1. DARPA Agent Markup Language Ashish Jain University of Colorado at Boulder

2. What is DAML ? Technology to enable software agents to dynamically identify and understand information sources. Formatting web so that it can easily be understood by intelligent agents Express ontologies (formal specification of a concept – vocabularies, inter-relationships etc) Add reasoning cues –Disjoint from, union of, transitive

3. DAML: Status Currently being explored at University level – SHOE (Maryland), OWL (Washington) – Largely grows from past DARPA projects But not transitioning –W3C focused on short-term gains: HTML/XML

4. DAML: Objectives 1. Create an Agent Mark-Up Language (DAML) built upon XML that allows users to provide machine-readable semantic annotations for specific communities of interest. 2. Create tools that embed DAML markup on to web pages and other information sources in a manner that is transparent and beneficial to the users. 3. Use these tools to build up, instantiate, operate, and test sets of agent-based programs that markup and use DAML

5. DAML vs HTML vs XML vs RDF HTML : –Limited set of tags, not suitable for search XML : – Extensible tags – Useful for data sharing but still not good for searching RDF: – We can only define global range on properties ( i.e. for all classes  apply constraints) –No mechanism for providing necessary and sufficient conditions –No support transitivity

6. DAML: Example 1. Find information about a researcher named James Hendler 2. Find a reference to a paper about SHOE coauthored by James Handler 3. Find a reference to the most recent paper about SHOE coauthored by James Handler

7. Query Processing (I) DAML ontologies for publication, researchers and topic have been built. First query can be formalized as : http://ai.src.com/daml/ontologies/Reseachers# James Hendler Dr.

8. Query Processing (II) After parsing the query, it is passed to DAML-Q ( DAML query engine) Looks for the namespace identifier, and sequentially searches the content of the web pages. Could be complicated because of presence of indirect ontological reference.

9. Inference in Queries (I) Uses first order theorem prover such as SNARK Written in LISP

10. Inference in Queries (II) Third query can be formalized as: (find) ?paperq such-that (and (pub-val ?paperq ?paper (author ?paper ?person (person-val (personq “James Hendler” ) ?person ) (about paper (paperq “SHOE”)) (= (pub-to-year ?paper) (year-fn ?natural))) prefer starts-after-starting-of on (year-fn ?natural) Time-limit 10)

11. Conclusions (I) Allows semantic interoperability at the level we currently have syntactic interoperability in XML –Revolutionizing web interoperability Objects in the web can be marked (manually or automatically) to include the following: –Description of data they contain –Description of function they provide –Description of data they provide This marks the environment for agents

12. Conclusion (II) Lack of user tools to create it Lack of agents that understand it But step in right direction