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<Self/>
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<Universe/>
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< P h y s i c a l U n i v e r s e / >
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<Think>
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<Memory/> <Remember/> <Remember/>
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<Self/>
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<Idea/>
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<Abstract/>
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<Observa<Perception/> tion/> <Perceive/>
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<Outside/>
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<Act/> <Controls/> <Effect/>
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FIGURE 11-7
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Primal Self portrait.
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<To> <Memory/> </To> </Path> </Remember> <Act> <Path> <From> <Idea/> </From> <To> <Controls/> </To> </Path> </Act> A <Path/> consists of one or more <Place/>s, with optional endpoints <From/> and <To/>. Within the <Self/> <Action/>s (generalizations of the LCS verb forms GO, BE, etc.) are also <Path/>s among abstract internal <Place/>s, collections of data generated and interpreted by traversing this schema-schema. The <Idea/> is the <Place/> for the results of the <Act/> of <Abstract/>ing <Perception/>s. Memory is the <Place/> within the <Self/> for the storage of <Interesting/> (in the sense of the AML <Histogram/>) <Observation/>s, Perception/>s, or <Idea/>s. Since an <Act/> is a <Path/> from an <Idea/> to <Controls/>, an <Idea/> may be a <Plan/> in the cognition cycle. At this level of abstraction, temporal reasoning and set theory do not exist, so <Plan/> cannot be fully expressed. A primal <Self/> portrait (Figure 11-7) may be generated by traversing this schema-schema. The use of <Self/> as schema-schema remains a challenging research issue. With present technology, however, it is feasible to autonomously evolve the CRA <Self/> into the <User/> domain via AML techniques like Q-learning. 11.2.3 Incremental Reinforcement Learning (RL)
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Declarative knowledge alone is insuf cient to guide AACR autonomous extensibility in the <User/> domain. Reinforcement can be a means of incrementally acquiring knowledge. Q-learning makes the degree of exploration explicit in the parameter (s, a) for a transition from system state s via
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candidate action a, and all forms of RL entail both the exploitation of current knowledge and the exploration of the unknown. 11.2.3.1 Exploitation: Finding WTOP The <Self/> may be used as a schema-schema for knowledge action evolution by the exploitation of current knowledge via RL. Suppose a <User/> tunes to WTOP every morning. The AACR has a goal of enhancing <Information/> <Preferences/> of the <User/>, so it needs to know if the <User/> is expressing a <Preference/> by tuning to WTOP. One way for a CR to nd out is to ask. This can be a pain in the neck to the user and not very informative to the AACR. AACR: What are you doing with the radio <User/>: I m listening to it you dummy. AACR: No, I mean the channel. <User/>: You are the radio, why don t you know what channel AACR: Sorry, I know what channel but what information are you getting from that channel <User/>: I m driving so be quiet, will you This continues until the AACR is returned for a refund or there is a traf c accident and lawsuit. In a more focused exchange the CR monitors the <User/> <Tuning/> to FM channel 107.7 in <Location> Fairfax County, VA </Location> reading the channel s <Annotation> News </Annotation>. The ability to read the text annotations is declared in RXML to the <Self/> (Expression 11-7) as a <Sensor/> along the <Sense/> <Path/> from the <FM-Radio/> of the <Outside/> to the <Observations/> inside the <Self/>. Expression 11-7 Reading FM Captions
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<Skill> <FM-Listen> <Outside> <FM> <From> <RF> 88 <Units> MHz </Units> </RF> </From> <To> <RF> 108 <Units> MHz </Units> </RF> </To> </FM> </Outside> <Action> <Call> read-fm-text.dll <CMD-line> <RF/> = X </CMD-line> <Returns> <Observation> <FM/> = X <Audio-Stream Stream = S <Annotation> WTOP NEWS </Annotation> </Observation> </Returns> </Call> </Action> </ FM-Listen> </Skill> The channel to which the user is tuned has the text <Annotation/> WTOP NEWS. The CRA <Self/> lists News as an <Information/> <Service/> for
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AUTONOMOUS EXTENSIBILITY
which a <User/> may express a <Preference/>. Thus, the AACR could ask the <User/> if it is OK to infer <User> <Information> <Preference> NEWS </Preference> </Information> . . . </User> If the user says OK, then the association is reinforced but it may be suppressed if the user says, No, I m listening to traf c and weather together on the eights, not to news right now. The <Self/> annotates positive and negative reinforcement separately and aggregates them when planning an action in a given state, forming Q(s, a) for NEWS versus for traf c and weather. RL to exploit existing knowledge might proceed via a focused dialog like the following: AACR: I would like to generalize from an observation. <User/>: OK, CR, go ahead. [Reinforces the inclination to generalize.] AACR: WTOP s broadcast is labeled news. Does that sound correct <User/>: Yes, CR, WTOP is a news station. [Reinforces con dence in FM annotation. CR might not parse that response but may just look for the positive reinforcement of yes without negatives.] AACR: Then can I generalize that you like news radio stations <User/>: Of course. But I like other things too. [Reinforces <Preference/>.] AACR: OK, I will conclude you like news, but I will not take this to be an exclusive inference regarding media content. Is that OK <User/>: OK. [Reduces news <Preference/> reinforcement.] The above interaction is a type of mixed-initiative that the <Self/> enables through the <User/> <Information/> <Preferences/> tags and associated data structures of the <Self/>. That kind of dialog quickly becomes as tedious as the PAL dialog at DARPATech 2005, where each brief phrase had to be con rmed by the PAL with OK or Alright. Researchers can stand this but most consumers will not. 11.2.3.2 RL Supports Incremental Planning The WTOP dialog implies that CR generated a plan based on an observation. RL inherently supports planning since RL aggregates reinforcement for alternative actions in given states. The choice of action given a state is the core operation in <Decide/> to <Act/>, and with such decisions, there is a plan. To conduct complicated sequences of actions may require further plan generation techniques. Learning to parse unknown languages and learning to solve challenging puzzles like Rubik s Cube require state space plan generation capabilities relevant to AACR. In particular, state space planning over