19,305 research outputs found
Planning while Believing to Know
Over the last few years, the concept of Artificial Intelligence (AI) has become essential in our daily life and in several working scenarios. Among the various branches of AI, automated planning and the study of multi-agent systems are central research fields. This thesis focuses on a combination of these two areas: that is, a specialized kind of planning known as Multi-agent Epistemic Planning. This field of research is concentrated on all those scenarios where agents, reasoning in the space of knowledge/beliefs, try to find a plan to reach a desirable state from a starting one. This requires agents able to reason about her/his and others’ knowledge/beliefs and, therefore, capable of performing epistemic reasoning. Being aware of the information flows and the others’ states of mind is, in fact, a key aspect in several planning situations. That is why developing autonomous agents, that can reason considering the perspectives of their peers, is paramount to model a variety of real-world domains.
The objective of our work is to formalize an environment where a complete characterization of the agents’ knowledge/beliefs interactions and updates are possible. In particular, we achieved such a goal by defining a new action-based language for Multi-agent Epistemic Planning and implementing epistemic planners based on it. These solvers, flexible enough to reason about various domains and different nuances of knowledge/belief update, can provide a solid base for further research on epistemic reasoning or real-base applications.
This dissertation also proposes the design of a more general epistemic planning architecture. This architecture, following famous cognitive theories, tries to emulate some characteristics of the human decision-making process. In particular, we envisioned a system composed of several solving processes, each one with its own trade-off between efficiency and correctness, which are arbitrated by a meta-cognitive module
Agent-Based Models and Simulations in Economics and Social Sciences
Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through a simulation, section 2 gives the possibility to understand more precisely - and then to justify - the diversity of the epistemological positions presented in section 1. Our final claim is that careful attention to the multiplicity of the denotational powers of symbols at stake in complex models and computer simulations is necessary to determine, in each case, their proper epistemic status and credibility.Agent-Based Models and Simulations ; Epistemology ; Economics ; Social Sciences ; Conceptual Exploration ; Model World ; Credible World ; Experiment ; Denotational Hierarchy
Refinement Modal Logic
In this paper we present {\em refinement modal logic}. A refinement is like a
bisimulation, except that from the three relational requirements only `atoms'
and `back' need to be satisfied. Our logic contains a new operator 'all' in
addition to the standard modalities 'box' for each agent. The operator 'all'
acts as a quantifier over the set of all refinements of a given model. As a
variation on a bisimulation quantifier, this refinement operator or refinement
quantifier 'all' can be seen as quantifying over a variable not occurring in
the formula bound by it. The logic combines the simplicity of multi-agent modal
logic with some powers of monadic second-order quantification. We present a
sound and complete axiomatization of multi-agent refinement modal logic. We
also present an extension of the logic to the modal mu-calculus, and an
axiomatization for the single-agent version of this logic. Examples and
applications are also discussed: to software verification and design (the set
of agents can also be seen as a set of actions), and to dynamic epistemic
logic. We further give detailed results on the complexity of satisfiability,
and on succinctness
Design of a solver for multi-agent epistemic planning
As the interest in Artificial Intelligence continues to grow it is becoming
more and more important to investigate formalization and tools that allow us to
exploit logic to reason about the world. In particular, given the increasing
number of multi-agents systems that could benefit from techniques of automated
reasoning, exploring new ways to define not only the world's status but also
the agents' information is constantly growing in importance. This type of
reasoning, i.e., about agents' perception of the world and also about agents'
knowledge of her and others' knowledge, is referred to as epistemic reasoning.
In our work we will try to formalize this concept, expressed through
epistemic logic, for dynamic domains. In particular we will attempt to define a
new action-based language for multi-agent epistemic planning and to implement
an epistemic planner based on it. This solver should provide a tool flexible
enough to be able to reason on different domains, e.g., economy, security,
justice and politics, where reasoning about others' beliefs could lead to
winning strategies or help in changing a group of agents' view of the world.Comment: In Proceedings ICLP 2019, arXiv:1909.07646. arXiv admin note: text
overlap with arXiv:1511.01960 by other author
DELPHIC: Practical DEL Planning via Possibilities (Extended Version)
Dynamic Epistemic Logic (DEL) provides a framework for epistemic planning
that is capable of representing non-deterministic actions, partial
observability, higher-order knowledge and both factual and epistemic change.
The high expressivity of DEL challenges existing epistemic planners, which
typically can handle only restricted fragments of the whole framework. The goal
of this work is to push the envelop of practical DEL planning, ultimately
aiming for epistemic planners to be able to deal with the full range of
features offered by DEL. Towards this goal, we question the traditional
semantics of DEL, defined in terms on Kripke models. In particular, we propose
an equivalent semantics defined using, as main building block, so-called
possibilities: non well-founded objects representing both factual properties of
the world, and what agents consider to be possible. We call the resulting
framework DELPHIC. We argue that DELPHIC indeed provides a more compact
representation of epistemic states. To substantiate this claim, we implement
both approaches in ASP and we set up an experimental evaluation to compare
DELPHIC with the traditional, Kripke-based approach. The evaluation confirms
that DELPHIC outperforms the traditional approach in space and time
Characterizing perfect recall using next-step temporal operators in S5 and sub-S5 Epistemic Temporal Logic
We review the notion of perfect recall in the literature on interpreted
systems, game theory, and epistemic logic. In the context of Epistemic Temporal
Logic (ETL), we give a (to our knowledge) novel frame condition for perfect
recall, which is local and can straightforwardly be translated to a defining
formula in a language that only has next-step temporal operators. This frame
condition also gives rise to a complete axiomatization for S5 ETL frames with
perfect recall. We then consider how to extend and consolidate the notion of
perfect recall in sub-S5 settings, where the various notions discussed are no
longer equivalent
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