| 1. |
- Aghighi, Meysam, 1988-
(författare)
-
Computational Complexity of some Optimization Problems in Planning
- 2017
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Automated planning is known to be computationally hard in the general case. Propositional planning is PSPACE-complete and first-order planning is undecidable. One method for analyzing the computational complexity of planning is to study restricted subsets of planning instances, with the aim of differentiating instances with varying complexity. We use this methodology for studying the computational complexity of planning. Finding new tractable (i.e. polynomial-time solvable) problems has been a particularly important goal for researchers in the area. The reason behind this is not only to differentiate between easy and hard planning instances, but also to use polynomial-time solvable instances in order to construct better heuristic functions and improve planners. We identify a new class of tractable cost-optimal planning instances by restricting the causal graph. We study the computational complexity of oversubscription planning (such as the net-benefit problem) under various restrictions and reveal strong connections with classical planning. Inspired by this, we present a method for compiling oversubscription planning problems into the ordinary plan existence problem. We further study the parameterized complexity of cost-optimal and net-benefit planning under the same restrictions and show that the choice of numeric domain for the action costs has a great impact on the parameterized complexity. We finally consider the parameterized complexity of certain problems related to partial-order planning. In some applications, less restricted plans than total-order plans are needed. Therefore, a partial-order plan is being used instead. When dealing with partial-order plans, one important question is how to achieve optimal partial order plans, i.e. having the highest degree of freedom according to some notion of flexibility. We study several optimization problems for partial-order plans, such as finding a minimum deordering or reordering, and finding the minimum parallel execution length.
|
|
| 2. |
- Bonet, Blai, et al.
(författare)
-
General Policies, Subgoal Structure, and Planning Width
- 2024
-
Ingår i: The journal of artificial intelligence research. - : AI ACCESS FOUNDATION. - 1076-9757 .- 1943-5037. ; 80, s. 475-516
-
Tidskriftsartikel (refereegranskat)abstract
- It has been observed that many classical planning domains with atomic goals can be solved by means of a simple polynomial exploration procedure, called IW, that runs in time exponential in the problem width, which in these cases is bounded and small. Yet, while the notion of width has become part of state-of-the-art planning algorithms such as BFWS, there is no good explanation for why so many benchmark domains have bounded width when atomic goals are considered. In this work, we address this question by relating bounded width with the existence of general optimal policies that in each planning instance are represented by tuples of atoms of bounded size. We also define the notions of (explicit) serializations and serialized width that have a broader scope, as many domains have a bounded serialized width but no bounded width. Such problems are solved nonoptimally in polynomial time by a variant of the Serialized IW algorithm. Finally, the language of general policies and the semantics of serializations are combined to yield a simple, meaningful, and expressive language for specifying serializations in compact form in the form of sketches, which can be used for encoding domain control knowledge by hand or for learning it from examples. Sketches express general problem decompositions in terms of subgoals, and terminating sketches of bounded width express problem decompositions that can be solved in polynomial time.
|
|
| 3. |
- Bonet, Blai, et al.
(författare)
-
On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies
- 2024
-
Ingår i: Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2024).
-
Konferensbidrag (refereegranskat)abstract
- Recently,a simple but powerful language for expressing and learning general policies and problem decompositions (sketches) has been introduced in terms of rules defined over a set of Boolean and numerical features. In this work, we consider three extensions of this language aimed at making policies and sketches more flexible and reusable: internal memory states, as in finite state controllers; indexical features, whose values are a function of the state and a number of internal registers that can be loaded with objects; and modules that wrap up policies and sketches and allow them to call each other by passing parameters. In addition, unlike general policies that select state transitions rather than ground actions, the new language allows for the selection of such actions. The expressive power of the resulting language for policies and sketches is illustrated through a number of examples.
|
|
| 4. |
- Drexler, Dominik, 1993-, et al.
(författare)
-
Expressing and Exploiting Subgoal Structure in Classical Planning Using Sketches
- 2024
-
Ingår i: The journal of artificial intelligence research. - : AI ACCESS FOUNDATION. - 1076-9757 .- 1943-5037. ; 80, s. 171-208
-
Tidskriftsartikel (refereegranskat)abstract
- Width-based planning methods deal with conjunctive goals by decomposing problems into subproblems of low width. Algorithms like SIW thus fail when the goal is not easily serializable in this way or when some of the subproblems have a high width. In this work, we address these limitations by using a simple but powerful language for expressing finer problem decompositions introduced recently by Bonet and Geffner, called policy sketches. A policy sketch R over a set of Boolean and numerical features is a set of sketch rules C -> E that express how the values of these features are supposed to change. Like general policies, policy sketches are domain general, but unlike policies, the changes captured by sketch rules do not need to be achieved in a single step. We show that many planning domains that cannot be solved by SIW are provably solvable in low polynomial time with the SIWR algorithm, the version of SIW that employs user-provided policy sketches. Policy sketches are thus shown to be a powerful language for expressing domain-specific knowledge in a simple and compact way and a convenient alternative to languages such as HTNs or temporal logics. Furthermore, they make it easy to express general problem decompositions and prove key properties of them like their width and complexity.
|
|
| 5. |
- Drexler, Dominik, 1993-, et al.
(författare)
-
Expressing and Exploiting the Common Subgoal Structure of Classical Planning Domains Using Sketches
- 2021
-
Ingår i: 18th International Conference on Principles of Knowledge Representation and Reasoning, Hanoi, November 3-12, 2021. - : International Joint Conferences on Artificial Intelligence Organization (IJCAI Organization). - 9781956792997
-
Konferensbidrag (refereegranskat)abstract
- Width-based planning methods deal with conjunctive goals by decomposing problems into subproblems of low width. Algorithms like SIW thus fail when the goal is not easily serializable in this way or when some of the subproblems have a high width. In this work, we address these limitations by using a simple but powerful language for expressing finer problem decompositions introduced recently by Bonet and Geffner, called policy sketches. A policy sketch R over a set of Boolean and numerical features is a set of sketch rules that express how the values of these features are supposed to change. Like general policies, policy sketches are domain general, but unlike policies, the changes captured by sketch rules do not need to be achieved in a single step. We show that many planning domains that cannot be solved by SIW are provably solvable in low polynomial time with the SIW_R algorithm, the version of SIW that employs user-provided policy sketches. Policy sketches are thus shown to be a powerful language for expressing domain-specific knowledge in a simple and compact way and a convenient alternative to languages such as HTNs or temporal logics. Furthermore, they make it easy to express general problem decompositions and prove key properties of them like their width and complexity.
|
|
| 6. |
- Drexler, Dominik, 1993-, et al.
(författare)
-
Learning Hierarchical Policies by Iteratively Reducing the Width of Sketch Rules
- 2023
-
Ingår i: 20th International Conference on Principles of Knowledge Representation and Reasoning, Rhodes, Greece, September 2-8, 2023.
-
Konferensbidrag (refereegranskat)abstract
- Hierarchical policies are a key ingredient of intelligent behavior, expressing the different levels of abstraction involved in the solution of a problem. Learning hierarchical policies, however, remains a challenge, as no general learning principles have been identified for this purpose, despite the broad interest and vast literature in both model-free reinforcement learning and model-based planning. In this work, we introduce a principled method for learning hierarchical policies over classical planning domains, with no supervision from small instances. The method is based on learning to decompose problems into subproblems so that the subproblems have a lower complexity as measured by their width. Problems and subproblems are captured by means of sketch rules, and the scheme for reducing the width of sketch rules is applied iteratively until the final sketch rules have zero width and encode a general policy. We evaluate the learning method on a number of classical planning domains, analyze the resulting hierarchical policies, and prove their properties. We also show that learning hierarchical policies by learning and refining sketches iteratively is often more efficient than learning flat general policies in one shot.
|
|
| 7. |
- Drexler, Dominik, 1993-, et al.
(författare)
-
Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width
- 2022
-
Ingår i: Proceedings of the 32nd International Conference on Automated Planning and Scheduling (ICAPS2022).
-
Konferensbidrag (refereegranskat)abstract
- Recently, sketches have been introduced as a general language for representing the subgoal structure of instances drawn from the same domain. Sketches are collections of rules of the form C -> E over a given set of features where C expresses Boolean conditions and E expresses qualitative changes. Each sketch rule defines a subproblem: going from a state that satisfies C to a state that achieves the change expressed by E or a goal state. Sketches can encode simple goal serializations, general policies, or decompositions of bounded width that can be solved greedily, in polynomial time, by the SIW_R variant of the SIW algorithm. Previous work has shown the computational value of sketches over benchmark domains that, while tractable, are challenging for domain-independent planners. In this work, we address the problem of learning sketches automatically given a planning domain, some instances of the target class of problems, and the desired bound on the sketch width. We present a logical formulation of the problem, an implementation using the ASP solver Clingo, and experimental results. The sketch learner and the SIW_R planner yield a domain-independent planner that learns and exploits domain structure in a crisp and explicit form.
|
|
| 8. |
- Geffner, Hector
(författare)
-
Target Languages (vs. Inductive Biases) for Learning to Act and Plan
- 2022
-
Ingår i: THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE. - : ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. - 9781577358763 ; , s. 12326-12333
-
Konferensbidrag (refereegranskat)abstract
- Recent breakthroughs in AI have shown the remarkable power of deep learning and deep reinforcement learning. These developments, however, have been tied to specific tasks, and progress in out-of-distribution generalization has been limited. While it is assumed that these limitations can be overcome by incorporating suitable inductive biases, the notion of inductive biases itself is often left vague and does not provide meaningful guidance. In the paper, I articulate a different learning approach where representations do not emerge from biases in a neural architecture but are learned over a given target language with a known semantics. The basic ideas are implicit in mainstream AI where representations have been encoded in languages ranging from fragments of first-order logic to probabilistic structural causal models. The challenge is to learn from data, the representations that have traditionally been crafted by hand. Generalization is then a result of the semantics of the language. The goals of this paper are to make these ideas explicit, to place them in a broader context where the design of the target language is crucial, and to illustrate them in the context of learning to act and plan. For this, after a general discussion, I consider learning representations of actions, general policies, and subgoals ("intrinsic rewards"). In these cases, learning is formulated as a combinatorial problem but nothing prevents the use of deep learning techniques instead. Indeed, learning representations over languages with a known semantics provides an account of what is to be learned, while learning representations with neural nets provides a complementary account of how representations can be learned. The challenge and the opportunity is to bring the two together.
|
|
| 9. |
- Haslum, Patrik, et al.
(författare)
-
Admissible Heuristics for Optimal Planning
- 2000
-
Ingår i: Proceedings of the 5th International Conference on Artificial Intelligence Planning and Scheduling (AIPS). - : AAAI Press. - 9781577351115 ; , s. 140-149
-
Konferensbidrag (refereegranskat)abstract
- hsp and hspr are two recent planners that search the state-space using an heuristic function extracted from Strips encodings. hsp does a forward search from the initial state recomputing the heuristic in every state, while hspr does a regression search from the goal computing a suitable representation of the heuristic only once. Both planners have shown good performance, often producing solutions that are competitive in time and number of actions with the solutions found by Graphplan and sat planners. hsp and hsp r, however, are not optimal planners. This is because the heuristic function is not admissible and the search algorithms are not optimal. In this paper we address this problem. We formulate a new admissible heuristic for planning, use it to guide an ida search, and empirically evaluate the resulting optimal planner over a number of domains. The main contribution is the idea underlying the heuristic that yields not one but a whole family of polynomial and admissible heuristics that trade accuracy for efficiency. The formulation is general and sheds some light on the heuristics used in hsp and Graphplan, and their relation. It exploits the factored (Strips) representation of planning problems, mapping shortest-path problems in state-space into suitably defined shortest-path problems in atom-space. The formulation applies with little variation to sequential and parallel planning, and problems with different action costs.
|
|
| 10. |
- Haslum, Patrik, et al.
(författare)
-
Heuristic Planning with Time and Resources
- 2001
-
Ingår i: Proceedings of the 6th European Conference on Planning (ECP).
-
Konferensbidrag (refereegranskat)abstract
- We present an algorithm for planning with time and resources based on heuristic search. The algorithm minimizes makespan using an admissible heuristic derived automatically from the problem instance. Estimators for resource consumption are derived in the same way. The goals are twofold: to show the flexibility of the heuristic search approach to planning and to develop a planner that combines expressivity and performance. Two main issues are the definition of regression in a temporal setting and the definition of the heuristic estimating completion time. A number of experiments are presented for assessing the performance of the resulting planner.
|
|
