A Cognitive Substrate for Achieving Human-Level Intelligence Nicholas L. Cassimatis AI Magazine Vol. 27, No. 2 (200 6) Summarized by Eun Seok Lee BI. 2008. 04. 07.
Jan 17, 2018
A Cognitive Substrate for Achieving Human-Level
IntelligenceNicholas L. Cassimatis
AI Magazine Vol. 27, No. 2 (2006)
Summarized by Eun Seok LeeBI. 2008. 04. 07.
A Profusion Problem• “When is the first Pittsburgh Steelers game after (the World Series, T
hanksgiving, my daughter’s birthday, the next full moon)?”• Cf. Cyc (Lenat and Guha 1990) & ThoughtTreasure (Mueller 1998) –
not yet to provide a comprehensive store of all the knowledge for such queries.
Question
knowledge info
National holidays
User info Celestial movements
The Procedural Profusion Problem
A human utterance
hidden Markov models
Convert continuous acoustic signal into discrete representation of the phonemes, morphemes, and words
chart- or search-based algorithms
Identify the syntactic structure
logical, case-based, and probabilistic methods
Reasoning about the world and other people’s intention
The Procedural Profusion Problem
• Too many computational problems involved with difficult-to-integrate methods.
• Each aspects can best be dealt with different methods• Tanenhaus and Trueswell 1995 – Above is very difficult problem – on
ly multiply for systems that integrate more of human-level intelligence.
• Then what about enabling computers automatically storing and collecting knowledge and algorithms? – not sufficient; only learn to delegate among existing algorithms.
• And, control and data structures of these different classes of algorithms are very difficult to integrate
• Thus, the profusion problem is a genuinely difficult integration problem.
The Cognitive Substrate Hypothesis
• Human’s earlier cognitive mechanism with adapting mechanism is sufficient to achieve human-level intelligence in all domains.
• A relative small set of computational problems that once solved, (“cognitive substrate,”) can be adapted to solve other problems.
An Example of a ‘Cognitive Substrate’Basic social, physical reasoning problem
Reasoning
Temporal intervals
Causal relations
Desire
Objects
EventsBelief
Ontologies
“Cognitive substrate” – A set of computational mechanisms
i.e. “Once a set of ‘cognitive substrate’ is constructed, the rest will be relatively easy.”
The Benefits of the Cognitive Substrate Hypothesis
1. Smaller problem2. Quicker intelligent system development3. Easier integration across domains
• If cognitive substrate underlie cognition in most domains of human cognition, then much of the problem of human-level cognition becomes more tractable.
• AI, Cognitive psychology, linguistics, neuroscience support the cognitive substrate hypothesis.
Implicit Substrate Hypothesis in Much AI Research
• Representative research: – Search through a state space (Newell and
Simon 1972)– Updating probabilities in a Bayesian network
(Pearl 1988)– A modest set of primitives that can represent
much of the semantics of human language (Shank 1975) – not implemented because reasoning problems with these primitives are not yet achieved.
– Thus require benefits of each specific class of AI methods should be integrated into one system.
Linguistic Semantics• Jackendoff – structures used to represent the semantics
of a relatively small set of semantic fields can be used to represent the semantics of many other semantic fields.
• i.e. primitives such as cause, go, path, to, from are common in other frameworks.
• The formalization:– “John entered the room.” -> GO (John, [path [to: room]])– “John left the room.” -> GO (John, [path [from: room]])
• Large set of word classes with only a few more primitives – support the notion that a relatively small set of mechanisms can lead to human-level intelligence in all domains.
Cognitive Psychology and Neuroscience
• Much nonspatial or physical thought involves mechanisms which are for spatial and physical ones.
• Barsalou et al (2003) – human mapping visual and motor representations onto abstract, nonphysical concepts
• Spivey et al (2001) – associating sitting position with verb ‘push/respect’
• Ricahrdson et al (2003) – harder understanding sentences of certain combination of images and words.
• Warington et al (1984) – visual and motor regions activation during nonperceptual cognition
Constituents of Cognitive Substrates
• Cassimatis 2002 – preliminary lists includes reasoning about time, space, part-hood, categories, causation, uncertainty, belief, desire.
• Implied research program– 1) identify and implement a cognitive substrate– 2) find mappings from multiple domains onto the cogn
itive substrate– 3) automate the process of adapting a cognitive subst
rate so that it can solve problems in other domains
Implementing a Cognitive Substrate
• Polyscheme cognitive architecture: – enables multiple computational methods to be implem
ented much more ubiquitous.
• Two principles: – CFP common function principle– MIP multiple implementation principle
• Attention Fixation – Very different algorithms can all be reformulated in ter
ms of sequences of attention fixations
Common Functions• Forward inference. • Subgoaling. • Simulate alternate worlds. • Identity matching.
• i.e.implemeting an algorithms– Search. “When uncertain about whether A is true, represent the w
orld where A is true, perform forward inference, represent the world where A is not true, perform forward inference. If forward inference leads to further uncertainty, repeat.”
– Stochastic simulation. “When A is more likely than not-A, represent the world where A is true and perform forward inference in it more often than you do for the world where not-A is true.”
MIP for forward inference• Neural Networks.
– The activation of input units of a feedforward neural network leads to a change in the activation of the output units of the network.
– Rrepresent facts that can be inferred from the facts represented by the input units.
• Forward rule changing. – Production systems can be constructed to match the left-hand sides of p
roduction rules against a set of currently known facts to infer new facts represented by the right hand sides of rules.
• Ontologies. – When an object, o, is a member of category C in a category hierarchy, on
e can infer that o is a member of C1 … Cn, the ancestors of C.
Implementing an algorithm
A CFP
Subgoaling
Simulate alternate worlds
Search
Stochastic simulationIdentity
matching
Forward inference
Algorithms
Neural Networks
Forward Rule Chaining
Ontologies An MFP
Attention Fixation• Very different algorithms
– can all be reformulated in terms of sequences of attention fixations
• Inference algorithms (originally based on very different computational formalisms): – can be executed as sequences of a small set of
common functions (according to the CFP) that can be easily interleaved
– and that these common functions can be implemented using many different algorithms (according to the MIP)
Leveraging a Cognitive Substrate
• How a substrate on a Polyscheme model of human physical reasoning used to construct a natural language parser?
• Many grammatical structures have analogues to nonlinguistic cognitive structures.
• Formalism: – Events (e), Objects (o), Places (p)– Category(e, MotionEvent), Agent(e, x), Origin(e, p1),
Destination(e, p2), Occurs(e, t) – An unsupported object falls is:
• Location(o, p1, t1) + Below(p2, p1) + Empty(p2, t1)• Category(e, MotionEvent) + Origin(e, p1) + Destination(e, p2) + Occu
rs(e, t2) + Meets(t1, t2).
Leveraging a Cognitive Substrate
• Utterances are events: Category(e, dog-utterance), Occurs(e, t).
• Word order is temporal order: Category(e1, JohnUtterance), Occurs(e1, t1) Category(e2, BitUtterance), Occurs(e2, t2)Meets(t1, t2)…
• Physical and linguistic events both belong to categories organized hierarchically– Constituency is a parthood relation.– Coreference and binding are object-identity relationships.– Phrase attachment is an event identity relationship.
Conclusion: Benefits of Cognitive Substrates
1. Much easier to create an intelligent system for new domains
2. Much easier integration among domains
3. The problem of achieving human-level AI is reduced and simplified by mapping a relatively small set of problems onto a substrate