https://kemlg.upc.edu Intelligent Decision Support Systems Intelligent Decision Support Systems (Part II - EVOLUTION OF DECISION SUPPORT SYSTEMS / INTELLIGENT DECISION SUPPORT SYSTEMS / AN EXAMPLE) Miquel Sànchez i Marrè, Karina Gibert [email protected], [email protected]http://kemlg.upc.edu/menu1/miquel-sanchez-i-marre http://kemlg.upc.edu/menu1/karina-gibert Course 2011/2012
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Intelligent Decision Support Systems
(Part II - EVOLUTION OF DECISION SUPPORT SYSTEMS / INTELLIGENT DECISION SUPPORT SYSTEMS / AN EXAMPLE)
A Decision Support System (DSS) is a system under the control of one or more decision makers that assists in the activity of decision making by providing an organised set of tools intended to impart structure to portions of the decision-making situation and to improve the ultimate effectiveness of the decision outcome [Marakas, 1999].
A Decision Support System (DSS) is a system which lets one or more people to make decision/s in a concrete domain, in order to manage it the best way, selecting at each time the best alternative among a set of alternatives, which generally are contradictory
DSS are a model-based set of procedures for processing data and judgments to assist a manager in his/her decision [Little, 1970]
DSS couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It’s a computer-based support for management decision makers who deal with semi-structured problems[Keen & Scott-Morton, 1978]
DSS is a system that is extendable, capable of supporting ad hoc analysis and decision modelling, oriented towards future planning, and of being used at irregular, unplanned intervals [Moore & Chang, 1980]
DSS enable managers to use data and models related to an entity (object) of interest to solve semi-structured and unstructured problems with which they are faced [Beulens & Van Nunen, 1988]
Main feature of DSS rely in the model component. Formal quantitative models such as statistical, simulation, logic and optimisation models are used to represent the decision model, and their solutions are alternative solutions [Emery, 1987; Bell, 1992]
DSS are systems for extracting, summarising and displaying data [McNurlin & Sprague, 1993]
Statistical /Numerical Models Linear Models, Logistic Regressions Clustering Data Analysis (PCA, DA, SCA, MCA...) Markovian Models
Simulation Models
Control Algorithms
Optimization Techniques Linear Programming Queue Models Inventory Models Transport Models Multiple Criteria Decision Making (MCDM)
Models
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Advanced Decision Support Systems (5)From observations to decisions [adapted from A.D. Witakker, 1993]
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Observations
Consequences
Recomendations
Predictions
KNOWLEDGE
Understanding
Data
Quantiy of Information
DECISION
INTERPRETATION
High Low
High
Low
Value andRelevance
toDecisions
Observations
Consequences
Recomendations
Predictions
KNOWLEDGE
Understanding
Data
Quantiy of Information
DECISION
INTERPRETATION
High Low
High
Low
Value andRelevance
toDecisions
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Advanced Decision Support Systems (6)Intelligence Density [V. Dhar & R. Stein, 1997]
Intelligence Density : the amount of useful decision support information that a decision maker gets from using the output from some analytic system (IDSS) for a certain amount of time
How much of the IDSS output do you have to examine before you can make a decision of a specified quality ?
How quickly can you get the essence (knowledge) of the underlying data from the IDSS output ?
Conceptually, Intelligence Density can be viewed as the ratio of the number of utiles (utility units) of decision-making power gleaned (quality) to the number of units of analytic time spent by the decision maker.
An IEDSS is an intelligent information system that reduces the time in which decisions are made in an environmental domain, and improves the consistency and quality of those decisions [Haagsma & Johanns, 1994]
A DSS is a computer system that assists decision makers in choosing between alternative beliefs or actions by applying knowledge about the decision domain to arrive at recommendations for the various options. It incorporates an explicit decision procedure based on a set of theoretical principles that justify the “rationality” of this procedure [Fox & Das, 2000]
The use of Artificial Intelligence tools and models provides direct access to expertise, and their flexibility makes them capable of supporting learning and decision making processes. There integration with numerical and/or statistical models in a single system provides higher accuracy, reliability and utility [Cortés et al., 2000]
DATA VALIDATION: noise, outliers and missing values
DATA INTEGRATION: homogeneous units and time scale
ON-LINE DATA: 210 digital & 20 analogical signalse.g. pH, flow rates (air, water and sludge), equipment status (valves,pumps, etc.), dissolved oxygen...
OFF-LINE DATA: 158 variablesQUANTITATIVE: e.g. COD, BOD, (V)SS, N, P, Temperature, metals,grease & oils, V30... at different sampling location
QUALITATIVE: e.g. floc characterisation, filamentous bacteria,protozoa and metazoa identification and presence, presence ofbubbles, settling observation...
E. Charniak & D. McDermott, 1985 Artificial Intelligence is the study of mental faculties through
the use of computational models.
E. Rich & K. Knight, 1991 Artificial Intelligence (AI) is the study of how to make
computers do things, which, at the moment, people do better
L. Steels, 1993 Artificial Intelligence is a scientific research field concerned
with intelligent behaviour.
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Artificial Intelligence (2) OMNI:
What is this the main goal of AI?
Herbert A. Simon (1994):AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, they you're really doing cognitive science; you're using AI to understand the human mind.
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Artificial Intelligence (3)
Artificial Intelligence is the study of the possible or existing mechanisms –in human or other beings–providing such behaviour in them that can be considered as intelligence, and the simulation of these mechanisms, named as cognitive tasks, in a computer through the computer's programming.
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Artificial Intelligence Areas by application field Puzzle Resolution
Automatic Theorem Proving/Logic Programming
Game Theory
Medical Diagnosis
Machine Translation
Symbolic Mathematics and Algebra
Robotics
Fault Diagnosis
Text Understanding and Generation
Monitoring and Control Systems
e-Commerce
Business Intelligence
Intelligent Decision Support Systems
Recommender Systems
Intelligent Web Services
Social Networks Analysis
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Artificial Intelligence Areas by Cognitive Tasks
Vision and Perception
Natural language understanding
Knowledge acquisition
Knowledge representation
Reasoning
Search
Planning
Explanation
Learning
Motion
Speech and Natural language generation
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AI Goals The construction of Intelligent Systems The construction of such intelligent systems or intelligent
agents means that the agents must show an intelligent behaviour like: Autonomy Learning skills Communication abilities Coordination abilities Collaboration abilities with other agents, either humans or
artificial ones.
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Human Beings or “Intelligent” Agents BehaviourEXTERNAL STIMULIVisionNatural Language Understanding
EXTERNAL REACTION ACTIONSMotionNatural Language Generation
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Intelligent Agents
An intelligent agent is a computer system that is capable of flexible autonomous action in order to meet its design objectives (Wooldridge and Jennings, 1995).
Agents must be: Responsive: agents should perceive their environment and
respond in a timely fashion to changes that occur in it, Proactive: agents should be able to exhibit opportunistic,
goal-directed behaviour and take the initiative where appropriate
Social: agents should be able to interact, when they deem appropriate, with other artificial agents and humans in order to complete their own problem solving capabilities and to help others with their activities.
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ExamplesAdapted from [Russell & Norvig 2000]
Agent Type Goals / Performance Measure Environment Sensors Actuators
Medical Diagnosis System Healthy patient, Reduced costs
Patient, hospital, staff Keyboard entry of symptoms, findings, patient’s answer
Display of questions, tests, diagnoses, treatments, referrals
Satellite image analysis system
Correct image categorization
Images from orbiting satellite
Colour pixel arrays Display of scene categorization
Internet Softbot Summarise relevant information
Users, the web Web pages, text, links Interesting topic detection in web pages, going to other web pages
Machine Translation system
Text translation from one language to another
Text, Linguistic knowledge Text Sentence translation, Semantic interpretation of paragraphs
MARS Pathfinder Maximum information gathering from Mars
Mars surface, Earth signals Telemetry, Mars environment images, Velocity and balance information
Forward, backward, accelerate, braking, turning, take one photo
Interactive English tutor Student’s score on test satisfactory
Set of students, testing agency
Keyboard entry Display of exercises, suggestions, corrections
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Kinds of Agents
Cognitive or deliberative agents: They are based in a deep knowledge representation. Rational actions are implemented through inference
processes by means of rule sets. For instance, a medical diagnosis system built up on a
deep medical knowledge, which provides a deliberative reasoning process.
Reactive or behavioural agents: They are a set of behaviours, which interact and evolve
to reach their goals. They are prepared to react to external stimuli. For instance, a mobile robot, which interacts with the
environment showing emergent behaviours.
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A Cognitive Agent: MYCIN
Medical Diagnosis Infeccious Breathing diseases Great Medical Knowledge Compilation process
Actuation Questions and answers with the doctor
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MYCIN: Example Dialogue (I)
>What is the patient’s name? John Smith >Age? He is 55 {Natural Langiage Interpretation}>Have you obtained positive cultures indicating general>type? {Evidence Caption}Yes>Let’s call the most recent culture CULTURE-1. Fromwhat site was CULTURE-1 taken? From the blood>When? June, 21, 2001>Let’s call the first significant organism from this blood>culture ORGANISM-1. Do you know the identity of>ORGANISM-1? No
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MYCIN: Example Dialogue (II)
>Is ORGANISM-1 a rod or a coccus or something else? Rod {Cause Discrimination}>What is the gramstain of ORGANISM-1? Gramnegative> Has John Smith a previous history of alcoholism?No> Is there evidence that the infection has hospitalary origin?Yes> My therapy recommendations will be based on the>following possible identities of the organism(s) that seem>to be significant:the identity of ORGANISM-1 may be
PSEUDOMONAS
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MYCIN: Example Dialogue (III)
>the identity of ORGANISM-2 may be KLEBSIELLA
the identity of ORGANISM-3 may be
ENTEROBACTER
>My preferred therapy recommendation is as follows:
>Give the following in combination:
>GENTAMYCIN
>Dose: 1.7 mg/kg Q8H - IV or IM
>Comments: Modify dose in renal >failure
>CARBENICILLIN {etc.}
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A Reactive Agent: Marvin
Data Monitoring Repeated Supervision of many parameters Alarm Categorization
Actuation / Reaction Data Anomalies Detection Sending Messages to Operators Alarm Detection
The deliberative/symbolic paradigms Concerned on the processing of symbols rather than
numerical values. Use a latent reasoning mechanism. Most of them are Cognitive-inspired approaches.
The reactive/subsymbolic paradigms Concerned about more numerical computations and providing
nice and intelligent optimizations schemes or function approximation schemes.
No evident reasoning mechanisms are used. Most of them are Bio-inspired approaches.
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Deliberative Approaches (1)
Logic paradigm: based on representing the knowledge about the problem and the domain theory through logical formulas. The main reasoning mechanism is the automatic theorem
proving using the automated resolution process set by Robinson. It is a very general mechanism.
Major techniques are based on Logic Programming.
Heuristic search and planning paradigm: it is based in searching within a space of possible states, starting from the initial state to a final state, where the problem has been solved. The state space is a graph structure to be intelligently explored. Most commonly used techniques are the A* algorithm and
Knowledge-Based paradigm: this kind of approach tries to get benefit from the particular knowledge of a concrete domain, which is normally used by experts when facing the problems to be solved. This knowledge is encoded in what has been named as a
Knowledge Base. Most common knowledge bases are implemented as inference rules (IF <conditions> THEN <actions>). The knowledge is explicitly represented by the inference rules.
Main examples of this paradigm are the Expert Systems and the Intelligent Tutoring Systems.
The reasoning mechanisms are the forward and backward reasoning engines.
The technique used here is commonly known as Rule-Based Reasoning (RBR), or sometimes also known as Knowledge-Based System (KBS).
Model-Based paradigm: this approach is very similar to the Knowledge-based, because the knowledge of a particular domain is used. The difference relies on the fact that the knowledge is implicitly encoded in some kind of model. Most common approaches are causal models reflecting the
causal relationships among several components of a system, or qualitative models reflecting the qualitative relationships among several attributes which are characterizing the domain.
The reasoning process is done through some kind of interpreter of the model and its component relationships.
Model-Based Reasoning (MBR) and Qualitative Reasoning are major techniques using this approach
Experience–Based paradigm: this approach tries to solve new problems in a domain using the solution given in the past to a similar problem in the same domain (analogical reasoning). Thus, the solved problems constitute the “knowledge” about the domain. As more experienced is the system better performance achieves,
because the experiences (cases or solved problems) are stored in the Case Base.
This way the system is continuously learning to solve new problems.
The technique used in this approach is known as Case-Based Reasoning (CBR) or Instance-Based Reasoning.
Connectionism paradigm: this approach is inspired by the biological neural networks which are in the brain of many living beings. The model of an Artificial Neural Network (ANN) mimics the
biological neural networks with the interconnection of artificial neurons.
The ANN will produce an output result, as an answer to several input information from the input layer neurons, emulating the neural networks of the brain which propagate signals among all the neurons interconnected.
ANNs are general approximation functions very useful in nonlinear conditions
Evolutionary Computation paradigm: Evolutionary computation is a bio-inspired approach mimicking selection natural process in biological populations. It uses iterative progress, such as growth or development in a
population. This population is then selected in a guided random search being able to use parallel processing to achieve the desired end.
Such processes are often inspired by biological mechanisms of evolution.
Evolutionary computation provides a biological combinatorial optimization approach.
Uncertainty reasoning paradigms: A Bayesian network, belief network or directed acyclic
graphical model Is a probabilistic graphical model that represents a set of
random variables and their conditional dependencies via a directed acyclic graph (DAG).
They provide an inference reasoning mechanism to obtain the new probability values of any variable within the network after some new evidences are known
Fuzzy logic systems Systems based on fuzzy logic and possibilistic theory to
model the vagueness and imprecision concepts. The mathematical possibilistic model assigns a possibility
value to each element which is evaluated regarding whether it belongs to a set. Values are evaluated in terms of logical variables that take on continuous values between 0 and 1.