Introducing Constrained Heuristic Search to the Soar Cognitive Architecture (demonstrating domain independent learning in Soar) The Second Conference on Artificial General Intelligence, AGI-09 Sean A. Bittle Mark S. Fox March 7th, /11

General problem solving and learning are central goals of AI research on cognitive architectures However, there are few examples of domain independent learning in cognitive architectures 2 /11 The Problem The Goal Demonstrate Soar can learn and apply domain independent knowledge But to achieve this goal we need to augment the Soar’s problem-solving paradigm (CHS-Soar)

Developed by Newell, Laird and Rosenbloom at CMU, 1983 Symbolic Cognitive Architecture where all long term knowledge is encoded as productions rules. Based on the hypothesis that all goal-oriented behavior can be cast as the selection and application of operators to a state in a problem space 3 /11 Soar Cognitive Architecture

Developed by Fox, Sadeh and Bayken, 1989 CHS is a problem solving approach that combination of constraint satisfaction and heuristic search where the definition of the problem space is refined to include: Problem Topology Problem Textures Problem Objective 4 /11 Constraint Graph VaVa VcVc CiCi C ii VbVb Constrained Heuristic Search (CHS) CP/CHS allows us to employ a generalized problem representation (CSP) and utilize generic, yet powerful problem solving techniques

“ What are we trying to learn?” 5 /11 CHS-Soar

What are “Texture Measures?” 6 /11 Constraint GraphExternal AgentSoar Agent Problem Data ActualNormalizedPruned VarDomMRVDEGMRVDEGNumMRVDEG WAR,G,B NTR,G,B SAR,G,B QR,G,B NSWR,G,B VR,G,B TR,G,B CHS-Soar Minimum Remaining Values (MRV) – variable selection Degree (DEG) – variable selection Least Constraining Value (LCV) – value selection

“How Do We Select a “Good” Texture Measure?” 7 /11 CHS-Soar MRV0.50 DEG0.00 DEG0.40 DEG0.60 DEG1.00

CHS-Soar “What Do We Learn...Again?” Standard Soar Chunk (Water Jugs) sp {chunk-54*d150*tie*2 :chunk (state ^name water-jug ^operator + ^problem-space ^desired ^jug ^jug ) ( ^name fill ^jug ) ( ^name water-jug) ( ^contents 0 ^volume 3) ( ^contents 0 ^volume 5) ( ^jug ) ( ^contents 1 ^volume 3) --> ( ^operator >) } CHS-Soar Chunk sp {chunk-128*d351*tie*2 :chunk (state ^phase |SelectVariableTexture| ^top-state ^name |CHS-Soar| ^operator + ^operator { <> } + ^problem-space ^desired ) ( ^texture |DEG| ^value 0.66 ^name |VariableTexture|) ( ^texture |MRV| ^value 1. ^name |VariableTexture|) ( ^name |CHS-Soar|) --> ( ^operator > ) } Traditional Soar Agent Chunks tend to include domain specific knowledge Hyper-heuristics: heuristics to choose heuristic measures 8 /11

Three experiments conducted to investigate: 1. Integration of rule and constraint based reasoning 2. Domain Independent Learning 3. Scalability of externally learned chunks Problem types being considered: Job Shop Scheduling (JSS) Map Colouring Radio Frequency Assignment Problem (RFAP) N-Queens, Sudoku, Latin Square Towers of Hanoi, Water Jugs Configuration Problems Random CSPs Experiments 9 /11

Results: Domain Independent Learning Map Colouring (n = 11) Job Shop Scheduling (n = 15) RFAP (n = 200) 10 /11

11 /11 Demonstrated integration of rule and constraint based reasoning Demonstrated the ability to learn rules while solving one problem type that can be successfully applied in solving another problem type Demonstrated ability to discover, learn and use multi-textured “hyper-heuristics” leading to improved solutions over traditional unary heuristics Conclusions