In systems such as group calendars or collaborative platforms, agents make group commitments to future actions that must adapt as new facts or constraints emerge. We develop a formal framework for revising such group intentions in systems where coalitions adopt shared, temporally extended intentions represented in a logic based on Alternating-Time Temporal Logic with strategy contexts. After formulating coherence criteria for systems of group intentions, we establish representation theorems in the style of Katsuno and Mendelzon, showing that revision operators satisfy rationality postulates precisely when they can be represented by preorders on strategy profiles. These results extend classical revision theory by covering non-total preorders and a logic of higher expressive power. Altogether, the framework lays the groundwork for principled revision of group intentions in systems where both coordination and change are essential.

Reward machines have recently been proposed as a means of encoding team tasks in cooperative multi-agent reinforcement learning. The resulting multi-agent reward machine is then decomposed into individual reward machines, one for each member of the team, allowing agents to learn in a decentralised manner while still achieving the team task. In this paper, we show how multi-agent reward machines for team tasks can be synthesised automatically from an abstraction of the environment in which the agents act and a high-level specification of the desired team behaviour expressed in a fragment of Alternating-time Temporal Logic. We present results from a number of benchmarks which suggest that our automated approach performs as well or better than reward machines in the literature.

Structural equation models (SEM) are a standard approach to representing causal dependencies between variables. In this paper we propose a new interpretation of existing formalisms in the field of Actual Causality in which SEM's are viewed as mechanisms transforming the dynamics of exogenous variables into the dynamics of endogenous variables. This allows us to combine counterfactual causal reasoning with existing temporal logic formalizms, and to introduce a temporal logic, CPLTL, for causal reasoning about such structures. Then, we demonstrate that the standard restriction to so-called recursive models (with no cycles in the dependency graphs) is not necessary in our approach. This fact provides us extra tools for reasoning about mutually dependent processes and feedback loops. Finally, we introduce the notions of model equivalence for temporal causal models and show that CPLTL has an efficient model-checking procedure.

There has been considerable work on reasoning about the strategic ability of agents under imperfect information. However, existing logics such as Probabilistic Strategy Logic are unable to express properties relating to information transparency. Information transparency concerns the extent to which agents' behaviours and actions are observable by other agents. Reasoning about information transparency is useful in many domains including security, privacy, and decision-making. In this paper, we present a formal framework for reasoning about information transparency properties in stochastic multi-agent systems. We extend Probabilistic Strategy Logic with new observability operators that capture the degree of observability of temporal properties by agents. We show that the model checking problem for the resulting logic is decidable.

The journal track of the 28th European Conference on Artificial Intelligence (ECAI-2025) offered the authors of papers recently accepted for publication by either one of the two leading discipline-wide journals in AI, Artificial Intelligence (AIJ) and the Journal of Artificial Intelligence Research (JAIR), the opportunity to present their work at the conference without undergoing an additional round of reviewing. Papers were eligible only if no part had previously been presented at a conference with archival proceedings. Traditionally, the authors of such papers would have missed out on the opportunity to present their work to a broader research audience. This limitation tends to discourage the submission of original work to journals without prior conference publications on the same topic. The intention of the journal track is to encourage a “journal-first” publication strategy–by giving authors the option to present their work at a suitable conference venue such as ECAI. On the following pages, for each paper presented at the journal track, we provide the DOI and the abstract of the original publication.

A key problem in the design of normative multi-agent systems is the cost of enforcing a norm (for the system operator) or complying with the norm (for the system users). If the cost is too high, ensuring compliant behavior may be uneconomic, or users may be deterred from participating in the MAS. In this paper, we consider the problem of synthesizing minimum cost dynamic norms to satisfy a system-level objective specified in Alternating Time Temporal Logic with Strategy Contexts (ATLsc∗). We show that synthesizing a dynamic norm under a bound on the cost of any prohibited set of actions has the same complexity as synthesizing arbitrary norms. We also show that synthesizing norms that minimize the average cost of the prohibited set of actions is unsolvable; however, synthesizing ε-optimal norms is possible.

Reward machines (RMs) are automata structures that encode (non-Markovian) reward functions for reinforcement learning (RL). RMs can reward any behaviour representable in regular languages and, when paired with RL algorithms that exploit RM structure, have been shown to significantly improve sample efficiency in many domains. In this work, we present pushdown reward machines (pdRMs), an extension of reward machines based on deterministic pushdown automata. pdRMs can recognise and reward temporally extended behaviours representable in deterministic context-free languages, making them more expressive than reward machines. We introduce two variants of pdRM-based policies, one which has access to the entire stack of the pdRM, and one which can only access the top k symbols (for a given constant k) of the stack. We propose a procedure to check when the two kinds of policies (for a given environment, pdRM, and constant k) achieve the same optimal state values. We then provide theoretical results establishing the expressive power of pdRMs, and space complexity results for the proposed learning problems. Lastly, we propose an approach for off-policy RL algorithms that exploits counterfactual experiences with pdRMs. We conclude by providing experimental results showing how agents can be trained to perform tasks representable in deterministic context-free languages using pdRMs.

Tracing and analysing the responsibility for unsafe outcomes of actors' decisions in multi-agent settings have been studied in recent years. These studies often focus on deterministic scenarios and assume that the unsafe outcomes for which actors can be held responsible are actually realized. This paper considers a broader notion of responsibility where unsafe outcomes are not necessarily realized, but their probabilities are unacceptably high. We present a logic combining strategic, probabilistic and temporal primitives designed to express concepts such as the risk of an undesirable outcome and being responsible for exceeding a risk threshold in one-shot games. We demonstrate that the proposed logic is (weakly) complete, decidable and has an efficient model-checking procedure. Finally, we define a probabilistic notion of responsibility and study its formal properties in the proposed logic setting.

We present Pure-Past Action Masking (PPAM), a lightweight approach to action masking for safe reinforcement learning. In PPAM, actions are disallowed (“masked”) according to specifications expressed in Pure-Past Linear Temporal Logic (PPLTL). PPAM can enforce non-Markovian constraints, i.e., constraints based on the history of the system, rather than just the current state of the (possibly hidden) MDP. The features used in the safety constraint need not be the same as those used by the learning agent, allowing a clear separation of concerns between the safety constraints and reward specifications of the (learning) agent. We prove formally that an agent trained with PPAM can learn any optimal policy that satisfies the safety constraints, and that they are as expressive as shields, another approach to enforce non-Markovian constraints in RL. Finally, we provide empirical results showing how PPAM can guarantee constraint satisfaction in practice.

Reward machines allow the definition of rewards for temporally extended tasks and behaviors. Specifying “informative” reward machines can be challenging. One way to address this is to generate reward machines from a high-level abstract description of the learning environment, using techniques such as AI planning. However, previous planning-based approaches generate a reward machine based on a single (sequential or partial-order) plan, and do not allow maximum flexibility to the learning agent. In this paper we propose a new approach to synthesising reward machines which is based on the set of partial order plans for a goal. We prove that learning using such “maximally permissive” reward machines results in higher rewards than learning using RMs based on a single plan. We present experimental results which support our theoretical claims by showing that our approach obtains higher rewards than the single-plan approach in practice.

There have been a number of attempts to develop a formal definition of causality that accords with our intuitions about what constitutes a cause. Perhaps the best known is the “modified” definition of actual causality, HPm, due to Halpern. In this paper, we argue that HPm gives counterintuitive results for some simple causal models. We propose Dynamic Causality (DC), an alternative semantics for causal models that leads to an alternative definition of causes. DC ascribes the same causes as HPm on the examples of causal models widely discussed in the literature and ascribes intuitive causes for the kinds of causal models we consider. Moreover, we show that the complexity of determining a cause under the DC definition is lower than for the HPm definition.

Reward machines have recently been proposed as a means of encoding team tasks in cooperative multi-agent reinforcement learning. The resulting multi-agent reward machine is then decomposed into individual reward machines, one for each member of the team, allowing agents to learn in a decentralised manner while still achieving the team task. In this paper, we show how multi-agent reward machines for team tasks can be synthesised automatically from an abstraction of the environment in which the agents act and a high-level specification of the desired team behaviour expressed in a fragment of Alternating-time Temporal Logic. We present results from a number of benchmarks which suggest that our automated approach performs as well or better than reward machines in the literature.

For an agent in a multi-agent environment, it is often beneficial to be able to predict what other agents will do next when deciding how to act. Previous work in multi-agent intention scheduling assumes a priori knowledge of the current goals of other agents. In this paper, we present a new approach to multi-agent intention scheduling in which an agent uses online goal recognition to identify the goals currently being pursued by other agents while acting in pursuit of its own goals. We show how online goal recognition can be incorporated into an MCTS-based intention scheduler, and evaluate our approach in a range of scenarios. The results demonstrate that our approach can rapidly recognise the goals of other agents even when they are pursuing multiple goals concurrently, and has similar performance to agents which know the goals of other agents a priori.

In multi-agent systems, norm enforcement is a mechanism for steering the behavior of individual agents in order to achieve desired system-level objectives. Due to the dynamics of multi-agent systems, however, it is hard to design norms that guarantee the achievement of the objectives in every operating context. Also, these objectives may change over time, thereby making previously defined norms ineffective. In this paper, we investigate the use of system execution data to automatically synthesise and revise conditional prohibitions with deadlines, a type of norms aimed at preventing agents from exhibiting certain patterns of behaviors. We propose DDNR (Data-Driven Norm Revision), a data-driven approach to norm revision that synthesises revised norms with respect to a data set of traces describing the behavior of the agents in the system. We evaluate DDNR using a state-of-the-art, off-the-shelf urban traffic simulator. The results show that DDNR synthesises revised norms that are significantly more accurate than the original norms in distinguishing adequate and inadequate behaviors for the achievement of the system-level objectives.

To build a theory of intention revision for agents operating in stochastic environments, we need a logic in which we can explicitly reason about their decision-making policies and those policies' uncertain outcomes. Towards this end, we propose PLBP, a novel probabilistic temporal logic for Markov Decision Processes that allows us to reason about policies of bounded size. The logic is designed so that its expressive power is sufficient for the intended applications, whilst at the same time possessing strong computational properties. We prove that the satisfiability problem for our logic is decidable, and that its model checking problem is PSPACE-complete. This allows us to e.g. algorithmically verify whether an agent's intentions are coherent, or whether a specific policy satisfies safety and/or liveness properties.

The need for tools and techniques to formally analyze and trace the responsibility for unsafe outcomes to decision-making actors is urgent. Existing formal approaches assume that the unsafe outcomes for which actors can be held responsible are actually realized. This paper considers a broader notion of responsibility where unsafe outcomes are not necessarily realized, but their probabilities are unacceptably high. We present a logic combining strategic, probabilistic and temporal primitives designed to express concepts such as the risk of an undesirable outcome and being responsible for exceeding a risk threshold. We demonstrate that the proposed logic is complete and decidable.

We propose a logic of east and west (LEW ) for points in 1D Euclidean space. It formalises primitive direction relations: east (E), west (W) and indeterminate east/west (Iew). It has a parameter τ ∈ N>1, which is referred to as the level of indeterminacy in directions. For every τ ∈ N>1, we provide a sound and complete axiomatisation of LEW , and prove that its satisfiability problem is NP-complete. In addition, we show that the finite axiomatisability of LEW depends on τ : if τ = 2 or τ = 3, then there exists a finite sound and complete axiomatisation; if τ > 3, then the logic is not finitely axiomatisable. LEW can be easily extended to higher-dimensional Euclidean spaces. Extending LEW to 2D Euclidean space makes it suitable for reasoning about not perfectly aligned representations of the same spatial objects in different datasets, for example, in crowd-sourced digital maps.

Abstract Group announcement logic (GAL) and coalition announcement logic (CAL) allow us to reason about whether it is possible for groups and coalitions of agents to achieve their desired epistemic goals through truthful public communication. The difference between groups and coalitions in such a context is that the latter make their announcements in the presence of possible adversarial counter-announcements. As epistemic goals may involve some agents remaining ignorant, counter-announcements may preclude coalitions from reaching their goals. We study the relative expressivity of GAL and CAL and provide some results involving their more well-known sibling APAL. We also discuss how the presence of memory alters the relationship between groups and coalition.

Recent work in multi-agent intention scheduling has shown that enabling agents to predict the actions of other agents when choosing their own actions can be beneficial. However existing approaches to 'intention-aware' scheduling assume that the programs of other agents are known, or are "similar" to that of the agent making the prediction. While this assumption is reasonable in some circumstances, it is less plausible when the agents are not co-designed. In this paper, we present a new approach to multi-agent intention scheduling in which agents predict the actions of other agents based on a high-level specification of the tasks performed by an agent in the form of a reward machine (RM) rather than on its (assumed) program. We show how a reward machine can be used to generate tree and rollout policies for an MCTS-based scheduler. We evaluate our approach in a range of multi-agent environments, and show that RM-based scheduling out-performs previous intention-aware scheduling approaches in settings where agents are not co-designed

In multi-agent systems, norm enforcement is a mechanism for steering the behavior of individual agents in order to achieve desired system-level objectives. Due to the dynamics of multi-agent systems, however, it is hard to design norms that guarantee the achievement of the objectives in every operating context. Also, these objectives may change over time, thereby making previously defined norms ineffective. In this paper, we investigate the use of system execution data to automatically synthesise and revise conditional prohibitions with deadlines, a type of norms aimed at prohibiting agents from exhibiting certain patterns of behaviors. We propose DDNR (Data-Driven Norm Revision), a data-driven approach to norm revision that synthesises revised norms with respect to a data set of traces describing the behavior of the agents in the system. We evaluate DDNR using a state-of-the-art, off-the-shelf urban traffic simulator. The results show that DDNR synthesises revised norms that are significantly more accurate than the original norms in distinguishing adequate and inadequate behaviors for the achievement of the system-level objectives.

It is often advantageous to be able to extract resource requirements in resource logics of strategic ability, rather than to verify whether a fixed resource requirement is sufficient for achieving a goal. We study Parameterised Resource-Bounded Alternating Time Temporal Logic where parameter extraction is possible. We give a parameter extraction algorithm and prove that the model-checking problem is 2EXPTIME-complete.

A key problem in Belief-Desire-Intention agents is how an agent progresses its intentions, i.e., which plans should be selected and how the execution of these plans should be interleaved so as to achieve the agent’s goals. Previous approaches to the intention progression problem assume the agent has perfect information about the state of the environment. However, in many real-world applications, an agent may be uncertain about whether an environment condition holds, and hence whether a particular plan is applicable or an action is executable. In this paper, we propose SAU, a Monte-Carlo Tree Search (MCTS)-based scheduler for intention progression problems where the agent’s beliefs are uncertain. We evaluate the performance of our approach experimentally by varying the degree of uncertainty in the agent’s beliefs. The results suggest that SAU is able to successfully achieve the agent’s goals even in settings where there is significant uncertainty in the agent’s beliefs.

We propose a logic of directions for points (LD) over 2D Euclidean space, which formalises primary direction relations east (E), west (W), and indeterminate east/west (Iew), north (N), south (S) and indeterminate north/south (Ins). We provide a sound and complete axiomatisation of it, and prove that its satisfiability problem is NP-complete.

There has recently been increasing interest in using reactive synthesis techniques to automate the production of manufacturing process plans. Previous work has assumed that the set of manufacturing resources is known and fixed in advance. In this paper, we consider the more general problem of whether a controller can be synthesized given sufficient resources. In the unbounded setting, only the types of available manufacturing resources are given, and we want to know whether it is possible to manufacture a product using only resources of those type(s), and, if so, how many resources of each type are needed. We model manufacturing processes and facilities as transducers (automata with output), and show that the unbounded orchestration problem is decidable and the (Pareto) optimal set of resources necessary to manufacture a product is computable for uni-transducers. However, for multitransducers, the problem is undecidable.

Abstract Coalition logic is currently one of the most popular logics for multi-agent systems. While logics combining coalitional and epistemic operators have received considerable attention, completeness results for epistemic extensions of coalition logic have so far been missing. In this paper we provide several such results and proofs. We prove completeness for epistemic coalition logic with common knowledge, with distributed knowledge, and with both common and distributed knowledge, respectively. Furthermore, we completely characterise the complexity of the satisfiability problem for each of the three logics. We also study logics with interaction axioms connecting coalitional ability and knowledge.

In this paper, we model manufacturing processes and facilities as transducers (automata with output). The problem of whether a given manufacturing process can be realized by a given set of manufacturing resources can then be stated as an orchestration problem for transducers. We first consider the conceptually simpler case of uni-transducers (transducers with a single input and a single output port), and show that synthesizing orchestrations for uni-transducers is EXPTIME-complete. Surprisingly, the complexity remains the same for the more expressive multi-transducer case, where transducers have multiple input and output ports and the orchestration is in charge of dynamically connecting ports during execution.

We consider the problem of detecting norm violations in open multi-agent systems (MAS). In this extended abstract, we outline the approach of [Alechina et al., 2018], and show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations.

We consider the problem of detecting norm violations in open multi-agent systems (MAS). We show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations. The cost of providing the incentives is not borne by the MAS and does not come from fines charged for norm violations (fines may be impossible to levy in a system where agents are free to leave and rejoin again under a different identity). Instead, monitoring incentives come from (scrip) fees for accessing the services provided by the MAS. In some cases, perfect monitoring (and hence enforcement) can be achieved: no norms will be violated in equilibrium. In other cases, we show that, while it is impossible to achieve perfect enforcement, we can get arbitrarily close; we can make the probability of a norm violation in equilibrium arbitrarily small. We show using simulations that our theoretical results, which apply to systems with a large number of agents, hold for multi-agent systems with as few as 1000 agents–the system rapidly converges to the steady-state distribution of scrip tokens necessary to ensure monitoring and then remains close to the steady state.

In this article, we study a minimal change approach to preference dynamics. We treat a set of preferences as a special kind of theory, and define minimal change preference contraction and revision operations in the spirit of the Alchourrón, Gärdenfors, and Makinson theory of belief revision. We characterise minimal contraction of preference sets by a set of postulates and prove a representation theorem. We also give a linear time algorithm which implements minimal contraction by a single preference. We then define minimal contraction by a set of preferences, and show that the problem of a minimal contraction by a set of preferences is NP-hard.

Many problems in AI and multi-agent systems research are most naturally formulated in terms of the abilities of a coalition of agents. There exist several excellent logical tools for reasoning about coalitional ability. However, coalitional ability can be affected by the availability of resources, and there is no straightforward way of reasoning about resource requirements in logics such as Coalition Logic (CL) and Alternating-time Temporal Logic (ATL). In this article, we describe a logic for reasoning about coalitional ability under resource constraints. We extend ATL with costs of actions and hence of strategies. We give a complete and sound axiomatization of the resulting logic, Resource-Bounded ATL (RB-ATL) and a model-checking algorithm for it.

The series of workshops on Intuitionistic Modal Logic and Applications (IMLA) owes its existence to the hope that philosophers, mathematical logicians and computer scientists would share information and tools when investigating intuitionistic modal logics and modal type theories, if they knew of each other's work. More than 10 years have passed since the retrospective view of de Paiva et al. [ 10 ], and progress in the area of constructive modal logic has been slow and getting slower. It is our view that differences in the outlook of the various groups of scholars interested in the topic, differences that were once fruitful, now are responsible for a tendency for the new work to be driven by technical issues that have not had wide interest, leading to compartmentalization and waning interest in the IMLA big tent. Work on modal type theories seems to have been pursued in narrow tracts. For instance, much work in the symposium on Principles of Programming Languages (POPL), in specific type systems could be considered work in applied constructive modal logic, but it is not considered so, as this perspective is not considered useful or productive. Generally speaking, topic specialists have stopped expecting outsiders to say anything of interest to them, so they do not make the effort to say anything of interest to outsiders.

International audience

We present an approach to incentivising monitoring for norm violations in open multi-agent systems such as Wikipedia. In such systems, there is no crisp definition of a norm violation; rather, it is a matter of judgement whether an agent's behaviour conforms to generally accepted standards of behaviour. Agents may legitimately disagree about borderline cases. Using ideas from scrip systems and peer prediction, we show how to design a mechanism that incentivises agents to monitor each other's behaviour for norm violations. The mechanism keeps the probability of undetected violations (submissions that the majority of the community would consider not conforming to standards) low, and is robust against collusion by the monitoring agents.

Alternating Time Temporal Logic (ATL) is widely used for the verification of multi-agent systems. We consider Resource Agent Logic ( RAL ), which extends ATL to allow the verification of properties of systems where agents act under resource constraints. The model checking problem for RAL with unbounded production and consumption of resources is known to be undecidable. We review existing (un)decidability results for fragments of RAL , tighten some existing undecidability results, and identify several aspects which affect decidability of model checking. One of these aspects is the availability of a ‘do nothing’, or idle action, which does not produce or consume resources. Analysis of undecidability results allows us to identify a significant new fragment of RAL for which model checking is decidable.

Several logics for expressing coalitional ability under resource bounds have been proposed and studied in the literature. Previous work has shown that if only consumption of resources is considered or the total amount of resources produced or consumed on any path in the system is bounded, then the model-checking problem for several standard logics, such as Resource-Bounded Coalition Logic (RB-CL) and Resource-Bounded Alternating-Time Temporal Logic (RB-ATL) is decidable. However, for coalition logics with unbounded resource production and consumption, only some undecidability results are known. In this paper, we show that the model-checking problem for RB-ATL with unbounded production and consumption of resources is decidable but EXPSPACE-hard. We also investigate some tractable cases and provide a detailed comparison to a variant of the resource logic RAL, together with new complexity results.

Abstract We consider the problem of decomposing a group norm into a set of individual obligations for the agents comprising the group, such that if the individual obligations are fulfilled, the group obligation is fulfilled. Such an assignment of tasks to agents is often subject to additional social or organizational norms that specify permissible ways in which tasks can be assigned. An important role of social norms is that they can be used to impose ‘fairness constraints’, which seek to distribute individual responsibility for discharging the group norm in a ‘fair’ or ‘equitable’ way. We propose a simple language for this kind of fairness constraints and analyse the problem of computing a fair decomposition of a group obligation, both for non-repeating and for repeating group obligations.

This paper describes a series of new qualitative spatial logics for checking consistency of sameAs and partOf matches between spatial objects from different geospatial datasets, especially from crowd-sourced datasets. Since geometries in crowd-sourced data are usually not very accurate or precise, we buffer geometries by a margin of error or a level of tolerance, and define spatial relations for buffered geometries. The spatial logics formalize the notions of `buffered equal' (intuitively corresponding to `possibly sameAs'), `buffered part of' (`possibly partOf'), `near' (`possibly connected') and `far' (`definitely disconnected'). A sound and complete axiomatisation of each logic is provided with respect to models based on metric spaces. For each of the logics, the satisfiability problem is shown to be NP-complete. Finally, we briefly describe how the logics are used in a system for generating and debugging matches between spatial objects, and report positive experimental evaluation results for the system.

We describe a tool, MatchMaps, that generates sameAs and partOf matches between spatial objects (such as shops, shopping centres, etc.) in crowd-sourced and authoritative geospatial datasets. MatchMaps uses reasoning in qualitative spatial logic, description logic and truth maintenance techniques, to produce a consistent set of matches. We report the results of an initial evaluation of MatchMaps by experts from Ordnance Survey (Great Britain’s National Mapping Authority). In both the case studies considered, MatchMaps was able to correctly match spatial objects (high precision and recall) with minimal human intervention.

Although many researchers have studied the user behavior of using text-based information search engine, less is known about search pattern for mobile content-based image search. We developed a Query-by-Drawing (QbD) mobile application, and conducted a user study on it to explore the search behavior of painting search by drawing on the touchscreen phone. Based on the resulting drawings and video-logs of drawing procedures on three task conditions, we analyzed the patterns of query formulation and query modification. We further examined the effects of user characteristic and task type on the search strategy when using our mobile application. The results elicited some guidelines for mobile QbD image search interface design and informed the potential improvements of our application.

Several logics for expressing coalitional ability under resource bounds have been proposed and studied in the literature. Previous work has shown that if only consumption of resources is considered or the total amount of resources produced or consumed on any path in the system is bounded, then the model-checking problem for several standard logics, such as Resource-Bounded Coalition Logic (RB-CL) and Resource-Bounded Alternating-Time Temporal Logic (RB-ATL) is decidable. However, for coalition logics with unbounded resource production and consumption, only some undecidability results are known. In this paper, we show that the model-checking problem for RB-ATL with unbounded production and consumption of resources is decidable.

We propose a new qualitative spatial logic for reasoning about part-whole relations between geometries (sets of points) represented in different geospatial datasets, in particular crowd-sourced datasets. Since geometries in crowd-sourced data can be less inaccurate or precise, we buffer geometries by a margin of error or level of tolerance σ, and define part-whole relation for buffered geometries. The relations between geometries considered in the logic are: buffered part of (BPT), Near and Far. We provide a sound and complete axiomatisation of the logic with respect to metric models, and show that its satisfiability problem is NP-complete.

For the case of people desire to find the previously-seen painting but only have vague memory of the painting, we designed and built a mobile phone application to enable people to search for paintings by drawing rough color sketches. Three-phase memory studies -- with 15-minute delay, 1-week delay and 1-month delay -were conducted to explore if people could draw from their visual memory and the resulting drawing were useful for search. Seventeen participants were involved in three memory studies. The experiment results implied that most of participants could draw usable rough color sketches from their memory as painting queries even one month after viewing. Our research also demonstrated that users could improve their performance if they got more familiar with the functions and usage of our application.

In many logic-based BDI agent programming languages, plan selection involves inferencing over some underlying knowledge representation. While context-sensitive plan selection facilitates the development of flexible, declarative programs, the overhead of evaluating repeated queries to the agent's beliefs and goals can result in poor run time performance. In this paper we present an approach to multi-cycle query caching for logic-based BDI agent programming languages. We extend the abstract performance model presented in (Alechina et al. 2012) to quantify the costs and benefits of caching query results over multiple deliberation cycles. We also present results of experiments with prototype implementations of both single- and multi-cycle caching in three logic-based BDI agent platforms, which demonstrate that significant performance improvements are achievable in practice.

This talk is on reasoning about agent programs written in Belief, Desire and Intention (BDI) agent programming languages. BDI programming languages (for example, [1], [2], [3]) have high-level programming primitives which correspond to the beliefs, goals and plans of an AI agent. A program contains a set of rules which allow the agent to adopt plans given its current beliefs and goals. Plans are essentially imperative programs. For example, an agent may have a rule which says that if it believes that it is currently located in room 1 and its goal is to be in room 2, then a suitable plan to adopt would be to exit room 1, turn right, move forward for 3 meters, turn right, and enter room 2.

We present a family of sound and complete logics for reasoning about deliberation strategies for SimpleAPL programs. SimpleAPL is a fragment of the agent programming language 3APL designed for the implementation of cognitive agents with beliefs, goals and plans. The logics are variants of PDL, and allow us to prove safety and liveness properties of SimpleAPL agent programs under different deliberation strategies. We show how to axiomatise different deliberation strategies for SimpleAPL programs, and, for each strategy we prove a correspondence between the operational semantics of SimpleAPL and the models of the corresponding logic. We illustrate the utility of our approach with an example in which we show how to verify correctness properties for a simple agent program under different deliberation strategies.

Recent work on Alternating-Time Temporal Logic and Coalition Logic has allowed the expression of many interesting properties of coalitions and strategies. However, there is no natural way of expressing resource requirements in these logics. In this article, we present a Resource-Bounded Coalition Logic (RBCL) that has explicit representation of resource bounds in the language. We give a complete and sound axiomatization of RBCL, a procedure for deciding satisfiability of RBCL formulas, and a model-checking algorithm.

In the most popular logics combining knowledge and awareness, it is not possible to express statements about knowledge of unawareness such as "Ann knows that Bill is aware of something Ann is not aware of" - without using a stronger statement such as "Ann knows that Bill is aware of p and Ann is not aware of p", for some particular p. Recently, however, Halpern and Rêgo (2006) introduced a logic in which such statements about knowledge of unawareness can be expressed. The logic extends the traditional framework with quantification over formulae, and is thus very expressive. As a consequence, it is not decidable. In this paper we introduce a decidable logic which can be used to reason about certain types of unawareness. The logic extends the traditional framework with an operator expressing full awareness, i.e., the fact that an agent is aware of everything, and another operator expressing relative awareness, the fact that one agent is aware of everything another agent is aware of The logic is less expressive than Halpern's and Rêgo's logic. It is, however, expressive enough to express all of Halpern's and Rêgo's motivating examples. In addition to proving that the logic is decidable and that its satisfiability problem is PSPACE-complete, we present an axiomatisation which we show is sound and complete.

The syntactic approach to epistemic logic avoids the logical omniscience problem by taking knowledge as primary rather than as defined in terms of possible worlds. In this study, we combine the syntactic approach with modal logic, using transition systems to model reasoning. We use two syntactic epistemic modalities: ‘knowing at least’ a set of formulae and ‘knowing at most’ a set of formulae. We are particularly interested in models restricting the set of formulae known by an agent at a point in time to be finite. The resulting systems are investigated from the point of view of axiomatization and complexity. We show how these logics can be used to formalise non-omniscient agents who know some inference rules, and study their relationship to other systems of syntactic epistemic logics, such as Ågotnes and Walicki (2004, Proc. 2nd EUMAS, pp. 1–10), Alechina et al. (2004, Proc. 3rd AAMAS, pp. 601–613), Duc (1997, J. Logic Comput., 7, 633–648).

International audience

Abstract We show how sequent calculi for some generalized quantifiers can be obtained by generalizing the Herbrand approach to ordinary first order proof theory. Typical of the Herbrand approach, as compared to plain sequent calculus, is increased control over relations of dependence between variables. In the case of generalized quantifiers, explicit attention to relations of dependence becomes indispensible for setting up proof systems. It is shown that this can be done by turning variables into structured objects, governed by various types of structural rules. These structured variables are interpreted semantically by means of a dependence relation. This relation is an analogue of the accessibility relation in modal logic. We then isolate a class of axioms for generalized quantifiers which correspond to first-order conditions on the dependence relation.