PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains
Authors: Vaishak Belle, Hector Levesque
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider the empirical behavior of prego in this section, where we measure the CPU time in milliseconds (ms) on non-trivial projection tasks, which is then contrasted with classical regression in a manner explained shortly. For the experimental setup, we imagine a planner to have generated a number of plans of increasing length. For our purpose, these plans are randomly generated action sequences, possibly involving combinations of noisy and noise-free actions and sensors, and thus, are representative of the plan search space. All experiments were run wrt the simple robot domain, on Mac OS X 10.8 using a system with a 2.4 GHz Intel Core 2 Duo processor, 4 GB RAM, and racket v5.3.6. |
| Researcher Affiliation | Academia | Vaishak Belle and Hector J. Levesque Dept. of Computer Science University of Toronto Toronto, Ontario M5S 3H5, Canada {vaishak, hector}@cs.toronto.edu |
| Pseudocode | Yes | Definition 1: Given a BAT D, a situation-suppressed expression e, and an action sequence σ, we define R[e, σ] as a situation-suppressed formula e as follows: 1. If e is a fluent: if σ = ϵ (is empty), then e = e; if σ = σ a then e = R(SSAe(a), σ ). 2. If e is a number, constant or variable, then e = e. 3. If e is Bel(φ, now) then f INIT G[φ, σ] where G is an operator for obtaining a (mathematical) expression from the belief argument φ, defined below. 4. Else e is (e1 e2 . . . en) and e = (R[e1, σ] R[e2, σ] . . . R[en, σ]) where is any mathematical operator over expressions, such as , , =, +, If, N, etc. ... Definition 2: Let D and σ be as above. Given any situationsuppressed fluent formula φ, we define G[φ, σ] to be a situation-suppressed expression e as follows: 1. If σ = ϵ, then e = If φ Then 1 Else 0. 2. Else, σ = σ α(t); let α(t ) = alt(α(t), u) and u R[Lα(t ), σ ] G[R[φ, α(t )], σ ]. |
| Open Source Code | No | The paper mentions that "prego is fully implemented" and "The system is realized in the racket dialect of the scheme family (racket-lang.org)" but does not provide an explicit statement or link for the open-source availability of the prego system's code. |
| Open Datasets | No | The paper describes a simulated robot domain with an initial uniform distribution for the fluent 'h' on [2,12], but it does not use or provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes experiments run on generated action sequences within a simulated robot domain but does not provide specific dataset split information (e.g., percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | Yes | All experiments were run wrt the simple robot domain, on Mac OS X 10.8 using a system with a 2.4 GHz Intel Core 2 Duo processor, 4 GB RAM, and racket v5.3.6. |
| Software Dependencies | Yes | The system is realized in the racket dialect of the scheme family (racket-lang.org)." and "All experiments were run wrt the simple robot domain... and racket v5.3.6. |
| Experiment Setup | Yes | The prego language is a simple representation language with a LISP-like syntax. A domain in prego is modeled as a basic action theory (or BAT) made up of the following five expressions (which we will illustrate immediately below): 1. (define-fluents fluent fluent ...) 2. (define-ini-p-expr expr) 3. (define-ss-exprs fluent act expr act expr . . .) 4. (define-l-exprs act expr act expr . . .) 5. (define-alts act altfn act altfn . . .) and "For the experimental setup, we imagine a planner to have generated a number of plans of increasing length. For our purpose, these plans are randomly generated action sequences, possibly involving combinations of noisy and noise-free actions and sensors, and thus, are representative of the plan search space. |