Social Mental Shaping: Modelling the Impact of Sociality on Autonomous Agents' Mental States

This paper presents a framework that captures how the social nature of agents that are situated in a multi-agent environment impacts upon their individual mental states. Roles and relationships provide an abstraction upon which we develop the notion of social mental shaping. This allows us to extend the standard Belief-Desire-Intention model to account for how common social phenomena (e.g. cooperation, collaborative problem-solving and negotiation) can be integrated into a unified theoretical perspective that reflects a fully explicated model of the autonomous agent's mental state

Goal generation with relevant and trusted beliefs

A rational agent adopts (or changes) its goals when new information (beliefs) becomes available or its desires (e.g., tasks it is supposed to carry out) change. In conventional approaches to goal generation in which a goal is considered as a \u201cparticular\u201d desire, a goal is adopted if and only if all conditions leading to its generation are satisfied. It is then supposed that all beliefs are equally relevant and their sources completely trusted. However, that is not a realistic setting. In fact, depending on the agent's trust in the source of a piece of information, an agent may decide how strongly it takes into consideration such piece of information in goal generation. On the other hand, not all beliefs are equally relevant to the adoption of a given goal, and a given belief may not be equally relevant to the adoption of different goals. We propose an approach which takes into account both the relevance of beliefs and the trust degree of the source from which the corresponding piece of information comes, in desire/goal generation. Two algorithms for updating the mental state of an agent in this new setting and three ways for comparing the resulting fuzzy set of desires have been given. Finally, two fundamental postulates any rational goal election function should obey have been stated

Grounding semantic cognition using computational modelling and network analysis

The overarching objective of this thesis is to further the field of grounded semantics using a range of computational and empirical studies. Over the past thirty years, there have been many algorithmic advances in the modelling of semantic cognition. A commonality across these cognitive models is a reliance on hand-engineering “toy-models”. Despite incorporating newer techniques (e.g. Long short-term memory), the model inputs remain unchanged. We argue that the inputs to these traditional semantic models have little resemblance with real human experiences. In this dissertation, we ground our neural network models by training them with real-world visual scenes using naturalistic photographs. Our approach is an alternative to both hand-coded features and embodied raw sensorimotor signals. We conceptually replicate the mutually reinforcing nature of hybrid (feature-based and grounded) representations using silhouettes of concrete concepts as model inputs. We next gradually develop a novel grounded cognitive semantic representation which we call scene2vec, starting with object co-occurrences and then adding emotions and language-based tags. Limitations of our scene-based representation are identified for more abstract concepts (e.g. freedom). We further present a large-scale human semantics study, which reveals small-world semantic network topologies are context-dependent and that scenes are the most dominant cognitive dimension. This finding leads us to conclude that there is no meaning without context. Lastly, scene2vec shows promising human-like context-sensitive stereotypes (e.g. gender role bias), and we explore how such stereotypes are reduced by targeted debiasing. In conclusion, this thesis provides support for a novel computational viewpoint on investigating meaning - scene-based grounded semantics. Future research scaling scene-based semantic models to human-levels through virtual grounding has the potential to unearth new insights into the human mind and concurrently lead to advancements in artificial general intelligence by enabling robots, embodied or otherwise, to acquire and represent meaning directly from the environment

Maintenance goals in intelligent agents

One popular software development strategy is that of intelligent agent systems. Agents are often programmed by goals; a programmer or user defines a set of goals for an agent, and then the agent is left to determine how best to complete the goals assigned to them. Popular types of goals are achievement and maintenance goals. An achievement goal describes some particular state the agent would like to bring about, for example, being in a particular location or having a particular bank balance. Given an achievement goal, an agent will perform actions that it believes will lead it to having the achievement goal realised. In current agent systems, maintenance goals tell an agent to ensure that some condition is always kept satisfied, for example, ensuring that a vehicle stays below a certain speed, or that it has sufficient fuel in its fuel tank. Currently, maintenance goals are reactive, in that they are not considered until after the maintenance condition has been violated. Only then does the agent begin to perform actions to restore the maintenance condition. In this thesis, we have discussed methods by which maintenance goals can be made proactive. Proactive maintenance goals may cause an agent to perform actions before a maintenance condition is violated, when it can predict that a maintenance condition will be violated in the future. This can be due to changes to the environment, or more interestingly, when the agent itself is performing actions that will cause the violation of the maintenance condition. Operational semantics that clearly demonstrate the functionality and operation of proactive maintenance goals have been developed in this thesis. We have experimentally shown that agents with proactive maintenance goals will reduce the amount of resources consumed in a variety of error-prone environments. This includes scenarios where the agent's beliefs are less than the true values, as well as when the beliefs are in excess of the true values

Constrained Rationality: Formal Value-Driven Enterprise Knowledge Management Modelling and Analysis Framework for Strategic Business, Technology and Public Policy Decision Making & Conflict Resolution

The complexity of the strategic decision making environments, in which busi- nesses and governments live in, makes such decisions more and more difficult to make. People and organizations with access to the best known decision support modelling and analysis tools and methods cannot seem to benefit from such re- sources. We argue that the reason behind the failure of most current decision and game theoretic methods is that these methods are made to deal with operational and tactical decisions, not strategic decisions. While operational and tactical decisions are clear and concise with limited scope and short-term implications, allowing them to be easily formalized and reasoned about, strategic decisions tend to be more gen- eral, ill-structured, complex, with broader scope and long-term implications. This research work starts with a review of the current dominant modelling and analysis approaches, their strengths and shortcomings, and a look at how pioneers in the field criticize these approaches as restrictive and unpractical. Then, the work goes on to propose a new paradigm shift in how strategic decisions and conflicts should be modelled and analyzed. Constrained Rationality is a formal qualitative framework, with a robust method- ological approach, to model and analyze ill-structured strategic single and multi- agent decision making situations and conflicts. The framework brings back the strategic decision making problem to its roots, from being an optimization/efficiency problem about evaluating predetermined alternatives to satisfy predetermined pref- erences or utility functions, as most current decision and game theoretic approaches treats it, to being an effectiveness problem of: 1) identifying and modelling explic- itly the strategic and conflicting goals of the involved agents (also called players and decision makers in our work), and the decision making context (the external and internal constraints including the agents priorities, emotions and attitudes); 2) finding, uncovering and/or creating the right set of alternatives to consider; and then 3) reasoning about the ability of each of these alternatives to satisfy the stated strategic goals the agents have, given their constraints. Instead of assuming that the agents’ alternatives and preferences are well-known, as most current decision and game theoretic approaches do, the Constrained Rationality framework start by capturing and modelling clearly the context of the strategic decision making situation, and then use this contextual knowledge to guide the process of finding the agents’ alternatives, analyzing them, and choosing the most effective one. The Constrained Rationality framework, at its heart, provides a novel set of modelling facilities to capture the contextual knowledge of the decision making sit- uations. These modelling facilities are based on the Viewpoint-based Value-Driven - Enterprise Knowledge Management (ViVD-EKM) conceptual modelling frame- work proposed by Al-Shawa (2006b), and include facilities: to capture and model the goals and constraints of the different involved agents, in the decision making situation, in complex graphs within viewpoint models; and to model the complex cause-effect interrelationships among theses goals and constraints. The framework provides a set of robust, extensible and formal Goal-to-Goal and Constraint-to Goal relationships, through which qualitative linguistic value labels about the goals’ op- erationalization, achievement and prevention propagate these relationships until they are finalized to reflect the state of the goals’ achievement at any single point of time during the situation. The framework provides also sufficient, but extensible, representation facilities to model the agents’ priorities, emotional valences and attitudes as value properties with qualitative linguistic value labels. All of these goals and constraints, and the value labels of their respective value properties (operationalization, achievement, prevention, importance, emotional valence, etc.) are used to evaluate the different alternatives (options, plans, products, product/design features, etc.) agents have, and generate cardinal and ordinal preferences for the agents over their respective alternatives. For analysts, and decision makers alike, these preferences can easily be verified, validates and traced back to how much each of these alternatives con- tribute to each agent’s strategic goals, given his constraints, priorities, emotions and attitudes. The Constrained Rationality framework offers a detailed process to model and analyze decision making situations, with special paths and steps to satisfy the spe- cific needs of: 1) single-agent decision making situations, or multi-agent situations in which agents act in an individualistic manner with no regard to others’ current or future options and decisions; 2) collaborative multi-agent decision making situ- ations, where agents disclose their goals and constraints, and choose from a set of shared alternatives one that best satisfy the collective goals of the group; and 3) adversarial competitive multi-agent decision making situations (called Games, in gamete theory literature, or Conflicts, in the broader management science litera- ture). The framework’s modelling and analysis process covers also three types of con- flicts/games: a) non-cooperative games, where agents can take unilateral moves among the game’s states; b) cooperative games, with no coalitions allowed, where agents still act individually (not as groups/coalitions) taking both unilateral moves and cooperative single-step moves when it benefit them; and c) cooperative games, with coalitions allowed, where the games include, in addition to individual agents, agents who are grouped in formal alliances/coalitions, giving themselves the ability to take multi-step group moves to advance their collective position in the game. ...

Dynamic Goal Hierarchies

. In this paper we introduce and formalise dynamic goal hierarchies. We begin with a formal definition of goals, according to which they are rational desires. In particular, we require that an agent's goals are coherent; that is, that the agent believes that each goal is jointly realisable with all of the goals which the agent considers to be more important. Thus an agent's goals form a hierarchy, and new goals are defined with reference to it. We then show how preferential entailment can be used to formalise the rational revision of goals and goal hierarchies. 1 Introduction Stan is writing a paper for a conference. On Monday he decides to work on the paper throughout the week and to finish it on Sunday. He also decides to take Saturday o# in order to go to the beach with his family. He considers that it is more important to finish the paper, but he believes that going to the beach for the day on Saturday will not prevent him from doing so. On Tuesday Stan works on the paper and his..