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Describe Got Speakers/Discussion here. variant formulation in bold face

Use Case Title: Got Speakers?




January 22, 2008

Original author

Mitch Kokar, Peter Vojtas and Kathy Laskey

Current lead

Mitch Kokar

Last Modified By

Mitch Kokar

Primary Actors

Customer, Store

Secondary Actors

Application domain


Triggering event

Buyer queries the Web for availability and features of speakers in local stores

Purpose/Goals (Summarize what the use case is to accomplish, e.g. story, real world example)

My main idea in reformulating some parts of this use case is following: this use case show how uncertainty annotation can help process information. Namely this use case can be a two step proces, in the first step some agent A1 has already annotated (UncertaintyModel: Fuzzy) Speakers according to preferences (see e.g. top-k use case), another agent A2 has annotated shops availability (UncertaintyModel: Probability) and now a new agent A3 has to annotate the sentence (conjunction (1)and(2) below) combine this annotation to a new one with some new combination model (UncertaintyModel: CombinationOfSeveralModels)

Customer needs to make a decision on (1) whether to go to a store today or wait until tomorrow to buy speakers, (2) which speakers to buy and (3) at which store. Customer is interested in two speaker features: wattage and price. Customer has not necessary "customer has" possibly a web service offers this (learned from user) a valuation formula that combines the likelihood of availability of speakers on a particular day in a particular store, as well as the two features. The features of wattage and price are fuzzy dependent on user (one prefers higher wattage another medium, one user prefers cheap Speakers another medium price).

Issues and Relevance to Uncertainty (Summarize how relevant to uncertainty (vagueness, ambiguity, imprecision,…) reasoning)

1. There is known probability distribution on the availability of particular speaker type in particular stores on a particular day in the future. Say there are two stores (not too close to each other) and the probability that speakers of type X will be available in stores A and B tomorrow are Pr(X, A)=0.4 and Pr(X, B)=0.6. The probabilities for all types of speakers are represented in the same way.

2. The customer has we have to specify, what does it mean customer has, maybe it is a service, our main task is automating search and uncertainty annotation has to enable this definitions of features of wattage and price in terms of fuzzy membership functions. For wattage, Customer has three such functions: weak, medium and strong. These are of "trapezoid shaped" membership functions. Similarly, for price Customer has three such membership functions: cheap, reasonable and expensive.

3. The valuation has three possible outcomes, all are expressed as fuzzy membership functions: bad, fair, good and super.

4. Customer knows reconsider customer knows the probabilistic information, since the probabilities are published on the web sites for the stores. The stores use the Uncertainty Ontology for this purpose.

5. Customer has reconsider customer has his/her own fuzzy definitions of the features of wattage and price. Again, the six membership functions that define these features are annotated with the Uncertainty Ontology.

6. Customer has reconsider customer has, maybe it is recomendation system a combination function that computes the decision, d, based upon those types of input. This function is specific to each customer, however the stores need to give them the inputs - the probabilities and the (crisp) values of wattage and price. The features are fuzzified by the customer's client software. Customer uses the Uncertainty Ontology to annotate the fuzziness of particular preferences.

Assumptions/Preconditions (List the preconditions necessary for this use case to occur, including description of necessary context)

Required Resources (List the general resources, such as data sources, ontologies, needed for scenario)

  1. Data collected by the store on the availability of items, which in turn depends on restocking and rate of selling
  2. Ontology Model, method, tool of uncertainty that covers both probability and fuzziness

Successful End (Describe what happens if this use case is successful)

Customer gets necessary information about the availability and types of speakers from stores. This information is sufficient for customer to compute the required metric.

Failed End (Describe what happens if this use case fails)

Main Scenario (List the sequence of events for the basic course (numbered))

  1. Customer formulates query about availability of speakers in the stores within some radius.
  2. Customer sends the query to the stores.
  3. Stores reply with information about the availability of speakers. The stores cannot say for sure whether a given type of speaker will be available in store tomorrow or not. It all depends on delivery and rate of sell. Thus stores provide the customer only with probabilistic information.
  4. Since part of the query involves requests that cannot be answered in crisp terms (vagueness), stores annotate their replies with fuzzy numbers.
  5. Customer uses the uncertainty annotated information to compute a metric.
  6. Customer uses the resulting values of the metric for particular stores and for particular types of speaker to decide whether to buy speakers, what type and which store.

Existing software or other resources (Summarize/provide references to existing tools that may be relevant to the use case problem, including examples of Required Resources noted above)

Additional background information or references (Summarize/provide references to information to further describe or characterize use case)

Variations (List the alternatives that will not be further decomposed at this time)

Open Issues