented in the IEEE INTELLIGENT SYSTEMS
domain of diabetes diagnosis using a Beo- Rules," Seventh IEEE Symp. Frontiers of wulf cluster and have found that computa- Massively Parallel Computation, IEEE Com- tion time decreased dramatically due to the 1. "Knowledge Discovery in Databases: An puter Soc. Press, Los Alamitos, Calif., 1999, Overview," Knowledge Discovery in Data- pp. 234–241.
substantially smaller internal hypergraphs bases, G. Piatetsky-Shapiro and W.J. Fraw- generated in each worker node. ley, eds., AAAI Press/The MIT Press, Cam- 7. N. Lavrac and S. Dzeroski, Inductive Logic bridge, Mass., 1991, pp. 1–30.
Programming, Ellis Horwood, Chichester,UK, 1994. 2. M.S. Chen, J. Han, and P.S. Yu, "Data Min- ing: An Overview from a Database Perspec- 8. S. Muggleton, ed., Inductive Logic Program- tive," IEEE Trans. Knowledge and Data Eng., ming, Academic Press, San Diego, Calif., Vol. 8, No. 6, Dec. 1996, pp. 866–883. 3. J.R. Quindlan, "Induction of Decision Trees," 9. A. VanGelder, K. Ross, and J. Schlipf, "The Machine Learning, Vol. 1, 1986, pp. 81–106. Well-Founded Semantics for General LogicPrograms," J. ACM, Vol. 38, No. 3, July 1991, We thank the students of the Learning with Rea- 4. J. Seitzer, "INDED: A Symbiotic System of pp. 620–650.
son research group for their help. In particular, we Induction and Deduction," Proc. 10th Mid- extend our appreciation to Lee Adams, Timothy west Artificial Intelligence and Cognitive Sci- 10. M. Gelfond and V. Lifschitz, "The Stable Denehy, Kevin Livingston, and Madhavi Yele- ence Conf. (MAICS-99), AAAI Press, Menlo Model Semantics for Logic Programming," swarapu. Grants from the following partially sup- Park, Calif., 1999, pp. 93–99.
Proc. Fifth Logic Programming Symp., 1990, ported this work: the National Science Foundation pp. 1070–1080. (Grants CCR-9211621, OSR-9350540, CCR- 5. M.J. Zaki, S. Parthasarathy, and M. Ogi- 9503882, and EIA-9806184); the Air Force Office haram, "Parallel Algorithms for Discovery of 11. J. Doyle, "A Truth Maintenance System," of Scientific Research (Grant F49620-93-C-0063); Association Rules," Data Mining and Knowl- Artificial Intelligence, Vol. 12, 1979, pp.
the Air Force Avionics Laboratory, Wright Labora- edge Discovery, Vol. 1, 1997, pp. 5–35.
tory (Grant F33615-C-2218); the Ohio Board ofRegents Investment Fund Competition; and the 6. L. Shen, H. Shen, and L. Chen, "New Algo- 12. L. Sterling and E. Shapiro, The Art of Prolog, Ohio Board of Regents Research Challenge.
rithms for Efficient Mining of Association MIT Press, Cambridge, Mass., 1994.
M A R C H / A P R I L 1 9 9 9
Robots & Education
Guest Editor: Robin Murphy, University of South Florida
How will we educate the next generation of engineers and scientistsabout and with robots? This issue will report educational experiences with robots and provide recommendations for further efforts.
Room service, AI-style
Gleaning the Web
Online maps: help or hindrance?
DARPA's High-Performance Knowledge Base program
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13. T. Denehy and J. Seitzer, "A Hypergraph Rep- both intelligent systems and computer network- in computer science. He is a member of the High- resentation for Deductive Reasoning Sys- ing. Her projects involve work in distributed Performance Intelligent Knowledge-Based Sys- tems." 11th Midwest Artificial Intelligence knowledge-based systems, meta-pattern discov- tems research group at UD. Contact him at the and Cognitive Science Conf., AAAI Press, ery with an emphasis on cycle mining, and col- Dept. of Computer Science, Univ. of Dayton, 300 Menlo Park, Calif., 2000, pp.74–77. laborative computing. She received her BS in com- College Park, Dayton, OH 45469-2160; buckley@ puter science from Arizona State University and 14. J. Seitzer, A Study of the Well-Founded and her MS and PhD (for her work in theoretical arti- Stable Logic Programming Semantics, doc- ficial intelligence) in computer science from the toral dissertation, Dept. of Electrical and University of Cincinnati. She is a member of the Yi Pan is an associate professor in the Department
computer Engineering and Computer Sci- ACM and AAAI. Contact her at the Dept. of Com- of Computer Science at Georgia State University.
ence, Univ. of Cincinnati, Cincinnati, Ohio, puter Science, Univ. of Dayton, 300 College Park, He was a faculty member in the Department of Dayton, OH 45469-2160; seitzer@ cps.udayton.
Computer Science at the University of Dayton. He 15. R. Buyya, High Performance Cluster Com- is an area editor in chief of the Journal of Infor- puting Programming and Applications, Pren- mation, an editor of the Journal of Parallel and Dis- tice-Hall, Upper Saddle River, N.J., 1999. tributed Computing Practices, an associate editorof the International Journal of Parallel and Dis- 16. W. Gropp, E. Lusk, and A. Skjellum, Using tributed Systems and Networks, and on the edito- MPI; Portable Parallel Programming with rial board of The Journal of Supercomputing. He Message Passing Interface, MIT Press, Cam- James P. Buckley is an assistant professor at the
received his BEng in computer engineering from bridge, Mass., 1999.
University of Dayton. His interests include the Tsinghua University, China, and his PhD in com- general area of intelligent database systems, with puter science from the University of Pittsburgh.
a specific focus on data mining, uncertainty man- He is an IEEE Computer Society Distinguished agement, and active databases. He has published Visitor, a senior member of the IEEE, and a mem- numerous papers in the areas of fuzzy logic and ber of the IEEE Computer Society. Contact him at Jennifer Seitzer is an assistant professor in the
databases, formal database semantics, data min- the Dept. of Computer Science, Georgia State Department of Computer Science at the Univer- ing, and parallel knowledge-based systems. He Univ., Atlanta, GA 30303; [email protected]; www.
sity of Dayton. Her research and study involves received his ME and PhD from Tulane University cs.gsu.edu/ cscyip.
Areas of expertise include
■ Signal Processing
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