Download Logic Programming and Intelligent Systems (LPIS) Group

Logic Programming and Intelligent Systems (LPIS) Group
Description of Research Areas
Logic Programming Systems
Parallel Logic Programming Systems
A number of Abstract Machines and Architectures has been designed to implement several
resolution algorithms in order to exploit both OR and AND parallelism of logic programs. Our
current research is concentrated towards a distributed memory architecture, named OASys, in
which the processing elements performing the OR-parallel computation, posess their own
address space while other simple processing units are assigned with AND-parallel computation
and share the same address space. OASys execution is based on distributed scheduling which
allows either environment copying or recomputation of paths depending on an estimated cost
before the distribution of work. Currently a prototype implementation is in progress, based on a
network of workstations.
AND/OR Parallelism
Our main objective is to extend the basic Resolution Principle of logic programs in order to
exploit both OR and AND parallelism. We have been investigating a number of resolution
algorithms for logic programs which would facilitate the use of distributed computer
architectures. In order to combine these two types of parallelism, we consider each alternative
path of the resolution tree as a totally independent computation. This gives rise to OR-parallel
execution. In addition, the conjunction of subgoals of each deterministic path can be resolved in
parallel, giving in effect AND-parallelism.
Meta-control
Recent work also describes the separation of logic programs from the control over them. We
have been testing a number of search algorithms used in Artificial Intelligence in order to
provide a meta-language in which someone could express guidance of search as a separate
programming activity. We adopt a notation in form of meta-directives which would give the user
all the necessary features to express search by only changing the meta-program while keeping
the same underlying logic program.
Constraint Logic Programming
The goal of the research in constraint logic programming is to improve the efficiency of logic
programming systems at combinatorial problems, by combining parallel execution of logic
programs and supporting constraints in logic. The focus of the research is on extending logic
programming to support constraints and parallelism at the same time. Various applications are
also developed to study specific requirements from the CLP system for various different
problems, such as:
 timetabling
 workforce scheduling
 production line optimization
 traffic lights control
Knowledge Base Systems
Rule Integration in Databases
The research resulted in an active object-oriented knowledge base system, called DEVICE, that
efficiently integrates active or event-driven (ECA), production and deductive rules into a single
Object-Oriented Database system.
More specifically, the active OODB EXACT has been extended with production and deductive
rules using the DEVICE compilation method which compiles the declarative condition of a highlevel rule into a network of complex events that incrementally matches the condition at run-time.
The network is similar to the RETE discrimination network, appart from two optimizations for
saving space: the event object re-usability, when multiple rules have common conditions, and the
virtual alpha node optimization, which does not store tokens in the network when they originate
from a non-selective condition.
The last event in the event network acts as a trigger of an ECA rule that hosts the original highlevel rule. The action of the ECA rule depends on the type of the high-level rule. Production
rules' actions are procedural specifications of the host database language, while deductive rules'
conclusions are more cumbersome. When the condition of a deductive rule is confirmed the
action is to insert in the database the object described in the conclusion (derived data). On the
other hand, the falsification of a previously true condition denotes that the derived object should
not be in the database, therefore an anti-action deletes the object.
The DEVICE system has been implemented on the EXACT system, which is an extensible
active OODB, that is itself an extension of ADAM OODB, which is built in Prolog. Currently
DEVICE runs on ECLiPSe and SICStus Prolog. It is ported to a parallel/distributed version of
Prolog, called CS-Prolog, in order to be executed in parallel (see below). Furthermore, DEVICE
is being used as an expert system shell for developing a distributed expert system for managing a
national WAN (ExperNet).
Furthermore, we explore ways to enhance the performance of active OODB systems through
efficient complex event handling and/or use of parallelism in event detection and condition
evaluation. Consequently, deductive and production rules will also be executed efficiently on
top. All these facilities will be integrated in the PRACTIC parallel OODB system.
Parallel Knowledge Base Systems
Research is focused on the development of a parallel knowledge base system. So far, a parallel
active Object-Oriented Database (OODB) model, named PRACTIC, has been defined and its
abstract machine has been studied. PRACTIC is based on the inter-class and intra-class parallel
query processing of queries. A hierarchical multi-processor architecture and various data and
knowledge declustering schemes have been studied, in order to increase the speed-up and
scalability of OO query processing. A prototype has been implemented on a transputer network
and a Unix workstation using CS-Prolog. In the future, it will also be ported to a cluster of
workstations.
Furthermore, the algorithms for distributing the event and rule objects of the DEVICE system
onto the abstract PRACTIC machine have been developed, along with parallel rule matching and
asynchronous execution schemes, for all three rule types.
Intelligent Systems
Knowledge-based Systems
The goal is to develop intelligent applications for solving non-trivial problems using the
expertise knowledge acquired by human experts. The group has been involved in the
development of various expert systems, such as:
 Expert system for software evaluation (ESSE)
 Distributed expert system for managing a national network (ExperNet)
 Various intelligent systems for searching in a database using incomplete, insufficient,
incorrect information
 Expert system for land evaluation
 Expert system for detecting problems in a Novell LAN
 Expert system for drug consultation
Real-time Intelligent Systems
The goal of the research in intelligent real-time systems is to develop autonomous systems that
are able to work in complex environments, taking decisions and acting in limited time periods.
The following topics are studied:
 Intelligent real-time systems architecture
 Development of scheduling algorithms that support Artificial Intelligence techniques
 Representation and reasoning about time
Also an intelligent system for navigating a vehicle in a city street is developed as a testbed
platform against which the research ideas will be tested.
Knowledge Discovery in Databases
The goal of the research in data mining is to develop methods and tools for detecting patterns
and useful correlations in raw data (observation data, commercial transactions, etc.) using
machine learning techniques. The following topics are studied:
 Discovery of Association Rules
 Mining Sequential Patterns
 Knowledge discovery in non-structured data (free text, multimedia, etc)
 Distributed Data Mining
Department of Informatics Home Page
LPIS Group Home Page
For any comments regarding this page contact Nick Bassiliades.
http://www.csd.auth.gr/~plk/plk_research.html