Cognitive architectures. Soar.Cognitive scienceUnified Theories of CognitionPsychological Validity as an Issue in Cognitive ArchitecturesCognitive architecturesExamples of cognitive architecturesSoarShort Long historyPhilosophy (in their own words)ComponentsProblem spacesLong term memory(Let us remember…) ProductionsWorking memoryDecision cycleImpassePerceptual-Motor SystemChunking-based LearningNew additions to SOARProgramming SOAREEL 5708Cognitive architectures. Soar.Lotzi BölöniEEL 5708Cognitive science•Cognitive science is usually defined as the scientific study either of mind.•Highly interdisciplinary:it is said to consist of, take part in, and collaborate with: –Psychology (especially cognitive psychology), –Linguistics, –Neuroscience, –Artificial intelligence (neural network research in particular), –PhilosophyEEL 5708Unified Theories of Cognition•Is a theory which attempts to unify all the theories of the mind in a single framework. •Allen Newell (1990) proposed that the current state-of-the-art in experimental psychology could now support such theories, based on years of accumulated results.•To assert a unified theory of cognition, one must propose mechanisms by which the results of these human cognitive experiments can be reproduced. The codification and simulation of these mechanisms is tantamount to designing an architecture for general intelligence.EEL 5708Psychological Validity as an Issue in Cognitive Architectures•Does the architecture make any attempt to model aspects of human behavior? •The answer is not always either easy or straightforward. •Some research in cognitive architectures is concerned with modeling the methods by which humans solve problems. •Another approach is to try to develop architectures which behave intelligently without regard to the psychological plausibility of the method by which the behavior is achieved. •Still yet another approach is to claim that intelligence can not be achieved without modeling the architecture of the brain first, and then determining the methods which will produce the desired behavior.•Power Law of Learning:–“the logarithm of the reaction time for a particular task decreases linearly with the logarithm of the number of practice trials taken.” –With more practice at a task, people seem to always be getting faster.–However, the rate of learning decreases the more practice one has.EEL 5708Cognitive architectures•“Architecture” – is a key word for this domain•In computer science, the architecture of a system is a fixed structure that provides a system which can be programmed.•In cognitive science, the term refers to the architecture of the mind: a fixed structure underlying the flexible domain of cognitive processing. –Cognitive architectures – for humans–Architectures for intelligent agents – for agents–The ambition of SOAR is to be both: a basis for both human and artificial cognition.EEL 5708Examples of cognitive architecturesSubsumption Architecture (Brooks)Heterogeneous Asynchronous Architecture (Gat)Plan-then-compile Architectures (Theo)Planning and Learning Architecture (PRODIGY) Modular-Integrated Architecture (ICARUS)Adaptive Intelligent Systems (AIS)A Meta-reasoning Architecture for 'X' (MAX)A Basic Integrated Agent (Homer) Problem-Space Architecture (Soar)Situated Action + Planned Action (Teton)Real-Time, Decision-Theoretic Architecture (RALPH-MEA)The Entropy Reduction Engine (ERE)EEL 5708SoarEEL 5708Short Long history•Started in 1983 a group led by Allen Newell•22 years of history.•8 versions.•Probably about 300 papers•It is still under active development. •Academic center: University of Michigan•Commercial arm: SoarTech Inc.EEL 5708Philosophy (in their own words)•The Soar project is an attempt to develop and apply a unified theory of human and artificial intelligence. •The core of the effort is the architecture – the fixed base of tightly coupled mechanisms – underlying intelligent behavior.•This architecture then forms the basis for wide-ranging investigations into basic intelligent capabilities – such as problem solving, planning learning, knowledge representation, natural language, perception and robotics. •This is a true cognitive-science enterprise, where human and artificial evidence and criteria are constantly intermingled in service of progress in both areas.EEL 5708Components1. Problem Spaces2. Long-Term Memory3. Attribute-Value Representation4. Preference Memory5. Decision Procedure6. Perceptual-Motor Subsystems7. Goal-Directed Behavior8. Chunking-Based LearningEEL 5708Problem spaces•Represents all tasks as collections of problem spaces•Problem space:–States + operators that manipulate statesEEL 5708Long term memory•Soar's long term memory is a production system based on Ops5. Productions:–have a set of conditions, which are patterns to be matched to working memory, –a set of actions to perform when the production fires. –Conditions can match to all the current goals, problem spaces, states and operators on the context stack in working memory.•Actions can only add elements to preference memory. These elements are attribute/value pairs for some object and a preference, which indicates a (lack of) desire to add this element to working memory. •Soar performs no conflict resolution between competing productions- all productions which match the current working memory fire.EEL 5708(Let us remember…) Productions(defrule mammal (animal ?name) (warm-blooded ?name) (not (lays-eggs ?name)) => (assert (mammal ?name)) (printout t ?name " is a mammal" crlf)) •The problem with a production system is the efficiency of matching a number of conditions against the knowledgebase. •The RETE algorithmEEL 5708Working memory•Soar's working memory consists of a set of (Object ^attribute value) elements. Value may be a symbolic constant, a number, a string, or an Object. The context stack consists of the context objects currently in working memory: all goals, problem spaces, states, and operators. All context elements are attached to a goal, and all goals except the top goal point to a supergoal, imposing a linear order on the context stack.•There must be some chain of elements from a context object to every element in working memory. If this chain is broken the element is removed from working memory.•The Decision Cycle can examine and modify the entire context stack. The