Interpreter Agent Architecture

• Interpreter Agent is a computational entity that interprets and dynamically modifies its own evaluators through meta-level communication and evolving dialogue protocols.
• It employs nondeterministic evaluation techniques, such as the amb construct and require clauses, to explore multiple outcomes and enable systematic backtracking.
• The STROBE model leverages layered interpreters and meta-adaptation to support dynamic service generation and protocol evolution in distributed environments.

An Interpreter Agent is a computational or software entity designed to process, evaluate, and dynamically adapt the interpretation of programmatic, logical, or communicative specifications through mechanisms that include evolving interpreters, meta-level learning, and interactive dialogue. This paradigm, exemplified by the STROBE model, approaches agents as Scheme interpreters whose internal evaluation mechanisms can be modified through communication, including the refinement of both control and meta-level evaluators via agent interactions (0802.1393).

1. The Interpreter Agent Model: Architecture and Learning Levels

The interpreter agent model is built on the core idea that agents are fundamentally program interpreters capable not just of evaluating code, but also of altering their own evaluators. In the STROBE framework, each agent contains one or more Scheme interpreters:

• Global interpreter: Maintains the agent’s private knowledge and domain logic.
• Inter-agent interpreters: Customized evaluators that represent the agent’s model of each individual peer, allowing for tailored interaction semantics.

Through communication (modeled as Scheme expressions), agents learn at three principal levels:

Level	Target	Example Action
Data	Variable/value-level updates (e.g., storing new facts)	Set or update bindings
Control	Functions/processes (e.g., new methods to manipulate data)	Define procedures
Meta	Modifying the evaluator (interpreter’s code and semantics itself)	Alter message evaluation function

The transition from a static REPL agent (Read-Eval-Print-Listen) is achieved by exposing the interpreter component to learning, thus enabling the agent to process messages that not only request state changes but can also modify its own evaluation pipeline, such as incorporating new performatives in agent communication languages like KQML.

2. Nondeterminism and Constraint-Driven Dialogue

A central innovation in interpreter agents is the use of nondeterministic interpretation, realized via the amb construct familiar from non-deterministic Scheme:

$$amb(\mathrm{exp}_1, \mathrm{exp}_2, \dots, \mathrm{exp}_n)$$

This construct enables expressions to yield multiple outcomes. Agent dialogues exploit this capability by incrementally adding constraints via require clauses:

$\text{require}(p) \equiv \begin{cases} \text{continue evaluation if %%%%0%%%% is true} \ \text{invoke the failure continuation %%%%1%%%% if %%%%2%%%% is false} \end{cases}$

The evaluation protocol is extended from the classic:

$eval(e, r, k)$

to

$amb\_eval(e, r, k_s, k_f)$

where $k_s$ and $k_f$ are continuations for success and failure. This protocol drives a "search through the evaluation tree": every refinement in agent dialogue prunes the possible outcomes by adding new constraints, with failure continuations enabling systematic backtracking to find alternative solutions.

3. Interpreter Modification via Meta-Level Communication

Meta-level learning in interpreter agents is a process in which the interpreter itself is modified during runtime by communication acts. This is illustrated in the “teacher–student” pattern: a teacher sends a message containing Scheme code that updates the student’s message evaluator, e.g., to recognize a new performative. The message protocol is typically:

• Assertion: (kqmlmsg 'assertion sender receiver content)
• Order: (kqmlmsg 'order sender receiver content)

where content is code that can modify the receiver's interpreter, for instance by redefining the ambevaluate-kqmlmsg function to handle a new dialogue act.

Each such communication refines not only the agent’s control structures, but the very semantics by which messages are parsed, interpreted, and acted upon—a powerful method for evolving communication protocols without requiring prior universal agreement on protocol semantics.

4. Dynamic Service Generation and Specification-by-Dialogue

Interpreter agents excel at dynamic, specification-by-dialogue construction of services, an ability particularly beneficial in distributed and Grid environments. For instance, in dynamic ticket purchasing:

• The client iteratively sends constraints (e.g., (require (eq? depart 'montpellier))).
• The service agent aggregates these constraints into a nondeterministic find-ticket function.
• Each invocation of find-ticket searches for a solution consistent with the accumulated constraints.
• The try-again performative triggers alternative solutions via the failure continuation.

This model allows for the joint, incremental refinement of a specification by two or more agents, essentially building and executing a program “on the fly,” as opposed to the traditional bifurcation of design and implementation phases.

5. Theoretical Properties and System Composition

The interpreter agent approach in STROBE leverages:

• First-class continuations: Scheme's primitives for capturing the state of computation underpin the advanced backtracking semantics needed for constraint satisfaction.
• Self-modifying code: Reification of the evaluator allows agents to extend their “language” at runtime in response to communication.
• Layered interpreters: Each agent can maintain several interpreters tailored to its partners, reflecting individual communication histories and evolving semantics independently.
• Distributed computation: Agents can teach and share optimized routines (e.g., memoized recursion), facilitating cooperative distributed processing in environments like computational Grids.

This extends the expressive capacity beyond typical multiagent systems, allowing agents not only to share and enforce rules, but to redefine what constitutes a valid rule, performative, or service signature at runtime.

6. Applicability, Limitations, and Directions

• Broader applicability: While demonstrated in service orchestration (as in Grid e-commerce), this model generalizes to any domain where on-the-fly specification and adaptive protocol evolution are beneficial.
• Implementation considerations: The reliance on Scheme’s reflection and continuation features makes portability to other languages nontrivial unless equivalent meta-programming constructs exist.
• Limitations: Without care, meta-level learning may induce semantic drift between agents unless further consistency protocols or negotiation mechanisms are introduced.
• Distinctiveness: The model uniquely supports dynamic modification not just of state or behavior, but of the very interpreter executing agent code, enabling a robust form of meta-adaptation unavailable in purely behavioral learning systems.

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The interpreter agent paradigm, as instantiated by the STROBE model, advances the state of agent architectures by supporting robust meta-level adaptation, fine-grained constraint negotiation in dialogue, and dynamic, programmatic service construction. Nondeterminism and communication-driven interpreter evolution are central features, enabling agents not only to process and refine specifications interactively, but to modify and extend their own processing semantics in open-ended environments (0802.1393).