143 research outputs found
Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering
We introduce multi-goal multi agent path finding (MAPF) which
generalizes the standard discrete multi-agent path finding (MAPF) problem.
While the task in MAPF is to navigate agents in an undirected graph from their
starting vertices to one individual goal vertex per agent, MAPF assigns
each agent multiple goal vertices and the task is to visit each of them at
least once. Solving MAPF not only requires finding collision free paths
for individual agents but also determining the order of visiting agent's goal
vertices so that common objectives like the sum-of-costs are optimized. We
suggest two novel algorithms using different paradigms to address MAPF:
a heuristic search-based search algorithm called Hamiltonian-CBS (HCBS) and a
compilation-based algorithm built using the SMT paradigm, called
SMT-Hamiltonian-CBS (SMT-HCBS). Experimental comparison suggests limitations of
compilation-based approach
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
The unification of statistical (data-driven) and symbolic (knowledge-driven)
methods is widely recognised as one of the key challenges of modern AI. Recent
years have seen large number of publications on such hybrid neuro-symbolic AI
systems. That rapidly growing literature is highly diverse and mostly
empirical, and is lacking a unifying view of the large variety of these hybrid
systems. In this paper we analyse a large body of recent literature and we
propose a set of modular design patterns for such hybrid, neuro-symbolic
systems. We are able to describe the architecture of a very large number of
hybrid systems by composing only a small set of elementary patterns as building
blocks.
The main contributions of this paper are: 1) a taxonomically organised
vocabulary to describe both processes and data structures used in hybrid
systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a
set of elementary patterns and a set of compositional patterns; 3) an
application of these design patterns in two realistic use-cases for hybrid AI
systems. Our patterns reveal similarities between systems that were not
recognised until now. Finally, our design patterns extend and refine Kautz'
earlier attempt at categorising neuro-symbolic architectures.Comment: 20 pages, 22 figures, accepted for publication in the International
Journal of Applied Intelligenc
Counterexample Guided Abstraction Refinement with Non-Refined Abstractions for Multi-Agent Path Finding
Counterexample guided abstraction refinement (CEGAR) represents a powerful
symbolic technique for various tasks such as model checking and reachability
analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been
applied for multi-agent path finding (MAPF), a problem where the task is to
navigate agents from their start positions to given individual goal positions
so that the agents do not collide with each other.
The recent CEGAR approach used the initial abstraction of the MAPF problem
where collisions between agents were omitted and were eliminated in subsequent
abstraction refinements. We propose in this work a novel CEGAR-style solver for
MAPF based on SAT in which some abstractions are deliberately left non-refined.
This adds the necessity to post-process the answers obtained from the
underlying SAT solver as these answers slightly differ from the correct MAPF
solutions. Non-refining however yields order-of-magnitude smaller SAT encodings
than those of the previous approach and speeds up the overall solving process
making the SAT-based solver for MAPF competitive again in relevant benchmarks
Agent programming in the cognitive era
It is claimed that, in the nascent âCognitive Eraâ, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., âAI as a serviceâ, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., âAI embedded into agentsâ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs
Adaptive search techniques in AI planning and heuristic search
State-space search is a common approach to solve problems appearing in artificial intelligence and other subfields of computer science. In such problems, an agent must find a sequence of actions leading from an initial state to a goal state. However, the state spaces of practical applications are often too large to explore exhaustively. Hence, heuristic functions that estimate the distance to a goal state (such as straight-line distance for navigation tasks) are used to guide the search more effectively. Heuristic search is typically viewed as a static process. The heuristic function is assumed to be unchanged throughout the search, and its resulting values are directly used for guidance without applying any further reasoning to them. Yet critical aspects of the task may only be discovered during the search, e.g., regions of the state space where the heuristic does not yield reliable values. Our work here aims to make this process more dynamic, allowing the search to adapt to such observations. One form of adaptation that we consider is online refinement of the heuristic function. We design search algorithms that detect weaknesses in the heuristic, and address them with targeted refinement operations. If the heuristic converges to perfect estimates, this results in a secondary method of progress, causing search algorithms that are otherwise incomplete to eventually find a solution. We also consider settings that inherently require adaptation: In online replanning, a plan that is being executed must be amended for changes in the environment. Similarly, in real-time search, an agent must act under strict time constraints with limited information. The search algorithms we introduce in this work share a common pattern of online adaptation, allowing them to effectively react to challenges encountered during the search. We evaluate our contributions on a wide range of standard benchmarks. Our results show that the flexibility of these algorithms makes them more robust than traditional approaches, and they often yield substantial improvements over current state-of-the-art planners.Die Zustandsraumsuche ist ein oft verwendeter Ansatz um verschiedene Probleme zu lösen, die in der KĂŒnstlichen Intelligenz und anderen Bereichen der Informatik auftreten. Dabei muss ein Akteur eine Folge von Aktionen finden, die einen Pfad von einem Startzustand zu einem Zielzustand bilden. Die ZustandsrĂ€ume von praktischen Anwendungen sind hĂ€ufig zu groĂ um sie vollstĂ€ndig zu durchsuchen. Aus diesem Grund leitet man die Suche mit Heuristiken, die die Distanz zu einem Zielzustand abschĂ€tzen; zum Beispiel lĂ€sst sich die Luftliniendistanz als Heuristik fĂŒr Navigationsprobleme einsetzen. Heuristische Suche wird typischerweise als statischer Prozess angesehen. Man nimmt an, dass die Heuristik wĂ€hrend der Suche eine unverĂ€nderte Funktion ist, und die resultierenden Werte werden direkt zur Leitung der Suche benutzt ohne weitere Logik darauf anzuwenden. Jedoch könnten kritische Aspekte des Problems erst im Laufe der Suche erkannt werden, wie zum Beispiel Bereiche des Zustandsraums in denen die Heuristik keine verlĂ€sslichen AbschĂ€tzungen liefert. In dieser Arbeit wird der Suchprozess dynamischer gestaltet und der Suche ermöglicht sich solchen Beobachtungen anzupassen. Eine Art dieser Anpassung ist die Onlineverbesserung der Heuristik. Es werden Suchalgorithmen entwickelt, die SchwĂ€chen in der Heuristik erkennen und mit gezielten Verbesserungsoperationen beheben. Wenn die Heuristik zu perfekten Werten konvergiert ergibt sich daraus eine zusĂ€tzliche Form von Fortschritt, wodurch auch Suchalgorithmen, die sonst unvollstĂ€ndig sind, garantiert irgendwann eine Lösung finden werden. Es werden auch Szenarien betrachtet, die schon von sich aus Anpassung erfordern: In der Onlineumplanung muss ein Plan, der gerade ausgefĂŒhrt wird, auf Ănderungen in der Umgebung angepasst werden. Ăhnlich dazu muss sich ein Akteur in der Echtzeitsuche unter strengen Zeitauflagen und mit eingeschrĂ€nkten Informationen bewegen. Die Suchalgorithmen, die in dieser Arbeit eingefĂŒhrt werden, folgen einem gemeinsamen Muster von Onlineanpassung, was ihnen ermöglicht effektiv auf Herausforderungen zu reagieren die im Verlauf der Suche aufkommen. Diese AnsĂ€tze werden auf einer breiten Reihe von Benchmarks ausgewertet. Die Ergebnisse zeigen, dass die FlexibilitĂ€t dieser Algorithmen zu erhöhter ZuverlĂ€ssigkeit im Vergleich zu traditionellen AnsĂ€tzen fĂŒhrt, und es werden oft deutliche Verbesserungen gegenĂŒber modernen Planungssystemen erzielt.DFG grant 389792660 as part of TRR 248 â CPEC (see https://perspicuous-computing.science), and DFG grant HO 2169/5-1, "Critically Constrained Planning via Partial Delete Relaxation
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents
Reconfiguring Directed Trees in a Digraph
In this paper, we investigate the computational complexity of subgraph
reconfiguration problems in directed graphs. More specifically, we focus on the
problem of determining whether, given two directed trees in a digraph, there is
a (reconfiguration) sequence of directed trees such that for every pair of two
consecutive trees in the sequence, one of them is obtained from the other by
removing an arc and then adding another arc. We show that this problem can be
solved in polynomial time, whereas the problem is PSPACE-complete when we
restrict directed trees in a reconfiguration sequence to form directed paths.
We also show that there is a polynomial-time algorithm for finding a shortest
reconfiguration sequence between two directed spanning trees.Comment: 10 page
Goal reasoning for autonomous agents using automated planning
MenciĂłn Internacional en el tĂtulo de doctorAutomated planning deals with the task of finding a sequence of actions, namely
a plan, which achieves a goal from a given initial state. Most planning research
consider goals are provided by a external user, and agents just have to find a
plan to achieve them. However, there exist many real world domains where
agents should not only reason about their actions but also about their goals,
generating new ones or changing them according to the perceived environment.
In this thesis we aim at broadening the goal reasoning capabilities of planningbased
agents, both when acting in isolation and when operating in the same
environment as other agents.
In single-agent settings, we firstly explore a special type of planning tasks
where we aim at discovering states that fulfill certain cost-based requirements
with respect to a given set of goals. By computing these states, agents are able
to solve interesting tasks such as find escape plans that move agents in to safe
places, hide their true goal to a potential observer, or anticipate dynamically arriving
goals. We also show how learning the environmentâs dynamics may help
agents to solve some of these tasks. Experimental results show that these states
can be quickly found in practice, making agents able to solve new planning
tasks and helping them in solving some existing ones.
In multi-agent settings, we study the automated generation of goals based on
other agentsâ behavior. We focus on competitive scenarios, where we are interested
in computing counterplans that prevent opponents from achieving their
goals. We frame these tasks as counterplanning, providing theoretical properties
of the counterplans that solve them. We also show how agents can benefit
from computing some of the states we propose in the single-agent setting to
anticipate their opponentâs movements, thus increasing the odds of blocking
them. Experimental results show how counterplans can be found in different
environments ranging from competitive planning domains to real-time strategy
games.Programa de Doctorado en Ciencia y TecnologĂa InformĂĄtica por la Universidad Carlos III de MadridPresidenta: Eva OnaindĂa de la Rivaherrera.- Secretario: Ăngel GarcĂa Olaya.- Vocal: Mark Robert
- âŠ