Tunnel vision and confirmation bias can lead to miscarriages of justice. A way to avoid tunnel vision is to consider your evidence in light of more than one scenario. Alternative scenarios allow us to consider how probable each scenario is, compared to the other considered scenarios. Bayesian Networks have been proposed as a formal method for reasoning about the probability of scenarios. Specifically, alternative scenarios were modelled using Bayesian networks with a constraint node, which ensures mutual exclusivity. However, the performance of these methods in situations where not all possible alternative scenarios are modeled, the non-exhaustive setting, has not been investigated. Since it is impossible to explicitly cover everything that could possibly have happened in a model, it is important to know how these methods handle non-exhaustiveness. We evaluate four methods using an agent-based model that simulates an environment in which a crime could occur. Taking this as the ground truth, we compare different Bayesian network modeling methods on five aspects related to the quality of the representation of the ground truth as well as computational performance. We find that some methods result in disparities between the ground truth and the predicted posterior probabilities for the scenarios in a non-exhaustive setting. In an exhaustive setting, the proposed methods perform well. The construction approach that models scenarios in terms of conjunctions of events performs well in both settings.

Theory of mind refers to the human ability to reason about the mental content of other people, such as their beliefs, desires, and goals. People use their theory of mind to understand, reason about, and explain the behaviour of others. Having a theory of mind is especially useful when people collaborate, since individuals can then reason on what the other individual knows as well as what reasoning they might do. Similarly, hybrid intelligence systems, where AI agents collaborate with humans, necessitate that the agents reason about the humans using computational theory of mind. However, to try to keep track of all individual mental attitudes of all other individuals becomes (computationally) very difficult. Accordingly, this paper provides a mechanism for computational theory of mind based on abstractions of single beliefs into higher-level concepts. These abstractions can be triggered by social norms and roles. Their use in decision making serves as a heuristic to choose among interactions, thus facilitating collaboration. We provide a formalization based on epistemic logic to explain how various inferences enable such a computational theory of mind. Using examples from the medical domain, we demonstrate how having such a theory of mind enables an agent to interact with humans effectively and can increase the quality of the decisions humans make.

Scenario-based Bayesian networks (BNs) have been proposed as a tool for the rational handling of evidence. The proper evaluation of existing methods requires access to a ground truth that can be used to test the quality and usefulness of a BN model of a crime. However, that would require a full probability distribution over all relevant variables used in the model, which is in practice not available. In this paper, we use an agent-based simulation as a proxy for the ground truth for the evaluation of BN models as tools for the rational handling of evidence. We use fictional crime scenarios as a background. First, we design manually constructed BNs using existing design methods in order to model example crime scenarios. Second, we build an agent-based simulation covering the scenarios of criminal and non-criminal behavior. Third, we algorithmically determine BNs using statistics collected experimentally from the agent-based simulation that represents the ground truth. Finally, we compare the manual, scenario-based BNs to the algorithmic BNs by comparing the posterior probability distribution over outcomes of the network to the ground-truth frequency distribution over those outcomes in the simulation, across all evidence valuations. We find that both manual BNs and algorithmic BNs perform similarly well: they are good reflections of the ground truth in most of the evidence valuations. Using ABMs as a ground truth can be a tool to investigate Bayesian Networks and their design methods, especially under circumstances that are implausible in real-life criminal cases, such as full probabilistic information.

Local-HDP (Local Hierarchical Dirichlet Process) is a hierarchical Bayesian method recently used for open-ended 3D object category recognition. It has been proven to be efficient in real-time robotic applications. However, the method is not robust to a high degree of occlusion. We address this limitation in two steps. First, we propose a novel semantic 3D object-parts segmentation method that has the flexibility of Local-HDP. This method is shown to be suitable for open-ended scenarios where the number of 3D objects or object parts are not fixed and can grow over time. We show that the proposed method has a higher percentage of mean intersection over union, using a smaller number of learning instances. Second, we integrate this technique with a recently introduced argumentation-based online incremental learning method, enabling the model to handle a high degree of occlusion. We show that the resulting model produces explicit explanations for the 3D object category recognition task.

One way of reasoning with uncertainties in the context of law is to use probabilities. However, methods for reasoning about the probability of guilt in a court case requires us to specify a prior probability of guilt, which is the probability of guilt before any evidence is known. There is no accepted approach for specifying the prior probability of guilt but multiple solutions have been proposed. In this paper, we consider three approaches: a prior that is based on the population, a prior based on the number of agents that have similar opportunity as the suspect and a prior that represents a legal norm. For comparing and evaluating the approaches, we use an agent-based model as a ground truth in which all probabilities are known. With the data generated in the ground truth model, we investigate how the choice of prior influences the posterior probability of guilt for both guilty and innocent agents. Using a decision threshold, we can determine the effect of the three approaches on the rates of correct and incorrect convictions and acquittals. We find that the opportunity prior results in higher rates of both correct convictions and false convictions and requires more assumptions and access to data and knowledge than the legal prior and population prior.

Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria used to settle the acceptance of arguments are called semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. Recently, the notion of strong admissibility has been introduced for ADFs. In the current work we study the computational complexity of the following reasoning tasks under strong admissibility semantics. We address 1. the credulous/skeptical decision problem; 2. the verification problem; 3. the strong justification problem; and 4. the problem of finding a smallest witness of strong justification of a queried argument.

Abstract In social interactions, people often reason about the beliefs, goals and intentions of others. Thistheory of mindallows them to interpret the behavior of others, and predict how they will behave in the future. People can also use this ability recursively: they usehigher-order theory of mindto reason about the theory of mind abilities of others, as in “he thinks that I don’t know that he sent me an anonymous letter”. Previous agent-based modeling research has shown that the usefulness of higher-order theory of mind reasoning can be useful across competitive, cooperative, and mixed-motive settings. In this paper, we cast a new light on these results by investigating how the predictability of the environment influences the effectiveness of higher-order theory of mind. Our results show that the benefit of (higher-order) theory of mind reasoning is strongly dependent on the predictability of the environment. We consider agent-based simulations in repeated one-shot negotiations in a particular negotiation setting known as Colored Trails. When this environment is highly predictable, agents obtain little benefit from theory of mind reasoning. However, if the environment has more observable features that change over time, agents without the ability to use theory of mind experience more difficulties predicting the behavior of others accurately. This in turn allows theory of mind agents to obtain higher scores in these more dynamic environments. These results suggest that the human-specific ability for higher-order theory of mind reasoning may have evolved to allow us to survive in more complex and unpredictable environments.

Abstract Recently, several logics modelling evidence have been proposed in the literature. These logics often also feature beliefs. We call the process or function that maps evidence to beliefs consolidation. In this paper, we use a four-valued modal logic of evidence as a basis. In the models for this logic, agents are represented by nodes, peer connections by edges and the private evidence that each agent has by a four-valued valuation. From this basis, we propose methods of consolidating the beliefs of the agents, taking into account both their private evidence as well as their peers’ opinions. To this end, beliefs are computed iteratively. The final consolidated beliefs are the ones in the point of stabilization of the model. However, it turns out that some consolidation policies will not stabilize for certain models. Finding the conditions for stabilization is one of the main problems studied here, along with other properties of such consolidations. Our main contributions are twofold: we offer a new dynamic perspective on the process of forming evidence-based beliefs, in the context of evidence logics, and we set up and address some mathematically challenging problems, which are related to graph theory and practical subject areas such as belief/opinion diffusion and contagion in multi-agent networks.

The environment around general-purpose service robots has a dynamic nature. Accordingly, even the robot’s programmer cannot predict all the possible external failures which the robot may confront. This research proposes an online incremental learning method that can be further used to autonomously handle external failures originating from a change in the environment. Existing research typically offers special-purpose solutions. Furthermore, the current incremental online learning algorithms cannot generalize well with just a few observations. In contrast, our method extracts a set of hypotheses, which can then be used for finding the best recovery behavior at each failure state. The proposed argumentation-based online incremental learning approach uses an abstract and bipolar argumentation framework to extract the most relevant hypotheses and model the defeasibility relation between them. This leads to a novel online incremental learning approach that overcomes the addressed problems and can be used in different domains including robotic applications. We have compared our proposed approach with state-of-the-art online incremental learning approaches, an approximation-based reinforcement learning method, and several online contextual bandit algorithms. The experimental results show that our approach learns more quickly with a lower number of observations and also has higher final precision than the other methods. Note to Practitioners—This work proposes an online incremental learning method that learns faster by using a lower number of failure states than other state-of-the-art approaches. The resulting technique also has higher final learning precision than other methods. Argumentation-based online incremental learning generates an explainable set of rules which can be further used for human-robot interaction. Moreover, testing the proposed method using a publicly available dataset suggests wider applicability of the proposed incremental learning method outside the robotics field wherever an online incremental learner is required. The limitation of the proposed method is that it aims for handling discrete feature values.

Human agents can acquire knowledge and learn through argumentation. Inspired by this fact, we propose a novel argumentation-based machine learning technique that can be used for online incremental learning scenarios. Existing methods for online incremental learning problems typically do not generalize well from just a few learning instances. Our previous argumentation-based online incremental learning method outperformed state-of-the-art methods in terms of accuracy and learning speed. However, it was neither memory-efficient nor computationally efficient since the algorithm used the power set of the feature values for updating the model. In this paper, we propose an accelerated version of the algorithm, with polynomial instead of exponential complexity, while achieving higher learning accuracy. The proposed method is at least 200 times faster than the original argumentation-based learning method and is more memory-efficient.

Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria that have been used to settle the acceptance of arguments are called semantics. However, the notion of semi-stable semantics as studied for abstract argumentation frameworks has received little attention for ADFs. In the current work, we present the concepts of semi-two-valued models and semi-stable models for ADFs. We show that these two notions satisfy a set of plausible properties required for semi-stable semantics of ADFs. Moreover, we show that semi-two-valued and semi-stable semantics of ADFs form a proper generalization of the semi-stable semantics of AFs, just like two-valued model and stable semantics for ADFs are generalizations of stable semantics for AFs.

It has been shown in the late 1960s that each formula of first-order logic without constants and function symbols obeys a zero-one law: As the number of elements of finite models increases, every formula holds either in almost all or in almost no models of that size. Therefore, many properties of models, such as having an even number of elements, cannot be expressed in the language of first-order logic. For modal logics, limit behavior for models and frames may differ. Halpern and Kapron proved zero-one laws for classes of models corresponding to the modal logics K, T, S4, and S5. They also proposed zero-one laws for the corresponding classes of frames, but their zero-one law for K-frames has since been disproved.In this paper, we prove zero-one laws for provability logic with respect to both model and frame validity. Moreover, we axiomatize validity in almost all irreflexive transitive finite models and in almost all irreflexive transitive finite frames, leading to two different axiom systems. In the proofs, we use a combinatorial result by Kleitman and Rothschild about the structure of almost all finite partial orders. On the way, we also show that a previous result by Halpern and Kapron about the axiomatization of almost sure frame validity for S4 is not correct. Finally, we consider the complexity of deciding whether a given formula is almost surely valid in the relevant finite models and frames.

Abstract dialectical frameworks (ADFs) have been introduced as formalism for the modeling and evaluating argumentation. However, the role of discussion in evaluating of arguments in ADFs has not been clarified well so far. We focus on the grounded semantics of ADFs and provide the grounded discussion game. We show that an argument is acceptable (deniable) in the grounded interpretation of an ADF without any redundant links if and only if the proponent of a claim has a winning strategy in the grounded discussion game.

We study wireless ad-hoc networks from an agent-based perspective. In our model agents with different strategies such as being selfish, tit-for-tat or battery-based compete and cooperate. If only different levels of selfishness are allowed then being selfish is clearly the dominant strategy. However, introduction of more advanced strategies allows to some extent to combat selfishness. In particular we present a battery-based approach and a hybrid of battery-based and tit-for-tat approaches. The findings give hope that the introduction of widely available ad-hoc networks might at some point be possible. Even when users are given full control of their devices, effective strategies allow for the networks overall to be effective and feasible.

Theory of mind refers to the ability to reason explicitly about unobservable mental content of others, such as beliefs, goals, and intentions. People often use this ability to understand the behavior of others as well as to predict future behavior. People even take this ability a step further, and use higher-order theory of mind by reasoning about the way others make use of theory of mind and in turn attribute mental states to different agents. One of the possible explanations for the emergence of the cognitively demanding ability of higher-order theory of mind suggests that it is needed to deal with mixed-motive situations. Such mixed-motive situations involve partially overlapping goals, so that both cooperation and competition play a role. In this paper, we consider a particular mixed-motive situation known as Colored Trails, in which computational agents negotiate using alternating offers with incomplete information about the preferences of their trading partner. In this setting, we determine to what extent higher-order theory of mind is beneficial to computational agents. Our results show limited effectiveness of first-order theory of mind, while second-order theory of mind turns out to benefit agents greatly by allowing them to reason about the way they can communicate their interests. Additionally, we let human participants negotiate with computational agents of different orders of theory of mind. These experiments show that people spontaneously make use of second-order theory of mind in negotiations when their trading partner is capable of second-order theory of mind as well.

This paper discusses an implementation of four speech acts: assert, concede, request and challenge in a paraconsistent framework. A natural four-valued model of interaction yields multiple new cognitive situations. They are analyzed in the context of communicative relations, which partially replace the concept of trust. These assumptions naturally lead to six types of situations, which often require performing conflict resolution and belief revision.

When agents know a protocol, this leads them to have expectations about future observations. Agents can update their knowledge by matching their actual observations with the expected ones. They eliminate states where they do not match. In this paper, we study how agents perceive protocols that are not commonly known, and propose a logic to reason about knowledge in such scenarios.

Publication impact factors are more important now than 10 or 20 years ago, both for individual researchers and for journals. Citation indices such as Thomson Reuters (formerly ISI) Web of Knowledge, (http://wokinfo.com/) or Publish or Perish (www.harzing.com, based on Google Scholar scholar.google.com) are standardly consulted by job selection committees prior to interviewing candidates. 1 Individuals and journals post their h-indices online, and they compare their h-indices with those of their peers. (The h-index of an individual is the largest number n such that n of its publications are all cited at least n times. The definition also applies to research institutes, journals, etc.) We think it is important to be aware of these developments. It is particularly important for researchers at the start of their career that they are aware of how successful researchers operate in this changing academic environment. Logicians work across the spectrum of faculties and departments. They are found in philosophy, linguistics, computer science, cognitive science and mathematics departments. A development particularly affecting logicians with positions in science faculties is the strong trend in those faculties to select and promote personnel on the basis of quantitative measures, chiefly the h-index based on Web of Knowledge. One of the consequences is that in many science faculties in the Netherlands and abroad, researchers are actively discouraged to submit their work to journals without Web of Knowledge impact factor, such as (in 2010) Studia Logica, Journal of Logic, Language and Information and Journal of Philosophical Logic. This makes some logicians turn to journals of neighbouring fields (such as artificial intelligence, cognitive science and computer science) that do have Web of Knowledge journals, even if their papers would be very interesting for a logic journal. Is this a desirable development? It seemed wise to step back and consider the background, the facts and the strategies. We quickly recall what the h-index is and purports. We then discuss answers to a questionnaire on publication strategies sent out to seven well-known logicians, trying to determine whether the issue ‘lives’ among the community, and whether the tricks of the trade are quantitative or not. Following is an overview of h-indices of some well-known logicians (other than the interviewees), and as further reference material the h-indices of the 2009 Vidi grant winners, a recognition in the Netherlands of successful early-career logicians. Finally, we come with several recommendations and suggestions.

In recent years, multi-agent systems have come to form one of the key technologies for software development.The Formal Approaches to Multiagent Systems (FAMAS) workshop series brings together researchers from the fields of logic, theoretical computer science and multi-agent systems in order to discuss formal techniques for specifying and verifying multiagent systems, including many subtle and not easy to formalize aspects of agency.FAMAS addresses the issues of logics for multiagent systems, formal methods for verification, e.g.model checking, and formal approaches to cooperation, multi-agent planning, communication, coordination, negotiation, games, and reasoning under uncertainty in a distributed environment.The first workshop in the FAMAS series, FAMAS'03, took place in Warsaw in April 2003 as a satellite event of the European Conference on Theory and Practice of Software (ETAPS'03).A selection of contributed and invited papers was published in Fundamenta Informaticae as volume 63, issue 2,3 of 2004.The second FAMAS workshop, FAMAS'06, took place in August 2006 at the Riva del Garda, in conjunction with the European Conference on Artificial Intelligence (ECAI'06).The best contributions resulted in the current special issue of the Journal of Autonomous Agents and Multiagent Systems.FAMAS'07 was one of the agent workshops gathered together under the umbrella of Multiagent Logics, Languages and Organizations-Federated Workshops (MALLOW'007), taking place in September 2007 in Durham.Finally, FAMAS'09 will take place in Turin as a part of MALLOW'09.

The Sum-and-Product riddle was first published in the reference H. Freudenthal (1969, Nieuw Archief voor Wiskunde 3, 152) [6]. We provide an overview on the history of the dissemination of this riddle through the academic and puzzle-math community. This includes some references to precursors of the riddle, that were previously (as far as we know) unknown. We then model the Sum-and-Product riddle in a modal logic called public announcement logic. This logic contains operators for knowledge, but also operators for the informational consequences of public announcements. The logic is interpreted on multi-agent Kripke models. The information in the riddle can be represented in the traditional way by number pairs, so that Sum knows their sum and Product their product, but also as an interpreted system, so that Sum and Product at least know their local state. We show that the different representations are isomorphic. We also provide characteristic formulas of the initial epistemic state of the riddle. We analyse one of the announcements towards the solution of the riddle as a so-called unsuccessful update: a formula that becomes false because it is announced. The riddle is then implemented and its solution verified in the epistemic model checker DEMO. This can be done, we think, surprisingly elegantly. The results are compared with other work in epistemic model checking and the complexity is experimentally investigated for several representations and parameter settings.

We provide a strongly complete infinitary proof system for hybrid logic. This proof system can be extended with countably many sequents. Thus, although these logics may be non-compact, strong completeness proofs are provided for infinitary hybrid versions of non-compact logics like ancestral logic and Segerberg's modal logic with the bounded chain condition. This extends the completeness result for hybrid logics by Gargov, Passy, and Tinchev.

Abstract We investigate the theory IΔ0+Ω1 and strengthen [Bu86, Theorem 8.6] to the following: if NP ≠ co-NP, then Σ-completeness for witness comparison foumulas is not provable in bounded arithmetic. i.e., Next we study a “small reflection principle” in bounded arithmetic. We prove that for all sentences φ The proof hinges on the use of definable cuts and partial satisfaction predicates akin to those introduced by Pudlák in [Pu86]. Finally, we give some applications of the small reflection principle, showing that the principle can sometimes be invoked in order to circumvent the use of provable Σ-completeness for witness comparison formulas.