Rational Agents: Prioritized Goals, Goal Dynamics, and Agent Programming Languages with Declarative Goals

I introduce a specification language for modeling an agent's prioritized goals and their dynamics. I use the situation calculus along with Reiter's solution to the frame problem and predicates for describing agents' knowledge as my base formalism. I further enhance this language by introducing a new sort of infinite paths. Within this language, I discuss how to systematically specify prioritized goals and how to precisely describe the effects of actions on these goals. These actions include adoption and dropping of goals and subgoals. In this framework, an agent's intentions are formally specified as the prioritized intersection of her goals. The ``prioritized'' qualifier above means that the specification must respect the priority ordering of goals when choosing between two incompatible goals. I ensure that the agent's intentions are always consistent with each other and with her knowledge. I investigate two variants with different commitment strategies. Agents specified using the ``optimizing'' agent framework always try to optimize their intentions, while those specified in the ``committed'' agent framework will stick to their intentions even if opportunities to commit to higher priority goals arise when these goals are incompatible with their current intentions. For these, I study properties of prioritized goals and goal change. I also give a definition of subgoals, and prove properties about the goal-subgoal relationship. As an application, I develop a model for a Simple Rational Agent Programming Language (SR-APL) with declarative goals. SR-APL is based on the ``committed agent'' variant of this rich theory, and combines elements from Belief-Desire-Intention (BDI) APLs and the situation calculus based ConGolog APL. Thus SR-APL supports prioritized goals and is grounded on a formal theory of goal change. It ensures that the agent's declarative goals and adopted plans are consistent with each other and with her knowledge. In doing this, I try to bridge the gap between agent theories and practical agent programming languages by providing a model and specification of an idealized BDI agent whose behavior is closer to what a rational agent does. I show that agents programmed in SR-APL satisfy some key rationality requirements

Goal Formation through Interaction in the Situation Calculus: A Formal Account Grounded in Behavioral Science

Goal reasoning has been attracting much attention in AI recently. Here, we consider how an agent changes its goals as a result of interaction with humans and peers. In particular, we draw upon a model developed in Behavioral Science, the Elementary Pragmatic Model (EPM). We show how the EPM principles can be incorporated into a sophisticated theory of goal change based on the Situation Calculus. The resulting logical theory supports agents with a wide variety of relational styles, including some that we may consider irrational or creative. This lays the foundations for building autonomous agents that interact with humans in a rich and realistic way, as required by advanced Human-AI collaboration applications

Cognitive modeling of social behaviors

To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual mind as ways of carrying out activities. This requires for the psychologist a shift from only modeling goals and tasks —why people do what they do—to modeling behavioral patterns—what people do—as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). To illustrate these ideas, this article presents an extract from a Brahms simulation of the Flashline Mars Arctic Research Station (FMARS), in which a crew of six people are living and working for a week, physically simulating a Mars surface mission. The example focuses on the simulation of a planning meeting, showing how physiological constraints (e.g., hunger, fatigue), facilities (e.g., the habitat’s layout) and group decision making interact. Methods are described for constructing such a model of practice, from video and first-hand observation, and how this modeling approach changes how one relates goals, knowledge, and cognitive architecture. The resulting simulation model is a powerful complement to task analysis and knowledge-based simulations of reasoning, with many practical applications for work system design, operations management, and training

Federal Workforce: Sustained Attention to Human Capital Leading Practices Can Help Improve Agency Performance

[Excerpt] A careful consideration of federal pay is an essential part of fiscal stewardship and is necessary to support the recruitment and retention of a talented, agile, and high-performing federal workforce. High-performing organizations have found that the life-cycle of human capital management activities—including workforce planning, recruitment, on-boarding, compensation, engagement, succession planning, and retirement programs—need to be aligned for the cost-effective achievement of an organization’s mission. However, despite some improvements, strategic human capital management—and more specifically, skills gaps in mission critical occupations—continues to be a GAO high-risk area. This testimony is based on a body of GAO work primarily issued between June 2012 and March 2017. It focuses on (1) lessons learned in creating a more market driven, results-oriented approach to federal pay, and (2) opportunities, in addition to pay and benefits, that OPM and agencies could use to be more competitive in the labor market and address skills gaps

Deterring Violent Non-State Actors in the New Millennium; Strategic Insights, V. 1, issue 10 (December 2002)

This article appeared in Strategic Insights, v.1, issue 10 (December 2002)Approved for public release; distribution is unlimited

Social Development, Asset Building, and Social Investment: The Historical and International Context

This article provides an historical background to the special issue by tracing the evolution of social development, asset building and social investment in different parts of the world. These approaches transcend remedial and service-oriented interventions and seek to promote progressive social change. They also stress the importance of investing in people and communities, and focusing on their strengths rather than deficits. The historical evolution of these three approaches in different countries and world regions is described, and their key features are highlighted. The article compares these approaches and considers some of their implications for social welfare, pointing out that they raise a number of issues that should be debated. Some of these issues and the challenges they pose to social welfare scholars are discussed