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Artificial Intelligence

Artificial Intelligence. Artificial Intelligence (AI) is the name given to encoding intelligent or humanistic behaviors in computer software. Problem: Nobody has created a widely accepted definition of intelligence. At one time was considered a uniquely human quality.

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Artificial Intelligence

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  1. Artificial Intelligence • Artificial Intelligence (AI) is the name given to encoding intelligent or humanistic behaviors in computer software. • Problem: Nobody has created a widely accepted definition of intelligence. • At one time was considered a uniquely human quality. • Now generally accepted to be an animal quality. • Has been linked to tool use, tool creation, learning, adaptation to novel situations, capacity for abstraction. • Problem: Nobody has created a widely accepted definition of artificial intelligence. • Cognitive models attempt to recreate the actual processes of the human brain. • Behavioral models attempt to produce behavior that is reasonable for a situation regardless of how the behavior was produced. • Tend to focus on reasoning, behavior, learning, adaptation.

  2. Artificial Intelligence Challenges • Format of Knowledge – the data structures we have discussed so far capture data values, but not data meaning. • Graphs, trees, lists. • Size of Knowledge – How do you store it all? Once stored how do you access only the pertinent items and skip over irrelevant items. • Humans are good at this, though we don’t know why. • Relationships between Pieces of Knowledge – This is worse than the size of knowledge. • Given n items and m types of relationships, there are m*(n2) possible relationships. • Is it better to explicitly represent relationships or derive them in real time as we need them?

  3. Artificial Intelligence Challenges • Ambiguity – Knowledge ultimately represents natural phenomena that are inherently ambiguous. How do we resolve this? • Acquiring Knowledge – How does one combine new and old information? • Relationship to old knowledge. • Negative learning – can we detect false information or contradictions? • Can we quantify the reliability of the knowledge? “Truth nets” attempt to do this. • Deriving Knowledge, Abstracting Knowledge – Given a set of information, can I derive new information? Reasoning systems and proof systems attempt to do this. Can I group similar knowledge items into a more general single item?

  4. Artificial Intelligence Challenges • Adaptation – How can I use what I know in new situations? What constitutes a new situation? • Sensing – Sensing is the ability to take in information from the world around you. Virtually all computer systems “Sense” 1’s and 0’s through keyboard, mouse, and serial port. • Perception – Perception is related to sensing, in that the meaning of the thing sensed is discovered. Auto example. • Emotional Intelligence – • “I think therefore I am.” Renee Descartes, about 1640. • “Descartes Error” is a book by Antonio R Damasio, 1995, in which he proposes that traditional rational thought without emotional content fails to create intelligent behavior. • Social Knowledge, Ethics – How do I behave with my teammates, strangers, friend, foe? What are my responsibilities towards others as well as myself?

  5. Proposed AI Systems • Rule Based Behavior – designed behavior specifying sets of conditions and responses. • Finite-State Machines – Graphical representations of the state of systems, with sensory inputs leading to transitions from state to state. • Scripts – attempts to make behavior production tractable by anticipating behaviors that follow certain sequences. “The Restaraunt Script” is a typical example; we expect roughly the same behaviors (be greeted, be seated, order drinks, get drinks, …) no matter what restaurant we are in. • Case-based and Context-Based Reasoning – attempt to reduce search space of possible behaviors by only considering those associated with certain situations or contexts.

  6. Proposed AI Systems • Cognitive Models – Attempts to model cognitive processes. • Cognitive Processes – attempt to match human thinking by reproducing human thought processes. • Neural Nets – attempt to match human thinking by reproducing brain synapse structures.

  7. Proposed AI Systems • Emergent Behavior – Overall behavior resulting from the interaction of smaller rule sets or individual agents. Overall behavior is not designed but desired. • Genetic Algorithms – represents behavioral rules as long strings, termed “genomes.” Behavior is evolved as various genomes are tried and evaluated. Higher rated genomes are allowed to survive and “reproduce” with other high ranking genomes. • Ant Logic – Named after the behavior of ant colonies, where individuals have very simple rule sets, but complex group behavior emerges through interactions. • Synthetic Social Structures – Models more complex animal social behaviors, such as those found in herds and packs. Allows efficient interaction without much communication.

  8. Natural Language Vocal Interaction Between Live and Synthetic Agents Keith Garfield and Donald A. Washburn Institute for Simulation & Training University of Central Florida 407-882-1342, 407-882-1433 kgarfiel@ist.ucf.edu,dwashbur@ist.ucf.edu

  9. Agenda • Problem Description and Motivation • Issues associated with automated voice processing • Natural Language Voice Interface (NLVI) Overview • Summary

  10. Problem Description and Motivation • Overall Goal: Produce a technology allowing natural language vocal interactions between live and virtual entities. • Overall Motivation: Enable • Reduced staffing requirements appropriately. • Virtual team members. • Virtual trainers/coachers/advisors.

  11. 3 Phases of Automated Speech Processing Speech to Text Natural Language Processing MIC Text to Speech SPKR

  12. Speech-To-Text (STT) Processing • Purpose: Convert the spoken word to text. • Techniques: • Match signal (digitized) to dictionary of sounds and words. • Improve accuracy via syntactic analysis (not semantic). • Improve accuracy by tracking history of the speaker. • Challenges: • Differences in speech between persons/genders • Differences in pronunciation given by the same person over time and in different situations.

  13. Speech-To-Text (STT) Processing (cont) • Notes: • Quite a bit of research currrently in this area • Bell Labs • Carnegie Mellon University (SPHINX) • Commercial Products available and used successfully. • VivaVoice • DragonNaturallySpeaking • MSSpeech API • Speaker-dependent systems achieve about 95% accuracy. • Speaker-independent systems may have poor accuracy, rely on limited vocabularies. • Future systems will probably be multi-modal (voice and gesture, voice and touch-screen)

  14. Text-To-Speech (TTS) Processing • Purpose: Converting text to spoken word. • Techniques: • Match text to phonetic dictionary of sounds/words. • Incorporate emotional content by intonations as suggested by punctuation and context. • Direct changes in pitch, volume, and speed to be imbedded explicitly in the text using special symbols (XML). • Challenges: • Lack of models relating intonations to emotion or intent. • Technically difficult to reproduce natural sounds. • Notes: • Commercial product available; not heavily researched by IST.

  15. Natural Language Processing (NLP) • Purpose (1): Extract meaning from text. • Purpose (2): Compose text conveying a specific meaning. • Techniques: • Parse sentences, often using Finite State Machine models ot tree-like data structures. • Store meaning in a knowledge representation database, often rule-based or realtional database. • Produce sentences using parse trees and semi-random word selection to compose sentences.

  16. Natural Language Processing Challenges • Parsing Sentences: Syntax and semantics are intertwined in natural languages. • Storing Meaning: Knowledge representation is still a difficult problem. Number of rules and relationships required to cover non-trivial domains is large. • Extracting Meaning from text: • Word meanings change when context changes. • Idioms, metaphors, and similes provide challenges. • Emotional content colors meaning (e.g. sarcasm or humor)

  17. Natural Language Ambiguity • Lexical Ambiguity - one word, many meanings • Stay away from the bank. • Structural Ambiguity - one sentence, differents grammatical structure. • He saw that gasoline can explode. (Source: Winograd, "Computer Software for Working with Language)

  18. Natural Language Ambiguity • Deep Structural Ambiguity - Same sentence, same grammatical structure, different meaning. • The chickens are ready to eat. • Semantic Ambiguity - Same phrase can have two meanings. • David wants to marry a Norwegian. • Pragmatic Ambiguity - confusin use of pronouns. • She dropped a plate on the table and broke it. (Source: Winograd, "Computer Software for Working with Language)

  19. NLVI Overview: Targets Military Trainers Immersive Command Environments Station

  20. NLVI History • Voice Federate (VF) project is a predecessor of NLVI. • VF demonstrated the feasibility of allowing vocal control over synthetic entities in an immersive simulation. • VF used SAIC’s Dismounted Infantry Semi-Automated Forces (DISAF) as a CGF platform. • VF allowed basic command and control over CGF entities • Allowable commands were a subset of existing DISAF unit and individual behaviors. No new behaviors created. • Scripted synthetic speech was generated to confirm receipt of orders, give notice of task completions, and provide spot reports when enemy forces sighted.

  21. Summary • Automated voice processing can be broken down into Speech-To-Text, Natural Language Processing, and Text-to-Speech phases. • The Natural Language Vocal Interface (NLVI) will allow limited natural language conversations between live and synthetic participants in simulations. • NLVI makes use of the limited knowledge domain and formalized military speech to aid in voice processing. • NLVI allows for queries to resolve ambiguities in the natural language processing.

  22. WDBInterface WDB Speech To Text (STT) Ln to Lc Translator Human Speech CGFInterface CGF Platform Synthetic Speech Text To Speech (TTS) Lc to LnTranslator Natural Language (Ln) CGF Language (Lc) NLVI Systems

  23. WDB Components Engine Receives information - Generates responses - Updates STS/LTL/CBQ Long Term Status (LTS) Echolon Structures Mission Goals, Intel Terrain/Nav Info Short Term Status (STS) Locations Current task Visibility/Threats Conditional Behavior Queue (CBQ) Misnomer A set of possible behaviors From which to choose <predicate><behavior> Ambiguous behaviors generate info requests

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