Using Answer Set Programming for Representating and Reasoning about Preferences and Uncertainty in Dynamic Domains Ravi Palla 1 , 2 , Dan Tecuci 1 , Vinay Shet 1 , Mathaeus Dejori 1 1 Siemens Corporate Research, Princeton, NJ, USA 2 School of Computing, Informatics and Decision Systems Engineering, Arizona State University Deep KR Challenge Workshop, 2011 1
Answer Set Programming (ASP) A form of declarative programming suitable for representing combinatorial search problems and knowledge-intensive applications. Syntax: rules: L ← L 1 , . . . , L m , not L m +1 , . . . , not L n L and L i are literals and not is default negation. Based on the stable model semantics [Gelfond and Lifschitz, 1988]. Models are called stable models or answer sets. Several efficient implementations (answer set solvers): smodels, DLV, cmodels, clasp(D), clingo, ASSAT, etc. 2
ASP Example: Computing Cliques Finding cliques of size ≥ n { in ( x ) } ← vertex ( x ) ← in ( x ) , in ( y ) , x � = y , not edge ( x , y ) ← { in ( x ) } n − 1 Every answer set corresponds to a clique of size ≥ n . 3
Why Do We Need to Consider Dynamic Domains in Healthcare? Physicians often need to consider cause-effect relationships for diagnosis and treatment. Systems that are required to help the physicians in these tasks need to be able to represent and reason with such knowledge. 4
Why Do We Need to Consider Dynamic Domains in Healthcare? Physicians often need to consider cause-effect relationships for diagnosis and treatment. Systems that are required to help the physicians in these tasks need to be able to represent and reason with such knowledge. Example : Knowledge: Gastritis causes gastrointestinal bleeding. Other Domain Knowledge: Aspirin may treat pain and should not be given to patients with gastrointestinal bleeding. Specific case: A patient with gastritis reports abdominal pain. Question: Is aspirin recommended in order to relieve his/her pain? 5
Why Do We Need to Consider Dynamic Domains in Healthcare? Physicians often need to consider cause-effect relationships for diagnosis and treatment. Systems that are required to help the physicians in these tasks need to be able to represent and reason with such knowledge. Example : Knowledge: Gastritis causes gastrointestinal bleeding. Other Domain Knowledge: Aspirin may treat pain and should not be given to patients with gastrointestinal bleeding. Specific case: A patient with gastritis reports abdominal pain. Question: Is aspirin recommended in order to relieve his/her pain? Answer: No, since gastrointestinal bleeding is a contraindication. 6
Representing the Example in ASP Gastritis causes gastrointestinal bleeding: h ( gastrointestinal bleeding , T + 1) ← h ( gastritis , T ) 7
Representing the Example in ASP Gastritis causes gastrointestinal bleeding: h ( gastrointestinal bleeding , T + 1) ← h ( gastritis , T ) Domain rules for drug recommendation: canAdminister ( X , Y , T ) ← may treat ( X , Y ) , not contraind ( X , T ) , h ( Y , T ) contraind ( X , T ) ← ci with ( X , Z ) , h ( Z , T ) 8
Representing the Example in ASP Gastritis causes gastrointestinal bleeding: h ( gastrointestinal bleeding , T + 1) ← h ( gastritis , T ) Domain rules for drug recommendation: canAdminister ( X , Y , T ) ← may treat ( X , Y ) , not contraind ( X , T ) , h ( Y , T ) contraind ( X , T ) ← ci with ( X , Z ) , h ( Z , T ) Other Domain knowledge: may treat ( aspirin , pain ) and ci with ( aspirin , gastrointestinal bleeding ). 9
Representing the Example in ASP Gastritis causes gastrointestinal bleeding: h ( gastrointestinal bleeding , T + 1) ← h ( gastritis , T ) Domain rules for drug recommendation: canAdminister ( X , Y , T ) ← may treat ( X , Y ) , not contraind ( X , T ) , h ( Y , T ) contraind ( X , T ) ← ci with ( X , Z ) , h ( Z , T ) Other Domain knowledge: may treat ( aspirin , pain ) and ci with ( aspirin , gastrointestinal bleeding ). Inertial axioms: h ( F , T + 1) ← h ( F , T ) , not ¬ h ( F , T + 1) ¬ h ( F , T + 1) ← ¬ h ( F , T ) , not h ( F , T + 1) 10
Representing the Example in ASP Gastritis causes gastrointestinal bleeding: h ( gastrointestinal bleeding , T + 1) ← h ( gastritis , T ) Domain rules for drug recommendation: canAdminister ( X , Y , T ) ← may treat ( X , Y ) , not contraind ( X , T ) , h ( Y , T ) contraind ( X , T ) ← ci with ( X , Z ) , h ( Z , T ) Other Domain knowledge: may treat ( aspirin , pain ) and ci with ( aspirin , gastrointestinal bleeding ). Inertial axioms: h ( F , T + 1) ← h ( F , T ) , not ¬ h ( F , T + 1) ¬ h ( F , T + 1) ← ¬ h ( F , T ) , not h ( F , T + 1) Patient-specific information: h ( gastritis , 0) and h ( pain , 0). 11
Representing the Example in ASP Gastritis causes gastrointestinal bleeding: h ( gastrointestinal bleeding , T + 1) ← h ( gastritis , T ) Domain rules for drug recommendation: canAdminister ( X , Y , T ) ← may treat ( X , Y ) , not contraind ( X , T ) , h ( Y , T ) contraind ( X , T ) ← ci with ( X , Z ) , h ( Z , T ) Other Domain knowledge: may treat ( aspirin , pain ) and ci with ( aspirin , gastrointestinal bleeding ). Inertial axioms: h ( F , T + 1) ← h ( F , T ) , not ¬ h ( F , T + 1) ¬ h ( F , T + 1) ← ¬ h ( F , T ) , not h ( F , T + 1) Patient-specific information: h ( gastritis , 0) and h ( pain , 0). 12
Representing the Example in ASP Gastritis causes gastrointestinal bleeding: h ( gastrointestinal bleeding , T + 1) ← h ( gastritis , T ) Domain rules for drug recommendation: canAdminister ( X , Y , T ) ← may treat ( X , Y ) , not contraind ( X , T ) , h ( Y , T ) contraind ( X , T ) ← ci with ( X , Z ) , h ( Z , T ) Other Domain knowledge: may treat ( acetaminophen , pain ). Inertial axioms: h ( F , T + 1) ← h ( F , T ) , not ¬ h ( F , T + 1) ¬ h ( F , T + 1) ← ¬ h ( F , T ) , not h ( F , T + 1) Patient-specific information: h ( gastritis , 0) and h ( pain , 0). 13
Preferential Diagnosis in the Healthcare Domain Consider the following knowledge from the guidelines for treatment of STEMI (ST Elevation Myocardial Infarction): Most cases of STEMI are caused by an occlusion of a major coronary artery. Coronary occlusion and reduction in coronary blood flow are usually due to physical disruption of an atherosclerotic plaque with subsequent formation of an occluding thrombus. Less commonly, a thrombus may form from a superficial erosion of the endothelial surface. Consider the following questions: A patient is diagnosed with STEMI. What is the likely cause? A patient is diagnosed with STEMI but no disruption of plaque was found. What is the likely cause? 14
Preferential Diagnosis in the Healthcare Domain Consider the following knowledge from the guidelines for treatment of STEMI (ST Elevation Myocardial Infarction): Most cases of STEMI are caused by an occlusion of a major coronary artery. Coronary occlusion and reduction in coronary blood flow are usually due to physical disruption of an atherosclerotic plaque with subsequent formation of an occluding thrombus. Less commonly, a thrombus may form from a superficial erosion of the endothelial surface. Consider the following questions: A patient is diagnosed with STEMI. What is the likely cause? Answer: disruption of plaque followed by formation of an occluding thrombus followed by coronary occlusion. 15
Preferential Diagnosis in the Healthcare Domain Consider the following knowledge from the guidelines for treatment of STEMI (ST Elevation Myocardial Infarction): Most cases of STEMI are caused by an occlusion of a major coronary artery. Coronary occlusion and reduction in coronary blood flow are usually due to physical disruption of an atherosclerotic plaque with subsequent formation of an occluding thrombus. Less commonly, a thrombus may form from a superficial erosion of the endothelial surface. Consider the following questions: A patient is diagnosed with STEMI but no disruption of plaque was found. What is the likely cause? Answer: erosion of the endothelial surface followed by formation of an occluding thrombus followed by coronary occlusion. 16
Representation Language L - C program: [ label :] L ← L 1 , . . . , L n , . . . , not L n +1 , . . . , not L m and [ label :] { L } ← L 1 , . . . , L n , . . . , not L n +1 , . . . , not L m [ label :]: optional label. { L } : used to represent uncertainty. prefer ( l 1 , l 2 ): prefer rule l 1 over l 2 . 17
Representation of the Example Sentences Most cases of STEMI are caused by an occlusion of a major coronary artery: r stemi ( T ) : { h ( stemi , T + 1) } ← h ( coronary occlusion , T ) 18
Representation of the Example Sentences Most cases of STEMI are caused by an occlusion of a major coronary artery: r stemi ( T ) : { h ( stemi , T + 1) } ← h ( coronary occlusion , T ) Coronary occlusion and reduction in coronary blood flow are usually due to physical disruption of an atherosclerotic plaque with subsequent formation of an occluding thrombus: r cor occ ( T ) : { h ( coronary occlusion , T + 1) } ← h ( occluding thrombus , T ) r cor bldflw red ( T ) : { h ( coronary bloodflow reduction , T + 1) } ← h ( occluding thrombus , T ) 19
Representation of the Example Sentences: Contd. Coronary occlusion and reduction in coronary blood flow are usually due to physical disruption of an atherosclerotic plaque with subsequent formation of an occluding thrombus: r thrombus 1( T ) : h ( occluding thrombus , T + 1) ← h ( disruption plaque , T ) 20
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