Artificial IntelligenceArtificial Intelligence ChallengesSlide 3Slide 4Proposed AI SystemsSlide 6Slide 7Slide 8Natural Language Vocal Interaction Between Live and Synthetic AgentsAgendaProblem Description and Motivation3 Phases of Automated Speech ProcessingSpeech-To-Text (STT) ProcessingSpeech-To-Text (STT) Processing (cont)Text-To-Speech (TTS) ProcessingNatural Language Processing (NLP)Natural Language Processing ChallengesNatural Language AmbiguitySlide 19NLVI Overview: Targets Military TrainersNLVI HistorySummaryNLVI SystemsWDB ComponentsArtificial 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.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?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?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?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.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.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.Natural Language Vocal Interaction Between Live and Synthetic AgentsKeith Garfield and Donald A. WashburnInstitute for Simulation & TrainingUniversity of Central Florida407-882-1342, 407-882-1433 [email protected], [email protected]•Problem Description and Motivation•Issues associated with automated voice processing•Natural Language Voice Interface (NLVI) Overview•SummaryProblem 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.3 Phases of Automated Speech ProcessingSpeech to Text Text to SpeechNaturalLanguageProcessingMICSPKRSpeech-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