Bibliographic record
Abstract
A shopping assistant agent system is presented, and its advantages and disadvantages are discussed. The system is based on a lightweight agent implementation called TEEMA (TRLabs Execution Environment for Mobile Agents). The TEEMA platform has been built adopting the concept of a microkernel, providing agents with a small number of basic services for communication, migration, and location. Additional services can be added on top of TEEMA, like name services, storage services, security services and database services. The shopping assistant agent system facilitates supermarket shopping. It works as follows. The user at home sends an agent with a shopping list to selected supermarkets. The agent then travels to each supermarket and retrieves a limited price list. The agent makes use of a residential gateway to protect the user information. The agent then returns to the user, and the user is informed of the results of the search. If the user decides to go to a supermarket, an agent is sent there through the residential gateway. The agent then registers to have access to the complete price lists. Registered agents have to be retrieved locally using a wireless-enabled PDA. When the user arrives at the supermarket, the user's PDA receives the agent. The user is then presented with a complete shopping list, with relevant information on special offers, and with an aisle map for the goods on the list. The system is distributed; its main logical components are ideally located at the user's location, at a residential gateway, at a mobile terminal, and at each participating supermarket. The system architecture is presented, together with the integration strategy to make the system work with legacy database and server software. The paper discusses strengths and weaknesses of this approach, and it compares the system with other supermarket shopping systems. The conclusions show that there is promise for this approach, provided that extreme care is used in developing the user interface.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".