Learning to Share Meaning in a Multi-Agent System (Part I)

1. Learning to Share Meaning in a Multi-Agent System(Part I) Ganesh Padmanabhan

2. Article • Williams, A.B., "Learning to Share Meaning in a Multi-Agent System ", Journal of Autonomous Agents and Multi-Agent Systems, Vol. 8, No. 2, 165-193, March 2004. (Most downloaded article in Journal)

3. Overview • Introduction (part I) • Approach (part I) • Evaluation (part II) • Related Work (part II) • Conclusions and Future Work (part II) • Discussion

4. Introduction • One Common Ontology? Does that work? • If not, what issues do we face when agents have similar views of the world but different vocabularies? • Reconciling Diverse Ontologies so that Agents can communicate effectively when appropriate.

5. Diverse Ontology Paradgm: Questions Addressed • “How do agents determine if they know the same semantic concepts?” • “How do agents determine if their different semantic concepts actually have the same meaning?” • “How can agents improve their interpretation of semantic concepts by recursively learning missing discriminating attributes?” • “How do these methods affect the group performance at a given collective task?”

6. Ontologies and Meaning • Operational Definitions Needed • Conceptualization, ontology, universe of discourse, functional basis set, relational basis set, object, class, concept description, meaning, object constant, semantic concept, semantic object, semantic concept set, distributed collective memory

7. Conceptualization • All objects that an agent presumes to exist and their interrelationships with one another. • Tuple: Universe of Discourse, Functional Basis Set, Relational Basis Set

8. Ontology • Specification of a conceptualization • Mapping of language symbols to an agent’s conceptualization • Terms used to name objects • Functions to interpret objects • Relations in the agent’s world

9. Object • Anything we can say something about • Concrete or Abstract  classes • Primitive or Composite • Fictional or non-fictional

10. UOD and ontology • “The difference between the UOD and the ontology is that the UOD are objects that exist but until they are placed in an agent’s ontology, the agent does not have a vocabulary to specify objects in the UOD.”

11. Forming a Conceptualization • Agent’s first step at looking at the world. • Declarative Knowledge • Declarative Semantics • Interpretation Function maps an object in a conceptualization to language elements

12. Distributed Collective Memory

13. Approach Overview • Assumptions • Agents’ use of supervised inductive learning to learn representations for their ontologies. • Mechanics of discovering similar semantic concepts, translation, and interpretation. • Recursive Semantic Context Rule Learning for improved performance.

14. Key Assumptions • “Agents live in a closed world represented by distributed collective memory.” • “The identity of the objects in this world are accessible to all agents and can be known by the agents.” • “Agents use a knowledge structure that can be learned using objects in the distributed collective memory.” • “The agents do not have any errors in their perception of the world even though their perceptions may differ.”

15. Semantic Concept Learning • Individual Learning, i.e. learning one’s own ontology • Group Learning, i.e. one agent learning that another agent knows a particular concept

16. WWW Example Domain • Web Page = specific semantic object • Groupings of Web Pages = semantic concept or class • Analogous to Bookmark organization • Words and HTML tags are taken to be boolean features. • Web Page represented by boolean vector. • Concepts  Concept Vectors  Learner  Semantic Concept Description (rules)

17. Ontology Learning • Supervised Inductive Learning • Output = Semantic Concept Descriptions (SCD) • SCD are rules with a LHS and RHS etc. • Object instances are discriminated based on tokens contained within sometimes resulting in “…a peculiar learned descriptor vocabulary.” • Certainty Value

18. Locating Similar Semantic Concepts • Agent queries another agent for a concept by showing it examples. • Second agent receives examples and uses its own conceptualization to determine if it knows the concept (K), maybe knows it (M), or doesn’t know it (D). • For cases, K and M, the second agent sends back examples of what it thinks is the concept that was queried. • First agent receives the examples, and interprets those using its own conceptualization to “verify” that they are talking about the same concept. • If verified, the querying agent then adds that the other agent knows its concept to its own knowledge base.

19. Concept Similarity Estimation • Assuming two agents know a particular concept, it is feasible and probable given a large DCM, that the sets of concept defining objects differ completely. • Cannot simply assume that the target functions generated by each agent using supervised inductive learning from example will be the same. • Need to define other ways to estimate similarity.

20. Concept Similarity Estimation Function • Input: sample set of objects representing a concept in another agent • Output: Knows Concept (K), Might Know Concept (M), Don’t Know Concept(D). • Set of Objects  Tries mapping set of objects to each of its concepts using description rules  each concept receives an interpretation value  interpretation value is compared with thresholds to make K,M, or D determination. • Interpretation Value for one concept is the proportion of objects in the CBQ that were inferred to be this concept. • Positive Interpretation Threshold = how often this concept description correctly determined an object in the training set to belong to this concept • Negative Interpretation Threshold

21. Group Knowledge • Individual Knowledge • Verification

22. Translating Semantic Concepts • Same algorithm as for locating similar concepts in other agents. • Two concepts determined to be the same, can be translated regardless of label in the ontologies. • Difference: After verification, knowledge is stored as “Agent B knows my semantic concept X as Y.”