CMPE260-Project 1 Solved

In this project, you will implement a matching application in Prolog.

2       Knowledge Base
You have 7 main files each containing a single type of relation. These files constitute your knowledge base.

•   glanian(GlanianName, GlanianGender, GlanianFeatures) Basic properties of a glanian.

–    GlanianName: name of the glanian.

–    GlanianGender: gender of the glanian.

–    GlanianFeatures: list of 10 main glanian features normalized in the range [0, 1]: Age, Salary, Intelligence, Romance, Creativity, Responsibility, Fun, Kindness, Honesty, Good Looking.

•   expects(GlanianName, ExpectedGenders, ExpectedFeatures) Expectations of a glanian from her partner.

–    GlanianName: name of the glanian.

–    ExpectedGenders: list of genders which this glanian expects.

–    ExpectedFeatures: list of features which this glanian expects.

•   weight(GlanianName, WeightList)

Relative importance of the each feature for this glanian.

–    GlanianName: name of the glanian.

–    WeightList: list of weights containing real numbers in the range [0, 1].

•   likes(GlanianName, LikedActivities, LikedCities) Activities and cities which this glanian likes.

–    GlanianName: name of the glanian.

–    LikedActivities: list of activities which this glanian likes.

–    LikedCities: list of cities which this glanian likes.

•   dislikes(GlanianName, DislikedActivities, DislikedCities, Limits) Activities and cities which this glanian dislikes and her tolerable feature limits.

–    GlanianName: name of the glanian.

–    DislikedActivities: list of activities which this glanian dislikes.

–    DislikedCities: list of cities which this glanian dislikes.

–    Limits: list of lists containing tolerable limits for each feature. Each list contains two values: minimum and maximum limit. If the glanian does not have a specific limit, then the list is empty.

•   city(CityName, HabitantList, ActivityList) Basic properties of a city.

–    CityName: name of the city.

–    HabitantList: list of habitants that live in this city.

–    ActivityList: list of activities that can be done in this city.

•   old relation([GlanianName1, GlanianName2]) Old relationships.

–    GlanianName1: name of the first glanian.

–    GlanianName2: name of the second glanian.

3      Predicates
In this section, we will go over the predicates that you are going to implement.

3.1 glanian distance(+Name1, +Name2, -Distance)
Given two glanians Name1 and Name2, this predicate will return the distance from Name1 to Name2.

This distance is the Euclidean distance between Name1’s ExpectedFeatures and Name2’s GlanianFeatures. It is a measure of how closer Name2 to Name1’s expectations. Notice that this predicate is not symmetric.

Let e = [e1,e2,...,e10] be the ExpectedFeatures of Name1 and f = [f1,f2,...,f10] be the GlanianFeatures of Name2. Then, the glanian distance is calculated as follows:

                                                                                                             (1)

 For example, if e = [−1,3,4], f = [2,7,8], then the glanian distance is p(3 − 7)2 + (4 − 8)2. We omit the first feature since e1 = −1 (i.e. Name1 does not care about the first feature).

Examples:

?- glanian distance(zhuirlu, josizar, D). D = 1.218001642035018.

?- glanian distance(josizar, zhuirlu, D).

D = 0.8932983824008639.

?- glanian distance(olisor, calemi, D). D = 1.0484364549175118.

?- glanian distance(calemi, olisor, D).

D = 1.2979672569059668.

3.2 weighted glanian  distance(+Name1, +Name2, -Distance)
Given two glanians Name1 and Name2, this predicate will return the weighted distance from Name1 to Name2. It is almost the same as the first except that Name1 puts weights to each features, showing her preferences.

Let e = [e1,e2,...,e10] be the ExpectedFeatures of Name1, w = [w1,w2,...,w10] be the WeightList, and f = [f1,f2,...,f10] be the GlanianFeatures of Name2. Then, the weighted glanian distance is calculated as follows:

                                                                                                         (2)

For example, if e = [−1,3,4], f = [2,7,8], w = [−1,0.5,1], then the weighted glanian distance is

 

p0.5(3 − 7)2 + 1(4 − 8)2.

Examples:

?- weightedglaniandistance(zhuirlu, josizar, D). D = 0.7717511418844807.

?- weightedglaniandistance(josizar, zhuirlu, D).

D = 0.4353217993622649.

  ?- weightedglaniandistance(olisor, calemi, D). D = 0.40758454337719924.

?- weightedglaniandistance(calemi, olisor, D).

D = 0.9851317196192598.

3.3 find  possible cities(+Name, -CityList)
This predicate will return a list of cities which contains:

•   The current city of Name.

•   Name’s LikedCities.

Examples:

 ?- find possible cities(zhuirlu, CityList). CityList = [venis, beyroot, istenbol]

?- find possible cities(josizar, CityList).

CityList = [corse town, seviliri, viyan]

3.4 merge possible cities(+Name1, +Name2, -CityList)
Given two glanians Name1 and Name2, this predicate will return the union of the two glanian’s possible cities.

Examples:

?- merge possible cities(zhuirlu, josizar, CityList).

CityList = [venis, beyroot, istenbol, corse town, seviliri, viyan] ?- merge possible cities(zhuirlu, zhuirlu, CityList).

CityList = [venis, beyroot, istenbol]

3.5 find  mutual activities(+Name1, +Name2, -ActivityList), 
Given two glanians Name1 and Name2, this predicate will return the list of mutual activities of two glanians. An activity is mutual if it is in both glanians’ LikedActivities.

Examples:

?- find mutual activities(zhuirlu, josizar, ActivityList).

ActivityList = []

?- find mutual activities(zhuirzaz, josizar, ActivityList).

ActivityList = [camping, swimming]

3.6 find  possible targets(+Name, -Distances, -TargetList), 
This predicate will return a list of possible glanians sorted by their distances as possible matching targets for Name. Distances should be a sorted list that contains glanian distance from Name to glanians in TargetList. The gender of each glanian in TargetList should be in ExpectedGenders of Name. For example, if a glanian has an empty list in her expects entry, then the results should be an empty list since she does not expect any gender.

Examples:

?- find possible  targets(zhuirlu, Distances, TargetList)

Distances = [0.3006409819036653, 0.3238039530333131, 0.328,

0.3493422390722312, 0.40817398251235953, 0.4136302696853797,

0.4190071598433611, 0.42604577218885764, 0.427071422598141|...]

TargetList = [golkolz, jai-blava, faeno, darcaluna, anfin, aidel, bloszen,] sheeanth, gallan|...]

?- find possible  targets(zhuirzaz, Distances, TargetList)

Distances = [0.3532860031192857, 0.4758739328855911, 0.5260465758846834,

0.5502290432174586, 0.5630337467683442, 0.5718933467002392,

0.6119852939409575, 0.6136024771788328, 0.6186784302042538|...] TargetList = [angwispm, engsangu, ranaqri, wistur, stermilky, faevine, jodturv, wilkster, faezab|...].

3.7 find  weighted targets(+Name, -Distances, -TargetList), 
This predicate will return a list of possible glanians sorted by their weighted distances as possible matching targets for Name. It is the same as the previous predicate except that the distances are calculated with weighted glanian distance(Name, Target, D).

Examples:

?- find weighted  targets(zhuirlu, Distances, TargetList)

Distances = [0.1385049818598595, 0.1692282511875603, 0.18459984019494705,

0.2198129454786501, 0.2261861556329211, 0.24256776991183307,

0.24317945842525432, 0.2508943801682293, 0.25718034333906625|...], TargetList = [jai-blava, golkolz, darcaluna, zazgo, brakea, sheeanth, lield, aidel, dignarv|...]

?- find weighted  targets(zhuirzaz, Distances, TargetList)

Distances = [0.26687880957468313, 0.30606038946586994, 0.3115456467357552,

0.3784484363820255, 0.3926651652489688, 0.40451883516098475,

0.40884393844106337, 0.4164695787209433, 0.41727860117671983|...], TargetList = [angwispm, engsangu, stermilky, nyax, wistur, ranaqri, thali, dorfae, faezab|...]

3.8 find  my best  target(+Name, -Distances, -ActivityList, -CityList,

-TargetList), 
This predicate will use all the other restrictions to find possible matching targets together with possible activities in possible cities. So in the end, a glanian will enter her name and will get a list of distances to her matching targets, and activities that can be done in possible cities. We can read each element in four of these lists as follows:

“Name and TargetList[i] can do ActivityList[i] in City[i]. This matching is close to the

Name’s preferences by Distances[i].”

The restrictions are as follows (index i can be read as for all elements in the list):

1.   Name and Target[i] should not have any previous oldrelation.

2.   Name should either like City[i] or be a habitant of City[i] (i.e. predicate 3.3), or there should be an Activity[i] in City[i] that is also in LikedActivities of Name.

3.   Activity[i] should not be in DislikedActivities of Name.

4.   City[i] should not be in DislikedCities of Name.

5.   City[i] should be in CityList where merge possible cities(Name, Target, CityList) is true (i.e. the city should be in the merged possible cities).

6.   Target[i]’s gender should be in the expected gender list of Name.

7.   Target[i]’s features should be in the tolerance limits (see dislikes) of Name.

8.   The intersection between Name’s DislikedActivities and Target[i]’s LikedActivities should not be more than two. In other words, there should not be three or more conflicting activities.

You should return all (Distance, Activity, City, Target) pairs that satisfy the above criterions in respective lists. Use weighted glanian distance(Name, Target, Distance) for the distance metric. Note that there might be more than one activity in a city for a found match.

Example:

?- find my  best target(josizar, Distances, ActivityList, CityList, TargetList).

Distances = [0.5972048350440575, 0.5972048350440575, 0.5972048350440575,

0.5972048350440575, 0.5972048350440575, 0.5972048350440575,

0.5972048350440575, 0.5972048350440575, 0.5972048350440575|...]

 ActivityList = [bird watching, bird watching, board gaming, boxing, camping, card game, circus, circus, collecting leaves|...]

CityList = [corse town, viyan, corse town, viyan, viyan, viyan, corse town, seviliri, seviliri|...]

TargetList = [tizstarb, tizstarb, tizstarb, tizstarb, tizstarb, tizstarb, tizstarb, tizstarb, tizstarb|...]

?- find my  best target(anthgall, Distances, ActivityList, CityList, TargetList).

Distances = [0.2726177800511184, 0.2726177800511184, 0.2726177800511184,

0.2726177800511184, 0.2726177800511184, 0.2726177800511184,

0.2726177800511184, 0.2726177800511184, 0.2726177800511184|...] ActivityList = [art gallery, basketball, basketball, bird  watching, board gaming, camping, card game, circus, circus|...]

CityList = [honk gonh, honk gonh, neu fork, neu fork, seaghoul, neu fork, neu fork, honk gonh, lonudonu|...],

TargetList = [gembzynth, gembzynth, gembzynth, gembzynth, gembzynth, gembzynth, gembzynth, gembzynth, gembzynth|...].

3.9 find  my best  match(+Name, -Distances, -ActivityList, -CityList,

-TargetList), 
This predicate is similar to the previous predicate with some additional constraints. In this predicate, we also take the matching target’s preferences into account. The restrictions are as follows (additional constraints are highlighted):

1.      Name and Target[i] should not have any previous oldrelation.

2.      Name should either like City[i] or be a habitant of City[i] (i.e. predicate 3.3), or there should be an Activity[i] in City[i] that is also in LikedActivities of Name.

3.      Target[i] should either like City[i] or be a habitant of City[i] (i.e. predicate 3.3), or there should be an Activity[i] in City[i] that is also in LikedActivities of Target[i].

4.      Activity[i] should not be in DislikedActivities of Name and Target[i].

5.      City[i] should not be in DislikedCities of Name and Target[i].

6.      City[i] should be in CityList where merge possible cities(Name, Target, CityList) is true (i.e. the city should be in the merged possible cities).

7.      Target[i]’s gender should be in the expected gender list of Name.

8.      Name’s gender should be in the expected gender list of Target[i].

9.      Target[i]’s features should be in the tolerance limits (see dislikes) of Name.

10.   Name’s features should be in the tolerance limits of Target[i].

11.   The intersection between Name’s DislikedActivities and Target[i]’s LikedActivities should not be more than two. In other words, there should not be three or more conflicting activities.

12.   The intersection between Name’s LikedActivities and Target[i]’s DislikedActivities should not be more than two.

You should return all (Distance, Activity, City, Target) pairs that satisfy the above criterions in respective lists. Use the following equation for the distance:

wgd(Name, Target, Distance) + wgd(Target, Name, Distance)

               (3) 2

where wgd is weighted glanian distance. Note that there might be more than one activity in a city for a found match.

Examples:

?- find my  best match(anthgall, Distances, ActivityList, CityList, TargetList).

Distances = [0.5363785971188019, 0.5363785971188019, 0.5363785971188019,

0.6186453156203476, 0.6186453156203476, 0.6186453156203476,

0.6186453156203476, 0.6186453156203476, 0.6186453156203476|...], ActivityList = [art gallery, jet skiing, jet  skiing, circus, crafting, frisbee, jet skiing, napping, paint|...],

CityList = [honk gonh, honk gonh, lonudonu, lonudonu, lonudonu, lonudonu, lonudonu, lonudonu, lonudonu|...],

TargetList = [kezdark , kezdark , kezdark  , azraur, azraur, azraur, azraur, azraur, azraur|...].

?- find my  best match(nysow, Distances, ActivityList, CityList, TargetList).

Distances = [0.657633337325202, 0.6699707062805402, 0.6699707062805402,

0.6699707062805402, 0.6699707062805402, 0.6699707062805402,

0.6699707062805402, 0.6704309489109601, 0.6704309489109601|...] ActivityList = [card  game, card game, crafting, drink, judo, park, photo, bird watching, camping|...]

CityList = [venis, venis, venis, seviliri, venis, ansterdum, venis, ansterdum, ansterdum|...]

TargetList = [amamort, narvvine, narvvine, narvvine, narvvine, narvvine, narvvine, shadvae, shadvae|...]

3.10                   Bonus
Implement a new predicate which you can decide its arguments that will find the 10 best matches in the whole database. List these 10 matches in a text file top10.txt as follows:

alper - cagla alper - mehmet ...

Permutations are not allowed in this list (i.e. there should not be an entry cagla - alper).